AI mimics human thought and perception at accelerated speeds. With its ever-evolving algorithms, natural language processing (NLP), and machine learning (ML), AI is getting better at filling the gaps between needs and resource availability. Because of its ability to quickly analyze and interpret vast stores of data, AI can be an invaluable tool for automation and process optimization.

McKinsey Global Survey reports that 65% of polled respondents said they use some form of generative AI in their operations, nearly double the results of last year.

Automation can drive growth, cut costs, boost production, and increase customer retention, but it must be employed strategically, with an eye towards meeting clearly-defined business objectives. AI tools can add value only when they are being used to enhance sound methodologies and viable processes.

What is artificial intelligence (AI) automation?

There are three tiers of artificial intelligence in business operations:

•  Assisted intelligence automates simple or repetitive tasks
•  Augmented intelligence learns from human inputs or behaviors over time
•  Automated intelligence offers fully machine-based decision-making

A business may choose to employ any or all of these types of AI in meeting its automation goals. Each should be used in support of improving departmental performance and meeting pre-established KPIs, not simply replacing human hands and minds.

Intelligent automation can look like a balanced trifecta of Business Process Management (BPM), Robotic Process Automation (RPA), and AI. BPM automates workflows and connects systems, while RPA relies on bots or virtual assistants to perform low-level repetitive tasks. AI extrapolates from available information and makes predictive choices accordingly. All three can work in tandem to successfully implement automation.

RPA bots are constrained by the examples and rules set by their human users, but AI can go beyond these limitations by learning over time and adjusting responses through successive iterations. Process mining for BPM is made much easier with AI’s analytical capabilities. 

Is AI the same as automation?

AI acts as a virtual facilitator for many business operations, including (but not limited to) automation. It can be inserted wherever speed, consistency, and precision are needed.

AI supports automation initiatives by combing through available data, analyzing changes over time, making recommendations, building low-code/no-code user apps, generating visual interfaces, and orchestrating workflows between departments.

If we can describe automation as a car that gets us to our destination faster, AI would be the onboard computer that offers improved navigation, accurate diagnostics, and advanced maintenance recommendations.

Benefits of automating business processes with AI

According to research by PwC, Global GDP may see a boost of up to 14% in the next six years due to the involvement of AI. By 2030, it’s believed that increased AI may contribute upwards of $15.7T in increased productivity and consumption side impacts to the global economy.

AI frees humans to meet more complex needs, such as engagement, strategy, and innovation. Its integration with automation is already bringing about profound transformations in several areas of business.

Intelligent automation is especially well-suited for improving customer service. Because of its 24-hour availability, AI can handle far more tickets than a human staff, greatly reducing backlogs and customer wait times. Chatbots can answer basic questions and deal with low-level issues, escalating requests to a human agent only when needed. AI-assisted language detection and translation tools expand the global reach of real-time support.

AI can be taught to emulate human conversation, making user experiences much more intuitive and enjoyable. Working with natural language prompts widens the scope of participation across departments, as employees don’t need coding experience to begin benefitting from new apps or platforms.

AI can also be trained to reclaim valuable legacy data. Intelligent document processing (IDP) culls information from an ocean of unstructured material, such as lists, ledgers, purchase orders, handwritten notes, invoices, and other forms. Intelligent image scanning opens up other types of potential information inputs.

Cost savings

AI serves to reduce operational costs in several important ways. Increased accuracy leads to better resource management, cutting duplicate work and unnecessary revisions. Faster turnaround times mean fewer labor hours, and streamlined workflows keep projects within budget. AI-assisted quality control slashes waste by minimizing errors.

Since AI works 24/7, it removes the need for worker breaks, holidays, and multiple shifts, extending business hours across time zones and geopolitical borders. Virtual workers can handle a much higher workload in far less time.

The use of AI also minimizes the need for data entry by human hands. Bots can collate and validate information, perform simple troubleshooting, and simultaneously handle multiple requests, allowing salaried professionals to focus on higher-level tasks during business hours.

Predictive maintenance becomes much easier with AI-generated reminders and automatically scheduled repairs.

Faster decision-making

AI supports human decisions with scalable simulations and improved trend forecasting. It can swiftly crunch and categorize tremendous amounts of data, summarize it, and use prescriptive analytics to make targeted recommendations. Such analysis can identify obstacles and pinpoint areas that need reworking, dropping the need for expensive outside consultancy. Companies can use AI-generated insights to map out areas where faster decisions are needed.

Agility is key to any company’s long-term success. Teams must respond swiftly to risks and make informed moves based on up-to-the minute data. Dynamically generated reports allow departments to reprioritize tasks, adjust resource allocation, and change process configurations.

Readiness to leave a decision to AI may depend on several factors, including time limits, budgetary constraints, and the complexity of the issues at hand. Many simple and routine decisions can easily be handed over to AI, but some scenarios will require more nuance, emotional intelligence, and awareness of interdependencies. Full decision automation may be fast and consistent, but leaving humans out of the loop altogether is not advisable. Humans are likely to have a more holistic understanding of how a decision may affect other parts of a system. 

The purpose of AI is to assist action and expand capacity. With the right training and continued improvements in machine learning, AI will adapt over time to more closely imitate human thought.

In Pipefy’s recent Business and IT Leader Survey, 48% of the executives polled anticipate that the application of generative AI to process and workflow automation will yield “better decision making.”

Better process efficiency and excellence

The first stage of optimization is process discovery, and AI can be an effective tool for parsing complex systems. Because AI is perfectly suited for rapid pattern discovery, it can be used to chart process frequency and complexity, and suggest possible interventions or enhancements. Turning AI towards the analysis of historical data can help identify areas where existing resources are being overextended.

Prior to investing in automation, a company first should determine which working processes are ready to be, or should be, sped up. Repetitive tasks, especially those that follow clear rules and require minimal human intervention, make especially good candidates for AI-assisted automation. 

Using AI to optimize processes can result in better time management for employees, more consistent outputs, and stronger performance overall. AI can make good processes more efficient, but only purposeful application will yield excellence.

Process standardization

Reliable and sustainable procedures make it easier to establish and assess KPIs. Standardization helps companies reduce waste, eliminate redundancy, remove bottlenecks, and remain in compliance. With AI-assisted standardization, employees spend less time reconciling irregularities or improvising remedies. AI can also aid in process documentation by building flowcharts, infographics, and other visual tools.

By using AI to map out departmental workflows, automation functions can be based on metrics rather than guesswork. AI can be used to assess large volumes of data and user behavior, then make informed recommendations about where to conform or converge processes.

Impromptu workarounds and idiosyncratic approaches to problem-solving often lead to data silos and impenetrable shadow IT. In contrast, using shared processes that are supported by AI will result in increased transparency, more consistent outcomes, and faster fixes.

Challenges of adopting AI automation and how to overcome them

For all of its power and promise, automation will not fix fundamentally broken processes. Any successful improvement depends upon a stable foundation and functional workflows already in place. AI can tag those processes which should be standardized before automation.

There may be some cultural resistance to change, especially in companies that have relied for too long on legacy systems and haphazard workarounds. Users may initially feel skeptical about AI or automation. The best remedy for such friction is active participation. Employees who feel empowered to utilize these new tools and to propel them forward in their daily work will reap the greatest benefits, and executives who are thoroughly briefed on the long-term objectives of automation are more likely to sustain their support.

Lack of an overall vision will lead to wasteful spending and stalled initiatives. When turning to AI as a component of automation, it’s important to invest in the right technologies, and to thoroughly understand their intended applications prior to purchase. Preliminary research and mapping should take a high view of end-to-end processes and data flow between departments, looking towards those areas which can be enhanced by speed, precision, and consistency.

Every initiative will have its attendant costs. Companies looking to adopt AI for use in automation should first investigate where automation can realistically drive savings, and measure initial expenses against long-term benefits. Companies must compare the costs of implementation, new tech advancements, and any necessary skill hiring against projected ROI.

No revolution is without risk. How vulnerable is your business as it stands? How will you build transparency into your automation initiatives? Do you have the right talent in place, or do you need to acquire it?

Not all jobs can be automated, and general purpose tech may not be appropriate for every process.

According to a 2024 MIT study (along with The Productivity Institute, and IBM’s Institute for Business Value), only 23% of worker tasks can be automated without incurring additional costs.

The shift to automation is a continuous effort, never a “one-and-done” solution. Results should be regularly validated and refined through rigorous testing, and any contributions made by AI must be given special scrutiny.

Any digital transformation depends upon thoughtful integration of new technology with existing resources. Look to AI as a support system for problem-solving, rather than a standalone solution in itself. 

Data privacy

Foundational models have been pre-trained on sets of data, and are generally self-supervising. Deep learning models can be further adapted for downstream tasks. All AI models are trained on enormous quantities of information, much of it derived from public data and web-scraping. Managing this dataset means carefully screening for bias, inaccuracy, or even toxicity. Some material may violate copyright, license restrictions, or intellectual property laws.

Disseminating sensitive information or collecting data beyond the declared scope can invite distrust among users and clients. Vigilant protection of customer transaction data may mean restricting its use in an AI environment.

Data generated by the Internet of Things is rapidly outpacing the information gathered from user experiences alone. AI can exploit this information for positive benefit, but companies have a responsibility to secure the privacy and safety of their customers.

Labor market impacts

AI has already seen explosive growth in several sectors, including healthcare, finance, automotive, logistics, retail, and manufacturing.

What role will people play in this shifting landscape? New personnel with current skills will be needed to implement and maintain AI technical support. In the coming years, we will see a skyrocketing demand for data scientists and engineers, while there will be a commensurate lessening of need for low-level data entry, call center staff, and other types of repetitive labor.

Businesses should already be conducting explorations of development in areas that can’t be replaced by AI, such as thought leadership, supervision, creativity, and ethics. Strategic hiring may become a matter of filling the spaces that exist between processes, below principles, and above workflows.

Employee acceptance and training

The hybrid workforce of the future will employ both AI and humans, working together to achieve business objectives. Employees can be encouraged to see AI training as empowering, skill-building, and an opportunity to foster a collaborative approach to problem-solving.

The use of low-code/no-code development encourages reskilling among the existing workforce, and AI is forming an increasingly consequential part of this shift. Writing a prompt is far easier than working with code, and the learning curve with AI tools is generally lower than it would be with many other types of tech adoption.

Bringing AI into automation allows a wider range of users to be involved in development, democratizing the process of workflow optimization and enabling cross-channel innovation.

AI automation examples

Automation follows rules and patterns established beforehand by human users, but its virtual workers can be further trained to form reactive solutions and predictive models of their own.

Manufacturing and freight are already seeing huge gains from the use of AI because their processes readily lend themselves to automation. Bots can be trained on supply chain data, learning how to monitor inventory spikes or predict parts starvation. Automated alerts can flag freight capacity issues and monitor weather delays or other disruptions, allowing companies to quickly locate alternative carriers.

Banks are increasingly turning to AI to assist customers, evaluate transaction data, and flag potentially fraudulent or unusual activity. AI aids in creating customized investment solutions and portfolio development, and it heavily shapes customer experiences in online transactions. In the volatile world of investments, AI’s ability to analyze historical data results in stronger predictive models and better threat detection.

The insurance industry has embraced AI automation, especially for claims processing, underwriting, regulatory compliance, risk management, and fraud prevention. It can be used to sort policyholder data, and keep up with customer preferences.

