The journey to self-optimizing business processes

AI and automation are rapidly shifting our expectations for digital experiences, but the path for businesses to get there isn’t all that clear.

When done right, AI and automation can be applied across the entire customer journey so that businesses can quickly and continuously adapt to new demands, both from customers and from the broader market. And with the right architecture, AI and automation can help drive entire operations—creating a self-optimizing organization.

Self-optimization means businesses will be able to identify new requirements and adjust their strategies in real time, so they become more resilient against unforeseen challenges, no matter how big or small. By applying intelligence across every process and action within their organization, employees will be able to focus their time and expertise on solving customer challenges and driving innovation. 

But many companies still struggle with the next phase of digital transformation. While businesses use many different forms of AI and automation already, they’re often isolated in the form of chatbots, RPA (robotic process automation), or report generation. But this is just one stop along the road to self-optimization. And with new generative AI tools and solutions flooding the market, that road continues to rapidly change. Enterprises need a roadmap to tie all this together for their journey with a broader strategy in mind.

How to achieve self-optimization

The reality is that the journey to self-optimization isn’t a short one. But done iteratively over time, businesses can realize ongoing improvements that make a measurable impact with each step. There are five distinct stages that businesses must go through to achieve the ultimate state, one in which they can apply a new level of intelligence to every process so they’re able to predict and pivot faster than ever before:

The baseline (stage 0)

Much of the work happening within businesses is not just manual, it’s also largely unmanaged. People are doing work with little structure or process behind it, leading to inconsistency, and there’s no way to track against best practices or prioritize the most important work. Disjointed and siloed processes are much to blame. As businesses grow over time, their IT infrastructure becomes even more complex and hard to streamline or integrate with new systems. Through no fault of our own, this is often the state that many businesses operate in today.

Creating a case for structure (stage 1)

The first step is to create a structure for your work processes and each individual task—what we often call a “case.” This enables work to be better managed, even if many stages within a process are still manual. This structure allows you to track how work is done, ensure the most important tasks are being worked on, and measure work against best practices. It also allows you to identify the areas that may be working against you, like unnecessary tasks that may distract or take time away from your teams, so you can make the changes necessary to streamline mundane work and focus on areas of value. Creating a structure for your work can be overwhelming, so start small. Take one process at a time, be deliberate, then expand.

Setting the stage for automation (stage 2)

Now that you have a structure in place, you can begin to add automation. Just like your skeleton gives your muscles something to work against, your case management structure provides a skeleton for applying automation. This might mean using rules to automate decisions or using APIs or RPA to connect to other systems. Here is where you can begin to automate routine tasks and instead focus time on solving customer problems.

Using data to your advantage (stage 3)

As you automate more work, you build a history—data—of how work gets done in your organization. This data provides the fuel that powers AI, driving predictions and decisions that allow you to make your work more intelligent. AI can be used to augment business rules by finding patterns in your repository of data. This might mean predicting what product offer to make to a particular customer based on their past buying habits. AI identifies patterns that humans may not recognize on their own, so work becomes more predictable and less reactive.

Applying intelligence to boost performance (stage 4)

In the final stage, you attach that intelligence to a feedback loop. Certain processes will begin to self-optimize, applying even more intelligence over time across every process and action within your organization. Tools like process mining can even help to automatically detect remaining bottlenecks in your workflows and make adjustments on the fly. Your self-optimized systems learn from every customer interaction to make the next interactions that much more effective.

Each business goes through the journey to self-optimization at their own pace, and some may be ahead of others. Start by understanding your processes and work through the journey step by step. Every company is different, and so is the journey towards self-optimization. Companies that are successful will recognize that they must adapt to change as it happens.

Deciphering what’s real and what’s hype with AI and automation

In a world where every software company claims to have game-changing generative AI and automation capabilities, achieving self-optimization depends on our ability to understand what’s just hype and what will actually benefit our organizations. Business leaders should focus on the practical application of AI and automation, rather than the shiny new capabilities that may not immediately impact or benefit their processes or employees.

For example, connecting a centralized AI decision engine to customer channels to analyze customer actions and predict their needs can make the customer experience more empathetic, consistent, and accurate. Or applying task mining to identify and fix inefficiencies that were previously unknown can transform back-office operations, saving time on manual work so employees can focus on tasks that truly usher business goals forward. 

Prioritize applying “practical AI,” integrating AI-powered algorithms, models, and tools into existing systems and processes. This approach will provide benefits like reduced costs, automated tasks, improved intelligent data-driven decisions, and better employee and customer experiences. You’ll start to see small wins, like time saved or issues resolved faster – that’s when you know you’re on the way to self-optimization.

Don Schuerman is CTO of Pega.

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Generative AI Insights provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss the challenges and opportunities of generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld’s technically sophisticated audience. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Contact doug_dineley@foundryco.com.