Beyond productivity gains, AI-assisted analysis has become an indispensable part of shaping customer expectations. AI has already revolutionized shopping, with more data touchpoints leading to better recommendation engines. It can chart customer demand, preferences, and purchasing habits. Online retailers are offering expanded personalized options with AI assists, and products can now be designed with customer tastes in mind. Because AI is designed to capitalize on large data sets, it can be used to segment customers by demographic and make sales projections based on past behaviors.

IT

AI automation helps IT by empowering users to build and manage their own workflows. By shifting the burden of problem solving and process optimization to other departments, IT teams are given more leeway to focus on larger issues, such as security, stability, and strategy.

AI’s use of cloud services speeds updates and machine imaging. It saves physical space in facilities by removing on-premises hardware. This is especially helpful when meeting storage-heavy needs, such as media archiving or content management. AI can help build the processes that distribute assets in the cloud, pulling IT employees out of server closets and into higher executive functions.

The performance of any AI tool is tied to the quantity and quality of available data. Before an AI program can be turned loose on banks of information, IT will need to study this material’s sources, evaluate its integrity, and determine how it will be utilized. Since AI platforms are adjustable based on dataset size, availability of computing power, and number of parameters, IT will likely be tasked with managing each of these variables.

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Digital transformation centers on bolstering best practices with the right technology. A CIO and their IT team must pre-determine how such a tool will be integrated into the existing tech stack, who will have access to it, and how existing resources would best benefit from AI assists.

Procurement

“Spend management” is the ongoing analysis and refinement of a company’s procurement strategy. Continuous audits serve to save money, reduce risk, and extract maximum value from a company’s relationships with its suppliers.

AI can help procurement teams by classifying requisitions, sorting invoices, managing or renewing contracts, and flagging discrepancies. It can predict spending trends, and offer quick pivots in response to volatile market prices, problematic locations, or poor vendor performance.

Sourcing is a complex part of procurement, often involving a lot of form-filling and approvals. AI helps manage sourcing by fulfilling repetitive tasks, collating vendor data, and basing suggestions on reliable metrics.

Over time, the focus of AI development may shift to industry-wide applications rather than targeting needs of individual firms. Since AI creates scalable services, allowing for rapid growth at a microeconomic level, such customizable approaches may benefit small businesses who don’t have large in-house resources.

HR

While it may sound counterintuitive, human resources is a business area that benefits tremendously from automation. Though AI tools are meant to augment human efforts rather than replace employees, automation can still save HR departments a great deal of time and labor.  Automation frees up staff by eliminating manual data entry and repetitive steps. 

HR departments around the globe are already using AI for validating and updating records. For hiring, AI can quickly scan resumes, schedule interviews, conduct preliminary screenings, and match suitable candidates with position requirements. Employee engagement and onboarding can be simplified and standardized. AI tools can assess performance and monitor fluctuations in attendance or productivity. Customizable templates and automated document classification help HR departments keep on top of their personnel needs.

How to start using AI automation at your company

Strategic planning is essential to the maturation of any enterprise. It’s important to conduct extensive research prior to pulling the trigger on a major tech overhaul. Trying to launch too many initiatives at once may inadvertently end up creating more data silos, so choosing one process to automate at a time helps keep efforts manageable and measurable. 

Process discovery (evaluating human behaviors) and process mining (evaluating data) will prove invaluable to any successful deployment. Map out the processes to be automated and seek employee feedback throughout. Study use cases, and outline the most desirable outcomes before weighing potential tech purchases.

Human oversight will become increasingly essential as we trust more of our lives to AI. Compliance is necessary in heavily regulated industries, and good governance reduces reputational risk. Keeping humans in the loop helps align intelligent automation with business objectives.

Monitor how AI is working for your company. How does it perform over time? How is its behavior changing? Look to audit trails, routine testing, and clearly defined performance metrics to help in tracking KPIs.

Here are some key steps you can take to bring the power of AI into your automation initiatives:

• Define goals ahead of time by consulting your stakeholders
•  Establish use cases and identify potential value of historical data
•  Determine the priorities for automation
•  Weigh any potential security or safety risks
• Select the right technology for the intended purpose
•  Hire the right team members
• Prepare for training and upskilling
• Look at the big data picture, including capture methods and repositories
• Try out a pilot program, and scale it over time
• Continuously monitor the results, and test for validity
• Establish firm governance policies

Leveraging AI automation with Pipefy

Pipefy is a no-code automation platform with AI capabilities that help businesses streamline operations across business lines to enhance productivity, ensure accuracy, and conserve IT resources.

Pipefy AI’s powerful ChatGPT-based technology gives business leaders a complete tool kit for making data-driven decisions at any given time by providing real-time analytics and easy access to insights about their processes – faster than ever. They simply tell the chatbot prompt what they need and, within seconds, receive a ready-made, fully customizable workflow! 

Start making better decisions and identifying inefficiencies today with one AI-powered tool!

The post AI Automation: What It Is And How To Use It appeared first on Pipefy.

Few businesses make full use of AI technology, however. AI-powered tools can take organizations to new heights of productivity by optimizing their processes, which is why many businesses are using them to increase their bottom line. This article explores the optimization process and its benefits across multiple industries.

What is AI workflow optimization?

Workflow optimization is a systematic approach to analyzing and improving business processes. These improvements take the form of increased efficiency, effectiveness, and/or quality, all of which can improve an organization’s bottom line. AI process optimization employs artificial intelligence (AI) and machine learning (ML) to achieve these goals faster and with fewer errors. It generally focuses on the identification and removal of unnecessary tasks, resulting in streamlined workflows.

In 2023, a study found that the AI market was valued at 196.63B, largely due to the technology’s ability to analyze historical data very quickly. This long-term data visibility gives business leaders the ability to find market patterns and trends; they can then make data-based decisions to drive their companies ahead of competition. 

AI process optimization can also automate many mundane tasks, freeing up time for users to perform nuanced, high-value, and high-priority work. Furthermore, it can analyze each stage of a production process in real time to identify bottlenecks and other process inefficiencies.

How AI optimizes processes

AI process optimization employs a variety of technologies, including ML models, computer vision, and natural language processing (NLP). ML statistical algorithms recognize patterns in processes, which it uses to improve them. Computer vision technology obtains information from images and videos, while NLP uses computational techniques to analyze and synthesize human languages. Together, they power AI process automation to perform the following tasks:

• Forecasting results
• Identifying bottlenecks
• Automating daily repetitive tasks
• Streamlining end-to-end processes
• Speeding up decision-making
• Redesigning inefficient processes

AI process optimization predicts customer behavior by analyzing data sources such as past behavior and market trends. It can then determine the customer’s most likely future behavior, based on their most pressing needs.

Forecast results

AI can also anticipate events before they occur through the use of predictive analytics. This capability allows the user to build models of these events, typically for the purpose of planning for worst-case scenarios. Assume, for example, that a transportation company wants to understand the effects of a natural disaster so they can determine the best routes for supply delivery and the best staff to deploy to perform the work.

This knowledge will directly affect delivery efficiency, especially during unusual events; careful planning is essential to ensure team members execute plans quickly and effectively. In addition, proper preparation would give the transportation business the ability to quickly reroute drivers in the event the primary route is unusable, while minimizing disruptions to deliveries.

Identify bottlenecks

Identifying process bottlenecks requires diligence and close attention to detail. Visual aids such as fish bone diagrams, flowcharts and time trackers are the traditional tools for this task, as they allow you to monitor business processes at each step. In addition, tracing bottlenecks requires a careful examination of both individual and departmental-level processes, depending on the scope of the project.

AI optimization software can greatly reduce the time users need to map out activities and tasks, in addition to cutting down the time needed to complete them. It can also identify processes with long queuing times – a clear indication of a bottleneck. A significant amount of idle time before the process begins is an even bigger red flag that some part of it is bottlenecked.

Automate daily repetitive tasks

Automating routine tasks at high volume is one of the most common uses of AI process optimization. AI is the most effective component for automating tasks, although other technologies can help develop complex automation strategies. In addition to saving time, automation reduces the rate of manual errors and allows workers to focus on high-level tasks.

Document processing is a common application for automation, as businesses also receive many documents like contracts and invoices in both paper and electronic format. After receiving these documents, employees have historically processed them by manually entering this information into an application. AI solutions like intelligent document processing (IDP) can convert them into usable data without the need for manual intervention.

Streamline end-to-end processes

While AI is a useful tool for streamlining individual processes, automating an entire workflow requires additional tools. Many automation technologies can augment AI, such as robotic process automation (RPA). This technology is well suited for filling in forms and other types of data entry once the data has been extracted from a document.

For example, an AI model can analyze purchase data from a supply chain in real-time to identify anomalies like a run on a particular product. The model could then alert a retail store about this issue, requiring a manager to decide if any action is required. If it is, the manager can enter the product number into an application, where upon an RPA bot connects with a supply system to order more of the product. This use of AI process optimization can save a great deal of time, especially during periods of volatile demand.

Speed up decision-making

Leaders in today’s business landscape require strong evidence to ensure they make the correct choices, and AI can assist with this process by analyzing patterns in a given data set. For example, business analysts often use predictive models to simulate the effects of price changes on profitability without the risks of implementing them in the real world. Companies can determine optimal prices without losing customers or profits.

The role AI process optimization plays in lending is another facet of its use in decision-making. Loan officers can analyze data to ensure they only grant loans to borrowers who follow sound financial practices. This strategy not only helps loan officers make better decisions, but it also reduces the time they need to make those decisions.

Redesign inefficient processes

AI process optimization can detect process inefficiencies and alert users to redundant steps. It can also identify the stages in a process that are responsible for the most delays, indicating where improvement efforts will have the most positive impact.

Benefits of optimizing processes with AI

The major benefits of AI process optimization include improved operational efficiency, fewer errors, cost savings and process standardization.

Improved Operational Efficiency

An increase in operational efficiency is the most significant benefit of AI process optimization. Businesses often use inefficient processes, typically manual processes that can be easily automated. This practice wastes time, increases employees’ workloads, and reduces profits.

AI can identify inefficiencies and redundancies in processes, reducing the time organizations spend on repetitive tasks that require a low level of skill to complete. The streamlining or complete automation of these tasks allows team members to spend more time on high-level tasks that will help grow the company.

Fewer Errors

AI effectively eliminates direct human error from processes, leading to improved outcomes. However, this benefit requires the AI solution to have access to accurate, up-to-date data. Automated data input and delivery can help improve the accuracy of input data for AI systems.

In addition to avoiding costly errors, AI process optimization helps organizations comply with government regulations and industry standards. It can also improve the performance of individual team members by relieving them from the stress of performing repetitive tasks. 

Furthermore, process optimization ensures that team members always have access to the data they need to maintain a business’s success. Finally, process optimization solutions typically use a permissions system that ensures sensitive information is only accessible by personnel who need it.

Cost Savings

AI process optimization provides a fast, powerful re-assessment and organization of resources and priorities. This has a tendency to reveal persistent, long-hidden ares of waste like data errors and bottlenecks, which compromise productivity and can increase operating expenses to a surprising degree.

Process Standardization

One of the most appealing attributes of AI technology is its ability to continuously perform the same task in exactly the same way without the need for human workers. AI-based solutions therefore provide far more consistent results, and are an ideal solution for disjointed processes that need to be standardized.

AI process optimization examples

Industry leaders often pave the way for other businesses by demonstrating the possibilities of emerging technology. What follows are a few examples some of the best-known global organizations deploying AI to optimize IT, procurement, and HR business processes.

IT

Microsoft was primarily interested in improving its security and efficiency when it added AI to its IT infrastructure, including both its cloud presence and on-premise data center. AI simplified the Microsoft network by automating access controls and creating a robust, risk-based isolation of suspicious devices. In addition, Microsoft used AI to monitor and manage third-party applications, including user access.

The same technology now detects network issues and repairs them, resulting in a resilient network that doesn’t require direct human intervention. These changes increased Microsoft’s operational efficiency by employing automation to manage resources, accelerate the deployment of software installations and updates, and manage network configurations.

Procurement

Shell wanted to use AI as a means to meet its procurement needs in the volatile energy market. It initially deployed AI technology for training equipment in its exploration and drilling program, significantly cutting the costs of extraction. This application allows machinery to learn from past experience, giving operators a better understanding of the current environment. It also leads to better results during exploration and causes less wear and damage to equipment.

In addition, Shell has put AI to work improving the safety of its fuel service stations. Vision-enabled cameras are installed at every service station to detect safety hazards such as customers lighting cigarettes near fuel pumps.

HR

Because of their current ubiquity, chatbots like ChatGPT are often the first thing most people think of when they hear the term “AI-based application.” When used in human resources (HR), this type of software maintains a large amount of employee-related data. 

RPG Group trained Leena AI on its internal HR data, allowing it to answer many employee questions in real time. The company then used this solution to create virtual assistants for employees that provided fast, accurate information about benefits, time off, and pay without the need for a human agent.

This system answered 92 percent of employees’ questions within minutes. It also reduced ticket resolution time from a full day to only four hours, saving the company thousands of hours in employee time per year.

Start optimizing your processes with Pipefy AI

AI process optimization is critical for businesses looking to unlock their full potential by reducing errors, minimizing risk, and maximizing productivity. Pipefy has extensive experience helping clients improve their processes with AI. Our no-code business process management (BPM) solution creates and optimizes processes by using historical data to help clients make smarter decisions.

The post Can AI Process Optimization Improve Your Business Processes? appeared first on Pipefy.

This article will explore all things workflow automation, including its strategic benefits, limitations, types and examples, future trends, and how companies connect their workflows for end-to-end integration.

What is workflow integration?

Workflow integration is the effective connection of different business systems and processes, enabling seamless data sharing between various platforms.

The intensive technology usage in business forces teams to use a myriad of different systems and applications daily. In this scenario, data exchange between these platforms becomes unavoidable. When this transference between SaaS tools and workflows is mostly manual, it tends to produce data silos and poor cross-team collaboration. 

The common consequences are excessive errors and rework, decreasing process lead time, productivity, and employee engagement.

Why do businesses need workflow integration

Workflow integration is critical to ensure collaboration, improve customer experience, and enable data-driven decision-making, besides reducing process errors. By leveraging integrated workflows, businesses can streamline operations, optimize their tech stack, and reduce expenses. Here are some benefits of workflow automation in detail:

• Process KPIs: Integrating workflows streamlines processes by automating repetitive tasks and eliminating manual data entry. This translates into time savings and allows employees to focus on high-priority activities. According to a Metrigy Study, 67% of users who embraced CPaaS had a revenue increase of up to 28.5%.
• Reduced silos: Data transfer through disparate systems can lead to data silos when certain employees can access information that their colleagues can’t. These silos allow cross-team misalignment, extensive manual data entry, human errors, and the need for constant rework.
• Improved collaboration: Integrating workflows facilitates seamless collaboration among teams and departments. Shared access to real-time data and streamlined communication channels enable employees to work together more effectively, enhancing overall communication and productivity.
• Enhanced customer experience: Integrated workflows enable businesses to deliver a more personalized customer experience. By consolidating customer data from various touchpoints, businesses can gain more knowledge of their behavior and offer more customized products or services.
• Improved employee experience: Eliminating manual data entry reduces employee frustration and a significant amount of their time is released to other activities, usually more relevant to the organization.
• Data-driven decision making: Connected workflows and systems improve data insights on business trends. This data-driven approach facilitates decisions that drive business growth and profitability.

Strategic advantages of workflow integration

Strategic workflow integration empowers businesses to achieve greater agility, scalability, innovation, cost efficiency, and competitive advantage. By orchestrating processes, systems, and teams, businesses can craft new growth opportunities.

Business agility

Integration plays a pivotal role in optimizing business processes and the flows between them, facilitating quicker operations, reducing human errors, and minimizing manual data entry.

This translates into tangible cost savings across different lines of business. For instance, when the payroll team automates data transfers across different business applications, from workforce management apps to payment tools, the business can save time; when all information is compiled from one app, it can be directly placed into another. 

Additionally, it mitigates possible rework, like receiving complaints from employees about payment errors. 

Cost-efficiency

Integrated systems and applications prevent tech stack sprawl – process fragmentation makes it harder to achieve the best use of business tools. In such scenarios, they are often hired separately for each department according to its own needs, increasing the chance that these tools needlessly perform overlapping actions. 

By integrating workflows, managers can easily spot systems that can be eliminated from the company’s budget, optimizing technology costs.

Increased revenue and retention 

Workflow fragmentation usually leads to bad customer service, constant errors, and lack of agility. This can jeopardize the customer experience and consequently harm NPS and retention. A well-oiled, integrated business operation mitigates errors and consistently  improves the quality of process execution, which can be a game-changer for optimizing word of mouth and increasing customer retention. 

Limitations in workflow integration

Although workflow integration is becoming mandatory for keeping businesses competitive, there are a few challenges and limitations leaders struggle with when they decide to connect processes and tools. 

The first one is resistance to technology. The least tech-savvy teams in the company may think, at first, that digitizing workflows and connecting different systems and processes is an unworthy effort, that will make them lose time and feel overwhelmed. 

To reduce this risk and orchestrate the team’s operation, it’s important to choose user-friendly workflow software. Some platforms, even in the low-code environment, are too complex for end-users and depend too much on IT development, making the learning curve too long.

Another possible limitation to keep in mind is the workflow management system’s integration capabilities. Some connect to few tools, making manual data entry a continued necessity. Before closing any deal, look at all software features and possible integrations, and investigate whether or not the support team can build customized integrations for your business for a reasonable price. 

Types of workflow integrations

There are a few different approaches to integrating business workflows. The most common include:

Native integrations. Platforms provide these out-of-the-box integrations with apps and systems. Native integrations don’t require APIs and external connectors. They are typically included with the platform subscription, or can be added for a low cost. Native integrations are commonly limited to only a few apps, so you will likely require other integrations.

Point-to-point integrations are connections between two apps built by your own IT team. In this case, your in-house developers are responsible for addressing issues with those connections; therefore, this type of integration can be time and resource-consuming to maintain. 

iPaaS integrations are third-party cloud-based platforms dedicated to creating and keeping integrations. Such tools are reliable, and compliant with world-class privacy and regulation standards. They centralize integrations and offer pre-built connectors that support connection efforts. The support is provided by the software vendor.

How to integrate workflows

Here’s an actionable step-by-step to connect your workflows and streamline business processes:

List inefficient and disparate workflows

First, identify the workflows that just don’t work as intended and need urgent attention; in these cases, check for potential connections. Begin by mapping out processes that are fragmented or blocked by the lack of integration.

For a workflow to be considered suitable for integration, it must involve the use of various software tools, each one covering a different part of the workflow.

To design an action plan, invite multiple teams to contribute to the discussion and describe the bottlenecks they’ve experienced related to inefficiencies, suboptimal structural setups, or process fragmentation. 

Identify relevant tools and stakeholders

After identifying the best candidates for integration among all workflows, the next step is mapping out all employees and tools who will be involved in each process. 

For example, in a recruitment process, the individuals involved include recruiters and talent managers, but don’t forget those people in the business areas who requested the job opening and actually select candidates. 

In a sales process, the representatives will likely pass the torch to an account manager or customer success manager to onboard that customer. 

Then, perform a review of each stage of the workflow to detail the tools and the team members involved at every step.

Choose the best solution 

After mapping out the necessary workflows and resources, choose the best connectors. You can pick one of the approaches we described above, or combine the three of them according to your needs. 

If you work with time-sensitive deadlines and need to be as cost-effective as possible, a no-code workflow automation solution like Pipefy can be the answer. Pipefy connects with more than 300 apps and tools and also offers custom integrations – in case your business needs to connect no-code workflows with an ERP. 

By using a no-code solution, you can shorten the learning curve, build digital workflows faster, and connect them in a drag-and-drop interface – a quick and safe solution to process fragmentation.

Automate the workflows

When connecting two or more workflows, you’ll probably come across steps or actions that overlap.Those overlaps are a great place to start when looking for tasks to eliminate with workflow automation. Repetitive and manual actions are the best candidates for automation; look for tasks like data transfers between two workflows, task assignment, triage, follow-up emails, due date notifications, and calculations.

Measure results and make adjustments

Once workflows are automated and connected, the next step is to establish business goals and monitor the results. If you use a no-code BPM tool to build your workflow, you will most likely have quick access to customizable reports and dashboards for real-time results. Establish periodic checkpoints and, should you spot any improvement opportunities or bottlenecks, make the necessary adjustments. 

Examples of integrated workflows

The following examples illustrate the powerful impact of workflow integration across different business areas: 

ITSM and Onboarding

Integrating IT Service Management (ITSM) with employee onboarding processes can significantly enhance the new hire experience while ensuring efficiency in both IT operations. 

This integration facilitates the automatic setup of necessary IT resources, such as user accounts, workstations, and access permissions, when a new employee is onboarded. IT staff receive notifications and can track requests through a single flow, ensuring no task falls through the cracks. As a result, new employees are ready to start working sooner, and IT can reduce downtime and improve client satisfaction.

Procurement and Accounts Payable

Linking procurement processes with accounts payable systems can optimize the flow from purchase order creation to payment processing. This integration ensures that, once a purchase order is approved and goods are received, the invoice processing begins automatically. 

It also reduces the manual effort required to match invoices with purchase orders and delivery receipts, minimizes errors, and accelerates payment cycle time. When businesses automate and connect these workflows, they can improve vendor relationships through timely payments and gain better visibility into cash flow and financial trends.

Sales and customer onboarding

Integrating sales workflows with customer onboarding processes establishes a trigger once a deal is closed to automatically begin the onboarding process, ensuring that all relevant customer information is transferred from the sales team to the onboarding team without delays or errors. 

This seamless transition not only enhances the customer experience by providing a smooth start, but it allows sales personnel to focus on selling rather than administrative tasks. It also reveals valuable insights into customer behaviors and preferences, enabling personalized and effective follow-up services and marketing strategies.

Predictive insights and future trends in workflow integration

AI

Artificial intelligence is now ubiquitous; it is included in most workflow automation/integration software capabilities. 

Some IpaaS vendors offer entire platforms that build generative AI-powered bots and connect them with their ecosystems. Others simply provide a way to connect existing solutions to pre-built systems.

According to a Pipefy survey of U.S. enterprise business leaders, 54% of the people interviewed anticipate better decision-making and the flexibility that allows it to be the primary benefit of applying generative AI to process and workflow automation.

No-code/low-code connectors

Workflow automation and integration tools have always held significant potential for companies that want to align employees, processes, and technology. Early solutions and API connectors weren’t always easy to leverage, however. While many vendors attempted to smooth digital transformations with simple packages of business tools, many solutions still required developer knowledge.

Fortunately, one of the major market trends in recent years is the growth of intuitive, streamlined solutions. Companies are creating no-code and low-code platforms that ensure anyone can take advantage of workflow integrations. 

In this sense, the forecasts for the IpaaS – Integration Platform as a Service, tools that usually don’t require any coding to build the bridge between two systems or apps – are impressive. The average growth expected from 2021 to 2026 is 30%, with these tools reaching a market size of US$ 13.9B by the end of 2026.

CpaaS

CPaaS (Communication Platform as a Service) tools have been evolving with the rise of new strategies that many businesses call “Network as a Service.” These tools connects workflows with multiple channels, improving communication with customers and reducing time spent on communication and manual data transfer between various messaging platforms. 

IDC forecasts the CPaaS market to grow from US$14.3B in 2022 to US$29.7B in 2026 (an annual compound growth rate of 15.8%), “as many enterprises embrace cloud-enabled communication API solutions and services that help them easily and affordably increase customer engagement and improve operational efficiency.”

Enhance workflow integration 2x faster 

Pipefy helps companies seamlessly orchestrate their workflows and processes from a single platform, avoiding the overlaps, manual data entry, and excessive spending that occur with untamed and disconnected tech stacks. With 300+ native connectors and API integrations, Pipefy mitigates data silos and increases overall visibility.

Because of its ease of use, Pipefy shrinks the learning curve, allowing an implementation that is 2x faster than other low/no-code automation solutions. Don’t waste time searching for unsafe methods to connect your workflows: Pipefy is SOC2 and ISO27001 certified, and features include 256-bit encryption, SSO, 2FA, and permission management.

The post Workflow Integration: Definition, Effective Strategies and Business Examples appeared first on Pipefy.

Such a system learns from accumulated data, thus increasing its efficiency over time. IA applications include pattern analysis, data assembly, and classification, which have use cases in a variety of industries. 

This guide distinguishes IA from related technologies, discusses its benefits, and summarizes the steps for implementing IA.

What is intelligent automation (IA)?

IA, also known as intelligent process automation (IPA), combines technologies like artificial intelligence (AI) and robotic process automation (RPA). A clear knowledge of IA requires you to understand the key differences between IA and its components, which are closely related concepts.

Differences between IA and RPA

RPA is a traditional technology in which users create a recording of a task which they convert to instructions for software. For example, they might open a folder, then a file, copy data into the file, and close the file. RPA software could then repeat the same process at scale.

IA is an advancement of RPA whose technology automates more complex tasks with minimal human intervention. 

Unlike pure RPA, IA can also learn how to improve its performance over time. For example, IA can extract data from scanned invoices and gradually learn which invoices an organization uses most often, allowing IA to get faster at extracting the required data.

Differences between IA and AI

The main differences between AI and IA lie with their focuses and capabilities. AI systems attempt to mimic human intelligence to perform tasks autonomously. On the other hand, IA systems combine AI and other automation technologies to enhance human performance by improving efficiency and facilitating decision-making. 

To illustrate this difference, an AI tool like ChatGPT can analyze a retailer’s datasets to predict customers’ buying behaviors, while IA could automate its lead qualification processes to let the sales team prioritize their efforts more effectively.

AI replicates human intelligence, while IA augments human capabilities. AI performs autonomous tasks without human intervention, while IA automates routine tasks and assists humans in making decisions. 

The objective of AI is to mimic human cognition and decision-making, while IA streamlines processes and enhances their efficiency. Examples of AI include natural language processing (NLP) and predictive analytics, while examples of IA include RPA and ead-generation chatbots.

How does intelligent automation work?

IA uses a variety of software that collaborates and synchronizes to optimize workflows. These components include:

• AI
• RPA
• Machine learning (ML)
• NLP
• Computer vision

AI

AI is a field of computer science that focuses on solving problems that have traditionally required human cognition. It’s a broad field, so it encompasses other technologies like deep learning and ML. 

Traditional automation tools can perform predefined tasks, but AI allows automated systems to learn from sources like historical data and user interactions. AI uses existing data to perform tasks more effectively over time. 

In particular, it can recognize patterns and use that analysis to solve new problems, which is especially useful in dynamic environments that change over time.

RPA

RPA is the use of software robots, commonly known as “bots,” to complete repetitive tasks based on a set of rules. They employ existing user interfaces (UIs), eliminating the need for new software integrations. 

Users can also train bots to operate software by generating the same results through the same UIs, although not necessarily using the same keystrokes. Some processes require multiple bots to complete, but they can scale to meet changes in demand once they’re trained to perform a task.

ML

ML is the process of using statistical algorithms and models to perform tasks without explicit instructions. These algorithms rely on large volumes of historical data to train an ML system on the required inputs and outputs by using patterns, historical data and inferences. 

This capability allows the system to predict outcomes and act on them. IA can thus use ML to develop more precise and efficient workflows without human intervention.

NLP

NLP is a technology that computers use to interpret and understand human language. Users can train these systems by inputting data like emails, social media posts and texts into NLP software. 

The software will then process this data, analyze it for trends and respond to human communication in real time. NLP thus improves the automated analysis of large text-based documents, resulting in greater comprehension and customer engagement.

Computer vision

Computer vision is a type of software designed to achieve human levels of accuracy when identifying people, places, and things in images. It helps automate processes like image extraction, identification, and classification. 

Computer vision users can rapidly upload an image library during these processes, providing a continual stream of new images and objects for an automated system. Computer vision is often part of IA systems in fields like autonomous driving, manufacturing, medical imaging, and process control.

Benefits of intelligent automation

IA platforms provide many benefits across industries due to their ability to use large data volumes and perform precise calculations, typically when conducting analyses and implementing business processes. These benefits include the following:

• Reduced costs
• Improved accuracy
• Increased scalability
• Faster decision-making
• Better customer experiences

IA supplements the workforce by analyzing data, improving productivity, and reducing costs. It also allows companies to quickly scale their operations without increasing risk, compromising quality, or increasing the load on existing workforces. Business leaders also benefit by increasing yields and improving the return on investment (ROI) of software.

Improved accuracy

IA improves accuracy by increasing the consistency and quality of processes. The use of AI to drive decision-making and automate repetitive tasks also increases accuracy.

Better customer experiences

IA improves customer experience by multiple means, including higher quality, faster times to market, and providing faster answers to questions. These advantages all contribute to better customer experiences, which is increasingly important for remaining competitive in a modern business environment.

Faster decision-making

IA uses ML algorithms that perform advanced analytics on large datasets to generate actionable insights. These results provide valuable information for making better decisions for strategic planning and identifying areas for improvement.

Increased scalability

IA solutions increase an organization’s ability to meet increases in business needs. This scalability ensures that companies can efficiently handle larger workloads without significantly increasing manual intervention or resource allocation.

Intelligent automation use cases

IA streamlines processes in many industries that are based on legacy systems requiring manual tasks. These processes can be prone to human error, costly, and resource-intensive. Industries that derive great benefits from IA include the following:

• IT
• HR
• Procurement

IT

IA solutions in IT can reduce the workload of service desk agents by automating many processes. This use case allows agents to focus on more complex cases requiring human creativity to solve.

User management is a common example of this IA, as it requires users to create accounts with appropriate permissions when onboarding new employees. They must also add and update user access details in various applications like Microsoft and Oracle products. This process is time-consuming when agents perform them manually, as they often involve entering the same data multiple times.

IA streamlines the account creation process by automatically retrieving account creation requests, creating the account, generating usernames, and passwords, and then notifying users that their account is ready for use. Automating this process reduces the time needed to create the account.

Procurement

An IA platform can streamline procurement processes by automating tasks such as searching for suppliers and vendors, onboarding them, and creating supplier scorecards, among many others. 

Its AI capability also allows such platforms to automate tasks like contract management, procure-to-pay, supplier relationship management, supply chain management, risk evaluation, and risk hedging. Businesses with IA can also automate inventory management for both raw materials and finished products, including tracking and auditing.

For example, IA can automate contract management by classifying contract formats and extracting fields from them via intelligent document processing. It can also track contract usage over time, allowing an enterprise to collect discounts from mechanisms like rebates, tiered pricing, and remuneration penalties specified in service level agreements (SLAs). In addition, IA solutions can proactively adjust invoice payments to account for reduced payments.

HR

HR management often involves many manual, repetitive processes. IA can provide significant benefits in terms of revenue increases and cost reductions. According to research by Workday and Personio, 93% of HR managers report using AI tools to reduce costs.

Companies can achieve many of these gains in recruitment by using IA to screen resumes, then create a short list of qualified candidates. This process is typically time-consuming but NLP bots can gather resumes, review them, and compare applicant data against relevant job requirements. 

This process reduces the time needed to eliminate unqualified applicants and notify all candidates of their results. AI also minimizes the frequency of false negatives and positives, since it doesn’t rely on predefined keywords and other rules.

How to get started with IA in your business

IA can be a key differentiator, providing organizations with competitive advantages, but it first requires them to identify business areas in which machines can outperform humans. Companies must adopt a robust strategy for IA that uses multiple technologies working together. 

The following is a set of basic steps for successfully implementing IA.

1. Set a goal

Get a clear understanding of the IA solutions overall strategy, including its objectives. This step typically focuses on identifying the functions and tasks requiring automation. In addition, it should determine the expected outcomes and benefits for the business.

2. Conduct process discovery

Process discovery identifies the areas with the greatest opportunities for improvement through automation. This analysis also includes an evaluation of the organization’s current technological infrastructure for the purposes of anticipating potential challenges to implementing IA.

3. Develop an implementation plan

Small businesses often implement their entire IA plan at once, but enterprises may prefer a test-and-learn approach. This plan should include milestones, targets, and timelines. 

The implementation plan should also emphasize the training employees and the resources they’ll require to ensure successful implementation and maintenance.

4. Build a minimum viable product (MVP)

A successful IA implementation may require significant effort and resources. Most organizations should therefore develop an MVP to ensure that the IA will provide the expected results before rolling out a complete implementation.

5. Test and validate the solution

Companies should test all data inputs and outputs before deploying an IA solution to a live environment to avoid disruptions to business. In addition, they should ensure their solution can handle the expected transaction volume.

6. Deploy the solution and measure its performance

Deploying an IA solution typically includes employee training and continual monitoring of its performance. The organization can then address any problems that prevent the solution from delivering its expected outcome.

7. Measure the results and optimize the solution

Measuring the results of an IA solution allows the company to optimize it. This process should include regular reviews and evaluations to determine if initial goals were achieved, with a focus on identifying areas of improvement an

The organization should continually refine its IA solution to ensure it remains aligned with its overall business strategy.

Automate your business processes with Pipefy

Business leaders routinely attempt to accelerate their companies’ digital transformations as a result of the increasing unpredictability of their operating environment. IA is revolutionizing this process due to its ability to maximize an organization’s potential, especially when it comes to creating seamless customer experiences.

Pipefy provides a dedicated platform that creates optimized business process models for your business. Key features include alerts and user tagging, which facilitate transitions between process steps. Pipefy’s AI features also analyze historical data, making it easier to make reliable and strategic decisions faster. 

The post Everything You Need to Know About Intelligent Automation  appeared first on Pipefy.

Fluctuating markets and talent shortages are compelling IT leaders across industries worldwide to seek new ways to revamp and improve spending. Resources must be allocated not only to approved business needs but for the support of organizational goals, as well. 

To say this is no small feat is an understatement; it involves businesses changing decades of spending patterns to embrace a new philosophy: cost optimization. When applied to IT, cost optimization is the continual business strategy of reducing operational costs and maximizing business value.

Cost reduction vs. value creation in IT

Modern IT leaders know the most effective cost optimization strategies do not always involve cutting costs. Drastic business line cost-reduction measures like hiring freezes, layoffs, and spending freezes don’t yield the long-term results IT teams must continually provide, even through uncertain economic times. 

A January 2024 Gartner report predicted a 6.8% bump in IT spending for the year. Organizations are directing those resources toward strategic value creation — specifically, the technology to bring that value to fruition.

Optimizing costs via continual improvement requires businesses to find the right balance of collaboration, transparency, and streamlining for efficiency. 

We’ve compiled a list of the 10 best strategies for IT teams seeking cost optimization, including suggestions for the technological means to initiate them.

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10 key techniques for IT cost management and optimization

1. Shared services model: collaborative efficiency across departments

A shared services model is one by which all business departments share resources. The method’s driving philosophy is the standardization and consolidation of services across an organization to streamline and automate them, optimizing costs via boosted efficiency.

Countless businesses have employed this model with good reason: it yields results. If your company utilizes HR or IT teams that distribute the same services for all employees in your organization, you’ve experienced the shared services model.

Large bottlenecks can occur when shared services aren’t executed effectively. Issues that can’t be contained to one department tend to have ripple effects company-wide. Shared services also introduce the risk of overloading service providers’ bandwidths. Given the current IT sector talent shortages, those teams simply cannot be spread any thinner, causing some business leaders to seek alternatives to this otherwise straightforward solution.

2. Cloud-first strategy: leveraging scalability and flexibility

A cloud-first strategy optimizes IT costs by emphasizing Internet, or cloud-based, services for employees and customers over those on-site. A cloud service provider hosts most or all IT services such as databases and applications, all or most of which the client accesses via the internet.

The flexibility organizations gain from an off-site service host makes a cloud-first strategy a good solution for those seeking to pivot business actions based on market behavior quickly.  Meanwhile, IT teams retain the control required for seamless operations without additional resource drains. 

The demands of the current digital world require businesses to provide mobile access to their services; a cloud-first strategy is a fast track to making this happen. Clients can gain entry to their accounts and operations from anywhere while all stakeholders have the assurance of robust, multi-layered data security. 

There are several types of cloud services, the most prevalent of which are IaaS, PaaS, and SaaS.

Infrastructure as a Service (IaaS)

IaaS is a pay-as-you-go cloud-computing service by which users rent servers that provide infrastructures for IT solutions. It grants businesses flexibility for requirements like large data centers. It’s a common solution for IT teams seeking increased control over operations, and fast, effective ways to deploy new applications. 

Platform as a service (PaaS)

The PaaS model provides a platform for users to develop, run, and manage applications without maintaining a cloud system. It includes infrastructure components such as development tools, networks, operating systems, servers, and storage. PaaS clients usually pay a flat fee based on the services they require and the number of users, but they can also pay as they go.

Software as a Service (SaaS)

SaaS is a ready-to-use cloud hosting service that clients rent from vendors who deliver storage, software, hardware, servers, and data security. Users gain access to the platform from anywhere an internet connection is available, on any device.

Most of us use SaaS services daily, whether we’re aware of it or not. Among the most popular SaaS business solutions are Slack and Salesforce.

3. Data center consolidation: streamlining for efficiency

Businesses constantly generate data. As it accumulates, they store it on increasing numbers of servers, then data center facilities. Data center consolidation is a means to reduce the number of these facilities without necessarily reducing the amount of data. 

Stakeholders in corporate mergers often choose a data consolidation strategy as they face the reality (and gargantuan fees) of managing multiple data lakes.

Data center consolidation is a method any business seeking IT cost optimization should consider. The reductions it creates in facilities, equipment, energy usage, and security (all of which make up a business’s data center footprint) costs are significant, no matter the business size. 

These reductions (specifically, the hours and labor they save) can establish new efficiency and productivity gains company-wide.

4. Application rationalization: balancing standardization and innovation

IT sprawl, or stack sprawl, is an insidious problem in tech in that it does not occur suddenly or deliberately. Applications accumulate over time when users need solutions for easier and more efficient operations. Most extraneous apps are IT-sanctioned, however, new software can creep in via shadow IT workarounds. 

The overabundance of ineffective software has become such a financial drain to businesses that application rationalization is a strategy that has emerged to help control spending, reduce security risks, and calculate the total cost of ownership (TCO) of existing software suites. 

If you’re unsure as to what that would look like, the following may give you a clearer picture of the endeavor:

The application rationalization process 

• Set the scope. Depending on the size and type of your business, the target reduction you’re shooting for may appear as a dollar amount, a specific number of apps to let go, or a list of tools that don’t integrate well with your non-negotiable apps. 

• Build a catalog. Create a master list of your organization’s software systems and platforms. Group them by function to identify duplicates, redundancies, and possible freeware alternatives. It’s crucial to include a complete list of all software at this stage; the list will serve as the source of truth as you assess the effectiveness and necessity of each tool.

• Evaluate your portfolio. Talk to the process owners and main users of each application. Ask questions such as: How much are subscription fees? Do free alternatives exist? How many people use the tool, and how often? Does the tool pose security threats? How well does the tool integrate with legacy and non-negotiable systems?

• Develop an implementation roadmap. Based on the assessment findings, devise a strategy for the new software portfolio implementation, including plans for each system to be retained, replaced, or retired.

• Stay at it. Note the cyclical nature of the process. Continually monitor the success and use of each application, setting a time at regular intervals for stack evaluation. New tools that make business life easier and more efficient are released regularly, so stack assessment is quickly becoming a regular occurrence in many organizations. 

1. Enhancing financial transparency: a critical pillar of IT governance

Optimizing costs for seamless IT governance requires financial transparency. Stakeholders want assurance of trust, organizational goal alignment, and accountability. 

Gartner purports the following as main tenets of IT financial transparency.

Gartner’s 6 pillars of financial transparency

2. Managing IT demands: aligning spending with business value

The business sector’s dependence on technology has shown no signs of slowing over the past few decades, translating to steadily increasing IT demands. As we’ve already established, modern IT teams simply don’t have the bandwidth for more tasks. 

Automation technology instills protocols for every request, allowing IT teams to collect the requests (including those made by phone, email, online form, etc.) in one central repository, organized by one or more criteria such as urgency, cost, and risk. 

The flexibility of automated request processes allows businesses to quickly shift gears when occasional interruptions occur for cases with high priority or urgency. 

3. Robotic process automation (RPA): harnessing AI for cost savings

Like demand management, RPA leverages automation technology for significant IT cost savings. RPA bots perform the same action repeatedly, far faster and more accurately than humans. They play an important role in the AI technology most businesses opt for when seeking new, creative solutions for optimizing costs. 

Many businesses deploy artificial intelligence (AI) chatbots as self-service centers for employees and customers. Their benefits include 24/7 help support and fast action for straightforward requests like password resets and equipment replacement. The IT time and labor this saves add up quickly. 

4. IT asset management reevaluation: ensuring optimal utilization

A periodic review of IT assets is essential to IT cost optimization and critical to any continual improvement initiative. A business’s IT assets are the hardware, software, systems, and information it uses for daily business operations. 

Poor asset visibility risks multiple occurrences of hidden costs, from subscription price hikes to unexpected hardware and device replacements. IT teams’ inability to account for assets during audits can lead to even more severe consequences.

Businesses use several methods to account for their IT assets, including spreadsheets and databases. The best method they can employ for such a crucial process is adopting an asset management tool. They make it easy for all stakeholders to stay informed on which stage in its life cycle each system currently resides and automate the tasks of keeping those systems maintained and upgraded.

5. Embrace digital transformation: a path to cost-effective innovation

We’ve made it to the ninth strategy! At this point, you may wonder, “Is there a solution that allows me to embrace all these strategies?” The answer is a resounding YES: digital transformation. This isn’t the first time you’ve heard of it and we’re certain it won’t be the last. 

The “transformation” in the term refers to moving from a manual workflow model to automated processes for efficiency, productivity, and growth. The best digital transformation tools have the following out-of-the-box capabilities businesses making cost-optimization improvements need:

• Cloud computing
• Collaborative services
• Efficient and plentiful data storage
• Automated application review processes
• Automated, customizable asset management
• AI technology for lightning-fast processes and services
• Financial transparency strategies (Including budget planning and cost benchmarking)

Moreover, the AI-powered data analysis of digital transformation platforms actively creates value for businesses by:

• Leveraging existing performance metrics to predict future outcomes based on industry and market shifts, and 
• Vastly improving user experiences, thereby strengthening customer relationships. 

10. Workforce optimization: balancing skill sets and costs

The goal of workforce optimization (WFO) is to best utilize employees according to their skillset, saving businesses the operational costs that result from the subsequent bumps in efficiency and productivity. Training and upskilling play a big role in this strategy, offering bright, reliable employees opportunities to learn new skills for career advancement. 

WFO requires the tracking of several different moving parts, including time, performance evaluations, and, in cases of training existing staff and hiring new people, scheduling. Therefore, businesses should choose a WFO platform with advanced data analysis features and the ability to integrate with and perform human resources (HR) processes. 

Successful WFO is a win for everyone. Businesses reap the benefits of new productivity wins and cost savings, employees with a new sense of purpose enjoy increased morale, and customers report higher satisfaction with service.

Next steps: a roadmap for CIOs and IT leaders

The future of IT cost optimization lies with AI-powered business process management found in business process automation (BPA) tools. The time and resources saved by automation, coupled with the data-driven insights gleaned from powerful analytics tools, will give businesses the foresight and flexibility they need to compete — and excel — in the coming years.

The 3 components of BPM 

Pipefy’s no-code BPA platform is already lighting the path to the future for our customers, one automated process at a time. 

A low learning curve, fast deployment, myriad integrations, and simple, customizable forms, databases, and portals are just a few reasons why Pipefy was included in the 2023 Market Guide to Business Process Automation Tools.  

The post Strategic IT Cost Optimization: 10 Key Techniques To Increase Business Value appeared first on Pipefy.

Ensuring that a change management implementation is successful can be a challenging process, but learning lessons from others makes it easier. The following real-world examples show how businesses have managed major changes within their organizations.

Change management definition

Change management is a strategy for guiding an organization and its members through changes, especially those with far-reaching effects. It generally refers to changes that affect employees at all levels, rather than orders that come from a higher level in the organization.

Change management typically focuses on people by encouraging them to actively participate in the process. This approach helps people adapt to the changes more easily and use them in their daily work.

Change management must also align with an organization’s beliefs, culture, and values to succeed, as a good match feels more natural and allows employees to adopt the change more easily. In addition, a smooth transition is more likely to lead to other lasting changes in the future.

8 real-world change management examples

The following examples of change management serve as inspiration for your own organization’s changes. They take place in a variety of industries and applications, but they all show how change management benefited the organization.

1. Global expansion

A Chicago-based health system announced plans to integrate with two other organizations to expand its global presence to almost 100 locations. Its leaders recognized the need to support the health system growth strategy with an effective change management plan that would provide a repeatable means of supporting personnel on both sides of the integration.

The plan included pairing leads from the health system with staff members in each new organization, providing them with one-on-one guidance through the system. This approach created a group of change practitioners, which the health system held meetings with every two weeks.

Change leaders also used their consistent, well-structured change management model to implement an ERP, which they can also use for future integrations, maintain effective patient care, and solve previous adoption challenges for existing hospital staff in HR, IT, finance, and supply chain functions. In addition, AHS leaders launched an ERP coaching program for the new change practitioners.

The change management work done at the beginning of the integration immediately paid off with high engagement, high awareness, and increased change management maturity throughout the health system. 

2. Cross-platform training

Sophos, a global cybersecurity company, was using Salesforce applications to streamline its sales process. However, it was facing challenges in change management due to the frequent updates to Salesforce, which included workflow changes, user interface (UI) enhancements, and other new features.

The sales team was struggling to keep pace with the updates, prompting operations managers to implement Whatfix, a digital adoption platform (DAP). This platform provided the sales team with on-demand training to help them master the new Salesforce features.

The training included interactive walkthroughs for Salesforce’s basic functionality and step-by-step guides for more detailed information on workflows. The change team also created in-app smart tool tips to describe the new controls in the Salesforce updates.

In addition, they embedded videos and other media in the applications to communicate product information, keeping product and sales teams aligned on new products.

The implementation of Whatfix’s DAP helped Sophos successfully manage changes in Salesforce and is a contributing factor to YoY 15% reduction in Sales Ops cases globally — an estimated 12,000 cases a year less.

3. Sustainability

Unilever is a product manufacturer with growing concerns about the sustainability of its products, largely due to climate change and resource depletion. The company needed to address these issues to maintain long-term relations with its customers amid their rising expectations for ethical manufacturing.

Unilever implemented a change management plan for its sustainability measures that included integrating environmentally-friendly methods into its supply chain and manufacturing processes.

This plan included measures for sustainable sourcing that committed the company to sourcing raw materials like tea and palm oil sustainably. For example, Unilever partnered with small farms with environmentally friendly practices. They also increased their focus on reducing emissions and other waste, including a goal of 100% compostable, reusable, or recyclable materials used for packaging.

The company also set targets for energy and water usage in their manufacturing processes. It started using concentrated detergents that require less water and added plant-based alternatives to meat in its manufactured food products.

In addition, Unilever’s change management plan included engagement with communities by supporting initiatives on education for environmental, hygiene, and sanitation issues.

4. Cultural change

Zurich Life, a global insurance company, was struggling to adapt to changing customer behaviors and market conditions in the late 1990s. It had become overly bureaucratic during a time when that industry was experiencing rapid changes in regulation and public policy, causing executives to recognize the need for faster processing times and lower operating costs.

Key steps in this change process included the implementation of software that allowed agents to see all the clients’ investments from a single interface. Zurich also recruited staff to serve as change champions and encouraged executives to increase their visibility to staff, thus improving employee engagement.

Steps for reducing bureaucracy included a reduction in the size and frequency of meetings. Zurich also encouraged staff to identify processes suitable for elimination.

These change management initiatives improved collaboration and customer service at Zurich, resulting in a happier workplace.

5. Digital transformation

As the University of Virginia (UVA) faced constant challenges of digital change, the organization began to experience a growing sense of change fatigue and weren’t achieving the intended results on high-priority projects. The need to remediate this growing trend was the primary reason that UVA decided to implement a change management policy.

Key actions in this initiative included certifying appropriate team members in change management methodologies and building change capabilities into existing workflows. The UVA also encouraged project managers to serve as change managers for their respective projects.

This institution has since achieved its performance goals in managing change, allowing it to thrive in the shifting environment that’s currently common in higher learning and complete its digital transformation.

6. Tool integration

In 2020, Microsoft’s system of tracking sales lacked automation which created challenges like duplication of effort, compliance gaps, and data misalignment for its leaders. As a result, the company set a goal to facilitate sales tracking across all geographic regions and organization roles.

It was determined to build a system that would meet everyone’s needs, despite the knowledge that it would disrupt these operations in the short term. One of the biggest challenges Microsoft faced was the resistance of its own employees to accept new business processes.

The company followed many best practices for managing change, including its use of the Awareness, Desire, Knowledge, Ability and Reinforcement (ADKAR) model. 

Components of its change management plan included the identification and documentation of key barriers to change. The company also encouraged the people reporting this information to become change agents and held meetings to prioritize project requirements at least once a week.

Additional measures included the deployment of plans to predict the company’s readiness, delivery and adoption of change. Microsoft also implemented multiple channels to gather feedback from end users regarding the proposed changes.

As a result of these practices, Microsoft has created a culture of continuous improvement that adapts change to meet its employees’ needs.

7. Quality improvement

General Electric (GE) had been in business for generations, but CEO Jack Welch felt it needed a complete overhaul to continue growing. He focused on improving product quality, primarily through the use of Six Sigma. This methodology attempts to reduce defects in products and processes, largely through continual testing and retesting.

Welch’s change management plan saved GE $10 billion, which he says accounted for half the changes at the company. The other half came from “people issues”, including the assembly of a team closely aligned in its beliefs, values, and vision.

These changes transformed a company that was already successful into an international powerhouse. GE had a market value of $12 billion when Welch became its CEO in 1981, but it was worth $280 billion when he left the company in 1998.

8. Online ordering

Domino’s was struggling to remain relevant in the highly competitive pizza delivery business in 2008, when key players realized that online orders would become the path to success in that industry.

They convinced Domino’s top management to focus on this channel, which differentiated the company from its competitors at that time. The company implemented a range of technologies that allowed customers to order pizza from many online platforms, including Alexa, Facebook, Google Home, Smart TV, and Twitter.

The next step was to implement customer loyalty programs as a means of driving sales, largely based on customer data the company already had.

This change management implementation was highly successful, helping to make the company more competitive. Domino’s still embraces change today and has recently tested new methods of delivery, including self-driving cars and drones.

Change management improvement

A clear understanding of the obstacles to change is necessary when implementing a change management plan. A DAP can help you with this process by introducing digital technology and processes without disrupting current operations. It also allows team members to learn new software through features like in-app guidance, smart tips, and interactive walkthroughs.

Pipefy’s no-code platform supports change management by empowering business teams to design workflows, document and plan for any risks and impacts, automate approvals, collect feedback, and assign the right people to each stage of implementation. 

With real-time audit trails and dashboards, collect the updates you need easily. Pipefy also integrates into existing workflows, minimizing disruption, standardizing workflows, and creating a consistent user experience.

The post Change Management: Real-World Strategies and Examples  appeared first on Pipefy.

Gartner predicts that the market for business process automation tools will increase by 38% in the next three years, up from $2.6B in 2022 to $3.6B in 2027. More business teams than ever rely on automation to reduce errors and improve efficiency, and the varieties and types of workflows they’re automating continue to diversify.

Access the free Automation Trends Report now 

Get the report

Benefits for IT teams

At the same time, IT teams are seeing benefits from automation. No-code automation tools that give business teams more autonomy are also conserving IT resources – giving them more time for the strategy and innovation that are essential for optimizing costs and spurring new business growth. 

Pipefy recently published the 2024 Process Automation Trends report that explores why more companies than ever are turning to business process automation tools and artificial intelligence to navigate economic challenges, drive business growth, increase business team autonomy, and make the best use of their IT resources.

What are the current trends in process automation? 

This report looks at which teams are using automation the most often, as well as which processes and workflows they are automating. Based on anonymized customer data, the report details

• Distribution by team for more than 10B automations
• Breakdown of automations by process for each team
• Analysis of estimated hours saved through email automation

Business process automation trends highlights

The report delves into detail to illustrate where automation is making the biggest impact for businesses. For example: 

• Customer Support teams automated more than 1B workflows
• Finance teams automate the broadest range of workflows, totaling 648M
• IT teams automated 251M workflows and service requests
• Finance teams automated the widest variety of workflows

IT Leader insights

The report also looks at business process automation trends from the perspective of IT leaders. also includes data from a recent survey in which they were asked about

• Drivers behind their adoption of automation tools
• Benefits their teams are seeing from automation tools
• How they evaluate automation tools
• Anticipated benefits from the fusion of AI with process automation
• Expected efficiency gains from AI-enhanced process automation

Learn more

Pipefy’s 2024 Automation Trends Report is free and available for download now. 

See which teams use automation the most, and which processes and workflows they are automating most often. The report includes information on the growth of the no-code automation market and insights from IT leaders about the benefits of automation and what they expect from the combination of artificial intelligence and process automation.

The post 2024 Business Process Automation Trends  appeared first on Pipefy.

When multiple people, business lines, and software systems intertwine, process management may require critical thinking on the part of several people to unravel the task and develop the definitive steps to complete it. Naturally, the bigger the business, the more complex this type of process management can become when processes lack structure and standardization. 

But the work doesn’t end once the process is defined and established. It will be performed many times over the course of weeks, months, or years, so it’s critical that processes be documented to ensure current and future stakeholders are aligned, following steps correctly, and not introducing any errors. 

What is process documentation?

Process documentation is the creation of an internal document that details the steps and resources needed to start, run, and complete a process. It’s a step-by-step guide for all stakeholders, laid out in an easy-to-understand format, filled with steps, owners, handoffs, tools, policies, and any additional relevant details. 

Insufficient documentation can temporarily go unnoticed, appearing as occasional delays or small inconveniences. For example, managers at a testing lab may be perplexed by a sudden, alarming rise in late fee charges. After some investigation, they discover that a new technician has been consistently late in providing his testing log approval signature, which violates established service level agreements (SLAs) with several lab customers. 

Those late fees and new technician delays could have been avoided had there been process documentation outlining duties, SLAs, and specific deadlines as part of new staff training. 

Types of process documents

Process documentation can take many forms, like procedures, logs, and simple checklists. 

Read on as we examine the most common types and the role each plays in process management: standard operating procedures (SOPs), policies, checklists, logs.

Standard operating procedures (SOPs)

These ubiquitous documents consist of detailed instructions and protocols for completing tasks in the most efficient, effective manner possible. SOPs are designed to mitigate risks and decrease errors. When businesses have quality standards to meet, the SOP is their governing source.

Policies 

Businesses write policies when they need to establish guidelines for making decisions and performing actions. A policy consists of a specific purpose and scope — including all relevant information for achieving that purpose — organized into a logical, clear document. Organizations regularly review all policies for accuracy and continued relevance, revising them when necessary.

Checklists 

A process checklist is a list of steps required to complete a process. It may sound simplistic, but checklists are a great way to impart the various phases of a process, including their chronological order, to a diverse range of employees. When multiple people use the same checklist as a source of truth, work becomes standardized and, therefore, consistent. 

Logs

Event logs are time-stamped records of events as they occur, usually in the form of a sequential list generated by some workflow management software systems. The events reveal valuable information about each task, including:

• Who performed the task.
• The time and date the task started or completed.
• The task’s priority level.
• The task’s success and failure.

Automated event logs are invaluable for understanding and visualizing process flows. Their biggest benefit, however, is that they serve as audit trails, providing businesses with no-effort, built-in regulatory compliance.

Benefits of process documentation

Many businesses dismiss process documentation as an afterthought, rushing to churn out instructions quickly in order to move on to the next process. They may reason that it does not propel business or that it is not a value-added activity. This is a mistake. 

Well-documented processes can quickly and easily be understood, completed, and replicated by stakeholders. Here are a few key benefits to consider as you explore process documentation.

Standardization

Standardizing business processes is the main objective of documentation. When individuals diverge — even very slightly — from documented process steps, a door opens to the risk of noncompliance and produce variance. 

Documentation is the source of truth for everyone (or machine, if the process is automated) carrying out that process to follow. The documented steps have been tried, tested, and proven to yield the best possible results for the organization. 

Training

When a new hire is onboarded, managers perform verbal and instructional training, but this should always be underpinned by a single source of instructions for the specific methods used to carry out daily processes. The official process documentation is that source.

Using approved documentation to train employees is a win-win-win because:

• New hires receive thorough training, resources to succeed, and a solid foundation for their career at their new company.
• Team members exchange time spent training new hires and correcting errors for opportunities to establish great working relationships with onboarding co-workers. 
• Businesses see minimal work delays/pauses due to preventable errors and missed details.

Compliance

Process documentation is a critical aspect of regulatory compliance, whether the regulations in question are industry standards or health and safety protocols. 

Well-documented processes specify resources and methods for mandatory checks and testing to ensure that every product or service contains no risks to people or environments.

Improvement

Goals shift as new tech innovations become available. In these circumstances, comprehensive process documentation is an invaluable resource. 

By providing an unrestricted, end-to-end view of processes, teams can identify places for improvement or have the information necessary to optimize workflows within those processes. Those improvements make businesses more competitive in quickly-shifting markets and allow them the agility to adapt to changing needs.

The evolving role of process documentation in business strategies

Process documentation begins with process mapping, which is an image-based representation of processes. A process map most often takes the form of a flow chart. 

Documentation is the corresponding verbal representation of a process. It is meant to communicate a process to stakeholders in detail, whereas the visible information on a process map is often limited to the names of phases and brief descriptions thereof.

Manual process documentation

Documenting processes as a business objective became common in the mid-20th century with the advent of project management. Engineers of that era recognized a need to create official records of business and project best practices. Those early documents existed in hard copy only, and were handled and controlled manually. 

This also means that this type of process documentation is not ideal for rapid changes, implementation, or sharing because of the manual control and distribution. 

Digital process documentation 

Time and technology changed process documentation, along with most business operations. The vulnerability of paper to damage and destruction had long been recognized as an issue for those who knew they’d need their hard copies well into the future. 

As digital formats went mainstream, businesses wasted no time adopting them, and by the 1990s, scanning documents using Optical Character Recognition (OCR) emerged as the best way to capture process documents in digital file formats for long-term storage. Machines could now read data, therefore manual computer data entry began disappearing from job descriptions.  

Automated process documentation

The late-20th century rise of automation had a massive impact on process documentation. One of the key objectives companies list when automating processes is streamlining for efficiency and savings; documentation became recognized as its foundational element. It simply is not possible to find all areas of waste and improvement without end-to-end visibility, which documentation provides.

The automation boom has also established process documentation as a precursor to setting up a workflow management tool, like no-code business process automation (BPA). BPAs not only automate, they align day-to-day tasks with business KPIs, integrate multiple software systems and apps, and standardize processes for current and future stakeholders with conditional logic, defined process steps, and mandatory fields.

Step-by-step guide to documenting business processes

If you’ve decided to document a process, get started by evaluating these seven steps as you research methods and tools. 

1. Identify the process. Choose a process that has a clear purpose. As you make your choice, pick a process whose purpose positively impacts many individuals, teams, and other processes in your organization.
2. Set boundaries. It’s extremely important to identify the starting and end points of the process. Open-ended processes can’t be relied upon for the expected results, and processes that lack one specific entry point are unclear from the outset.
3. Establish inputs and outputs. Process inputs are the resources required to complete a process. Process outputs are the results expected when a process is completed. It’s important to set these from the beginning to stay focused on your objective, and break down each phase into smaller tasks.
4. Identify steps. Determining the steps of a process may sound simple, but omitting steps can be quite easy — especially because they seem like such obvious steps. Ask yourself and your team questions like: what triggers this process from the start, and what direct actions create the end results? Organize those steps into sequential order. 
5. Consult stakeholders. Each individual who interacts with the process has a slightly different perspective on how it works (and how it should work). Take this time to learn from them. By contacting stakeholders outside of your own team, you will gather valuable insights into issues you hadn’t previously considered.
6. Build a flow chart. Combine all of this information and arrange it chronologically into a flow chart. Be sure to include the inputs and outputs of the process (and each step within it), service level agreements (SLAs), and the individuals involved.
7. Identify exceptions. As you create documentation for multiple processes, you’ll find variances among them. Approval sign-offs that circulate to different departments or higher levels than most are a good example of this. Note these exceptions in your documentation for clarity.
8. Test the process using the documentation you’ve created. Did all phases run smoothly? Did you include every step? Could others easily follow it? If so, congratulations! If not, begin with the first deviation that occurred and investigate the issue.

Process innovations and best practices

A fast, effective innovation in process documentation has emerged from robotic process automation (RPA). RPA is a block of code that quickly and repeatedly carries out a task based on program rules. These code blocks, or “bots,” can automatically generate event and activity logs faster and more accurately than previous methods, essentially handing quality control professionals formatted audit trails on a silver platter.

Keep the following tips in mind as you create your process documentation:

• Use clear, concise language. The instructions for each process should be easy for all users and employees to understand.
• Use images to illustrate what you want to convey. Written instructions only go so far; a visual representation can eliminate a good bit of confusion, especially when mapping certain handoffs or complicated workflows. When in doubt, try a screenshot or screen recording to demonstrate a concept.
• Keep text brief and easily readable. Organize written text with small paragraphs and bullet points, breaking big concepts into smaller ideas.
• Ask for feedback. The best way to assess how well something is working is by actually completing the process and then seeking feedback from those following the process documentation. Seek feedback from stakeholders for clarity, especially when process updates have occurred or when a step isn’t followed correctly. 

The future of process documentation: AI solutions

The past decade has seen astonishing developments in AI that documentation developers have been eager to add to their customers’ toolboxes. Some process documentation tools already incorporate chat-based AI tools that create linear processes from scratch, using sources like screenshots, bills of material, and checklists — all without human intervention.

The future of AI-automated process documentation includes the following features: 

• Visual, intuitive interfaces for rapid process customization.
• Document templates for businesses that frequently run similar processes.
• Shared folders, databases, or portals for document organization.
• Standardized intake forms with mandatory fields to keep processes running correctly from the start.

Better process management with Pipefy

The end goal of process documentation is sharing your documents with team members and implementing streamlined processes for staff to follow daily. 

Pipefy’s no-code BPA platform gives you and your team tools to turn notes, ideas, and objectives into practical, streamlined processes for more efficient operations, visible cost savings, and faster, error-free outcomes. 

Its visual user interface makes building and standardizing processes from scratch or a template easy and accessible to all business teams with IT in mind. That means teams become less IT dependent so IT gains the bandwidth to focus on more than the backlog.  

The post Business Process Documentation: Benefits & How to Get Started appeared first on Pipefy.

However, data’s increasing value in today’s organizations leaves little room for IT errors – sensitive data can fall into the wrong hands or compound existing process inefficiencies until those processes fail.

Implementing an IT framework is a sound approach for regaining control of your IT environment, but it requires care in choosing the right model.

This post provides a general overview of IT frameworks and an examination of specific frameworks with a focus on selecting the best framework for your organization.

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What is an IT framework?

An IT framework is a set of guidelines, instructions, and principles that determine an organization’s IT infrastructure. This framework ensures that an organization’s technology aligns with its overall business objectives, industry best practices, and government regulations.

IT frameworks also maintain order within the IT department itself. While an IT framework governs IT processes and systems, it doesn’t directly control decisions made by other departments.

Most popular IT frameworks

Each IT framework prioritizes its own set of factors for pursuing specific goals in IT governance. It is therefore critical to carefully select a framework that aligns with your organization’s unique requirements. The best IT frameworks include the following:

1. ITIL
2. COBIT
3. MOF
4. ISO
5. TOGAF
6. PRINCE2
7. PMBOK
8. CMMI
9. COSO IT
10. VAL IT
11. FEAF

ITIL

The Information Technology Infrastructure Library (ITIL) is recognized as a standard IT framework throughout the world. ITIL v4 is the current iteration as of 2024 and is designed for modern IT technologies including process automation, cloud computing, and DevOps. ITIL v4 addresses the following four dimensions of IT service:

• Organizations and people
• Value streams and processes
• Information and technology
• Partners and suppliers

It also includes 34 practices grouped into the following three categories:

• General management practices 
• Service management practices 
• Technical management practices

COBIT

The Information Systems Audit and Control Association (ISACA) developed Control Objectives for Information and Related Technologies (COBIT) framework. It was initially released in 1996, and the latest iteration is COBIT 5, which was released in 2012. This version of COBIT is based on the following five principles:

• Meeting stakeholder needs
• Covering the enterprise end-to-end
• Applying a single integrated framework
• Enabling a holistic approach 
• Separating governance from management

COBIT 5 also recognizes the following seven components of IT:

• People, policies, and frameworks 
• Processes
• Organizational structures
• Culture, ethics, and behavior 
• Information 
• Services, infrastructure, and applications
• People, skills, and competencies

MOF

The Microsoft Operations Framework (MOF) takes a holistic view of IT environments consisting of people, processes, and technology. It uses the following four quadrants to organize components for the framework:

• Changing quadrant 
• Operating quadrant
• Supporting quadrant 
• Optimizing quadrant

The current version is MOF 4.0, which guides an IT infrastructure across its entire life cycle, including design, development, operation, maintenance, and retirement. MOF 4.0 integrates many IT processes, including compliance, governance, risk, audits, and best practices as defined by Microsoft Solutions Framework (MSF).

ISO/IEC

The International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC) jointly published ISO/IEC 27001 in 2005; it was most recently updated in 2022.

This publication defines standards for an information security management system (ISMS) for the purpose of managing IT risks and improving security. ISO/IEC 27001 specifies requirements for all phases of an ISMS, including creation, implementation, maintenance and continual improvement.

Organizations that pass an audit by an accredited certification body receive ISO/IEC 27001 certification.

This IT framework is based on the following three principles of information security:

• Confidentiality 
• Information integrity
• Availability of data

TOGAF

The Open Group Architecture Framework (TOGAF) is developed by The Open Group and is based on the United States Department of Defense’s TAFIM and Capgemini’s Integrated Architecture Framework (IAF).

It offers a high-level approach to design and is currently one of the most popular frameworks for enterprise architecture. TOGAF relies heavily on the modularization and standardization of proven technologies.

This framework takes a high-level approach to IT governance based on the following four levels:

• Business 
• Application
• Data
• Technology

PRINCE2

PRojects IN Controlled Environments (PRINCE2) is a structured project management model the United Kingdom government developed specifically for information systems. It initially released PRINCE2 in 1996 and transferred ownership of PRINCE2 to AXELOS Ltd. in 2013, which is jointly controlled by the U.K. government and private interests.

PRINCE2 emphasizes the division of projects into manageable stages by using the following six tolerances:

• Scope
• Timescale 
• Risk 
• Quality 
• Benefits 
• Cost

PMBOK

The Project Management Body of Knowledge (PMBOK) is a set of guidelines for general project management that apply to IT projects.

The Project Management Institute (PMI) oversees the work, but other organizations contribute to it. PMBOK has evolved significantly over time, with the 2021 7th edition release being the most recent.

PMBOK offers unique features in project management, including its work breakdown structure (WBS) and critical path method. It also contains principles that overlap with general management regarding organizational operations.

Additional overlaps between PMBOK and other management disciplines include budgeting, financial forecasting, organizational behavior, and management science.

CMMI

Capability Maturity Model Integration (CMMI) is a program for appraising and improving processes. The CMMI Institute, a subsidiary of ISACA, published the first version in 2010, with the 2023 3.0 version being the most recent.

CMMI can be used to guide process improvement in any functional area, although it’s most commonly associated with IT.

This model is based on the following five maturity levels:

• Level 0 – Incomplete
• Level 1 – Initial 
• Level 2 – Managed 
• Level 3 – Defined 
• Level 4 – Quantitatively managed 
• Level 5 – Optimizing

These levels are hierarchical, such that each level includes the same requirements as the one below it, along with additional requirements. The end goal of CMMI is to raise all processes under its control to level five, although organizations may never achieve this goal for all processes.

COSO IT

The Committee of Sponsoring Organizations of the Treadway Commission (COSO) develops its self-named Enterprise Risk Management (ERM) framework. This framework is comprehensive, as it addresses operational risks across many areas, including IT.

As a result, it’s widely used for integrating risk management within an organization and isn’t specifically a framework for IT governance. However, COSO also offers a framework that is designed for IT, which is COSO Internal Control – Integrated Framework (COSO IC). This framework focuses on an organization’s internal controls within an organization, including those related to IT.

VAL IT

ISACA develops VAT IT, an IT governance framework. It complements and expands on COBIT by including a comprehensive IT governance control framework. The primary difference between COBIT and VAT focuses on investment decisions and their expected profits, while COBIT focuses on the framework’s implementation.

VAL requires support from senior management to be effective, not just lower-level leadership. IT provides a comprehensive framework that other processes must support, along with other guidelines that help executives understand and evaluate investments in IT.

FEAF

The U.S. federal government uses the Federal Enterprise Architecture Framework (FEAF) to manage enterprises within the federal government. It delivers standardized practices for developing and implementing IT governance with the Collaborative Planning Methodology. This methodology consists of the following types of actions:

• Identify and validate 
• Research and leverage 
• Define and plan 
• Invest and execute 
• Perform and measure

Importance of IT frameworks

Boards of directors don’t typically attach much importance to IT, especially when their organization doesn’t have IT governance. They usually lack the technical knowledge to ask pertinent questions within this area, leaving IT managers to manage IT assets. This dynamic often results in IT managers making unique decisions based on whims or limited knowledge.

This lack of IT oversight poses a serious threat because it exposes the organization to risks like failure to manage IT assets. Large enterprises have successfully managed this challenge by using IT frameworks to establish board-level committees to monitor and manage IT. These committees can then work with other committees at that level on functions like audit, compensation and governance.

Specific benefits of IT frameworks include the following:

• Risk management
• Improved decision-making
• Enhanced compliance
• Process standardization
• Better communication and transparency

Risk management

An IT framework with built-in risk management can significantly minimize the cost of data breaches. UpGuard reports that the average cost of a data breach for one business was $3.86 million in 2020, which can have devastating consequences for smaller companies.

In addition to direct cost savings, risk management can help an organization quickly achieve its goals. It also increases its resistance to cyberattacks and stabilizes business operators. Additional benefits of risk management include a reduction in legal liability, resulting in insurance premiums.

Improved decision-making

An IT framework’s ability to establish objectives, principles, and structure for an organization helps improve its decision-making ability. Leaders use these functions to monitor projects and resource usage more effectively, resulting in IT decisions that align with business goals. These decisions help save money by yielding a better return on investment (ROI).

IT frameworks also help define stakeholder responsibilities and create mechanisms for accountability, enabling clear decisions. It also implements the mechanisms required to monitor IT operations, ensuring they meet an organization’s needs.

Furthermore, IT frameworks usually focus on the governance aspects of decision-making, such as who makes decisions, how they make decisions, and how those decisions should govern operations. This capability allows IT managers to focus on operational decisions.

Enhanced compliance

IT governance, risk management, and regulatory compliance focus on different requirements, but they directly overlap. For example, the risk management function uses governance to mitigate risk by implementing controls. It then alerts administrators when users act outside the organization’s risk boundaries.

Compliance is becoming an increasingly important benefit of IT frameworks due to the current trend towards greater regulation in most environments. IT frameworks usually include road maps for regulatory compliance, especially those for data storage.

This feature facilitates auditing by ensuring applicable information is accessible, thus reducing financial and legal risks.

The compliance function of IT governance causes organizational activities to proceed in a way that complies with industry standards and government regulations, resulting in the proper use of infrastructure and effective data protection.

Process standardization

IT frameworks standardize processes, ensuring consistency across services. This feature prevents processes from skipping required steps, thus reducing the number of errors and improving reliability.

When executed well, standardization provides employees with a well-tested process to use that reduces ambiguity, improves quality, increases productivity, and boosts employee morale.

Process standardization improves the clarity of operations by eliminating the need for guessing to find the best procedure. It also increases quality by ensuring work is done in an optimized manner.

Standardization can significantly improve productivity when employees no longer rely on documentation or co-workers for answers. Employee morale is also improved when employees take pride in their mastery of a process.

Better communication and transparency

Stakeholders gain a better understanding of IT resource usage when they can see how the IT department functions. Greater transparency thus leads to better decisions and overall performance. Employees are also more inclined to share information when they observe open communication from upper management to the rest of the organization.

IT frameworks help an organization’s members share innovative ideas like workflow improvements or more detailed feedback in performance reviews. Companies thrive when their workforce feels safe bringing new ideas forward, leading to happier, more engaged employees.

A transparent workplace also recognizes success, building trust between workers and management and improving the company’s bottom line. Sharing knowledge across an organization shows that management respects employees, setting the precedent that transparency is a valued expectation.

The transparent communications enabled by IT frameworks also improve customer relations because staff members now care about a company’s performance. Employees root for their organization to thrive when they feel respected and trusted.

How can Pipefy help you implement IT frameworks?

Best practices for implementing an IT governance framework include a clear definition of goals and methods of measuring success. These key performance indicators (KPIs) require regular monitoring and reporting to ensure they remain aligned with business goals.

Pipefy’s no-code platform allows citizen developers to employ cost-effective, efficient business process management. They can also automate routine processes, driving organizational improvements in business units like HR, finance, marketing, and sales. In addition, our platform offers robust analytics that help users discover previously hidden insights into your organization’s operations.

The post 11 Most Popular IT Frameworks You Need to Know About appeared first on Pipefy.

IT teams are still expected to manage day-to-day operations and security. But businesses also leverage their IT teams to achieve broader goals. Businesses look to IT to help contain costs, inform strategy, and deliver innovative solutions to solve problems across the organization. 

According to Brian Greenberg, CIO at Fortium Partners: “As businesses increasingly rely on technology to drive growth and innovation, the responsibilities of IT leaders have evolved to encompass strategic planning and alignment with business objectives.” 

In other words, IT is no longer only working behind the scenes to keep systems running. They now also play a critical role in defining business goals and driving revenue growth. 

How are IT teams doing this? By expanding their zone of influence. 

What is the IT zone of influence? 

The IT zone of influence refers to the areas of the business where IT has visibility and control over all processes and workflows. That includes areas like finance, HR, customer operations, marketing, and others. 

In most companies, limited IT resources and bandwidth means that IT stays focused on the processes closest to the business core. Everything else — support processes and long tail processes — are primarily managed by the business teams that own them. But this situation leads to a number of problems that inhibit business growth. 

For example, when teams like HR and finance need their processes optimized or automated, they often must wait for IT resources to be available. In the meantime, their requests languish in the backlog. Frustrated, these teams resort to ad hoc workarounds that, in the long run, create more problems than they solve. 

Learn more about the different types of business processes. 

What happens outside the zone of influence?

Somewhere between 60-80% of department-led processes and workflows currently fall outside the typical IT zone of influence and often lack accountability, structure, and standardization. They may also be overdependent on complicated legacy systems or tribal knowledge, leaving IT teams with little or no visibility into how these processes are being managed or how they are performing. 

As a result, these departments may struggle with broken work handoffs, errors, delays, and data silos stemming from uncontrolled stack sprawl. They may also lack the agility they need to respond quickly to changes in business strategy, markets, or competitor activity. 

The new zone of influence

In a recent poll, CIOs named “automating business and/or IT processes” as the number one step they are taking to drive business results. Why? Because CIOs understand that the untapped efficiency and productivity gains within these processes hold the keys to company growth, cost optimization, and digital transformation.

In order to optimize and automate these processes, IT teams need to exert control and gain visibility into how they are being managed. In effect, they are “moving the line” that determines what falls within their zone of influence. By doing so, IT teams are able to: 

• Improve their ability to mitigate risk.
• Enhance utilization of the tech stack.
• Create more positive customer experiences.
• Make a positive impact on cost optimization and revenue.
• Reduce time to deliver projects that support business goals.

The challenge for IT

New expectations create new challenges for IT teams. IT teams already have their hands full managing day-to-day operations and security, so finding the time and resources to expand their zone of influence can be challenging. The situation is compounded by factors such as: 

• Stack sprawl.
• Neverending backlogs.
• Complex legacy systems.
• Quickly changing business demands.
• Unstandardized or unstructured workflows. 

Combined with IT talent shortages, burnout, and turnover, FTE costs are bound to skyrocket and IT’s reputation within the organization tarnishes as teams struggle to keep pace with all of the requirements and requests that come their way. 

According to Pipefy CTO Daniele Gemignani, a tell-tale sign that IT is struggling to keep pace with business needs is rising tension between IT and other areas of the company. “In this scenario, IT teams experience burnout and reduced productivity. It stifles innovation, as the IT team becomes too overwhelmed with immediate challenges to explore new ideas or improvements.” 

But IT teams are resourceful and inventive. Not only are they adapting to accommodate the new expectations placed upon them, they’re using tools and building partnerships that help them excel at supporting strategy and driving revenue. 

How IT teams are moving the line

One strategy many IT teams are using to expand their zone of influence involves building more collaborative, mutually-beneficial relationships with other departments. This involves giving business teams more autonomy, while ensuring that IT retains full visibility and control. 

Typically, businesses accomplish this through the use of business process automation (BPA) tools. By adopting a low-code/no-code BPA platform and a customized suite of workflow management tools, companies can bring more processes under IT supervision without stretching their teams too thin or adding to already complex tech stacks. 

BPA benefits for business and IT teams

Why business process automation?

Gartner predicts that the market for BPA software will grow by almost 40% by 2027, increasing from $2.6B to $3.6B — and it’s not by chance. IT leaders recognize that BPA software can help them expand their reach and influence and improve processes across the org. 

Pipefy recently surveyed enterprise business and IT leaders on their motives for adopting process automation. Most cited its capacity to increase efficiency, improve productivity, and reduce errors. Almost half (49%) said that the appeal of process automation was in its ability to conserve IT resources.

We also asked about the anticipated benefits of adopting process automation. The most frequent responses indicate that process automation has an important role to play in business strategy and execution, as well as cost optimization efforts.

Moving the line: good for the business, great for IT

Business teams want solutions quickly. C-suite executives want to see revenue growth and cost containment. IT leaders want the visibility and control to create new efficiency gains, eliminate data silos, and drive productivity among their FTEs. 

By increasing IT’s zone of influence, IT teams can deliver what each stakeholder needs to support the business. To do just that, IT teams are turning to process automation tools. 

Gartner predicts massive growth in the BPA tools market. If their predictions hold true, it will mean that the IT teams who are most successful at balancing day-to-day operations with new expectations are those that see the value of giving business teams more autonomy. 

In doing so, they give themselves more time for the strategy and innovation efforts that drive revenue growth.

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