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Camunda CEO Jakob Freund is a process automation expert. Here, he explains how organisations can use AI to improve and streamline automation workflows.
While AI technology has been around for decades, discussions of its promise and potential have reached a fever pitch in 2023. Experts say new generative AI tools alone could add up to $4.4tn of annual economic value. Other studies predict “Industrial Revolution-level” efficiency gains from the proliferation of AI in the workplace.
Now that AI is on everybody’s minds and corporate agenda, how can enterprises ensure that they’re optimising the technology to create true competitive advantage?
One critically important step is to pick AI applications that advance the organisation’s hyperautomation strategy. Hyperautomation is the business-driven, orchestrated use of multiple technologies, tools or platforms to rapidly identify, vet and automate processes.
When used alongside process orchestration, AI has the promise to streamline human workflows, aid developers in modelling processes, and even self-heal processes to ensure optimal performance. AI must work in concert with other tools in the hyperautomation tech stack.
While AI applications come in all shapes and sizes, there are three distinct types that organisations can use to improve automation workflows.
Generative AI tools
The first is the set of tools that are making the most noise in the market today – generative AI apps like ChatGPT. According to Gartner, 70pc of all companies are exploring ways to use generative AI in their operations. This could involve creating any number of applications – everything from financial report summaries to tech support emails.
We’re just starting to scratch the surface on potential use cases. But the technology exerts real promise in automation. Generative AI can be used to augment human workflows to make mundane tasks like form completions and data extraction much faster to execute.
When it comes to designing process models themselves, developers can lean on code generators like GitHub Copilot and others to accelerate the process. These systems can generate new code based on the data on which they’re trained — so it’s possible to analyse an organisation’s existing process models to develop new ones in the future, saving valuable development time.
In the testing phase, organisations can use generative AI to automatically generate testing data to ensure that end-to-end processes are working optimally. AI apps can test complex code paths and spot errors – often more effectively than humans – allowing developers to shift their attention to more challenging tasks.
Image: Jakob Freund
Predictive AI tools
AI’s biggest impact on automation could come in the area of predictive modelling.
Process complexity is one of the top areas of concern for implementing effective automation. According to the 2023 State of Process Orchestration survey, 72pc of IT leaders agree that real-world, mission-critical processes are becoming more complex to maintain. As more tasks become automated to meet customer experience demands, 69pc say it is harder to visualise end-to-end processes.
While predictive AI is nothing new, combining predictive models with process execution data can give teams a better picture of what’s happening within their processes so they can continuously improve them.
Organisations should implement predictive AI to analyse their process models’ performances in the past, and optimise future process models for the best impact. Optimising processes can have major business implications, including reducing costs, improving internal efficiency and driving better customer experiences.
AI can also help with decision modelling. For example, by using historical data, AI tools can help financial services firms predict instances of fraud and trigger certain actions based on fraud patterns that reveal themselves over time.
Augmented intelligence
Augmented intelligence presents an exciting opportunity to improve human productivity. This type of AI can help accelerate decision-making that normally would have been done by a human, thereby accelerating the efficiency of a process. This has the potential to be very significant, since 3bn business decisions are made each year. Research from Bain shows a 95pc correlation between decision effectiveness and financial performance.
In automation, augmented intelligence can be used to build self-healing processes. An augmented intelligence system could take process execution data analysis to the next level by deciding how to make a process more efficient, and then executing on that decision with minimal or no human intervention.
Data from previous models can give developers suggestions based on Business Process Model and Notation (BPMN) best practices. In the future, by applying augmented intelligence, organisations can set up mechanisms to guide users toward preferred decisions or, in some cases, actually make automated decisions.
Orchestrating AI in the larger automation tech stack
AI can take an organisation a long way. But to generate true business value from the use of AI, organisations need to ensure that AI applications are acting in concert with the many ‘endpoints’ that play roles in carrying out business processes. Endpoints can be people, software systems or physical devices. While AI is just one type of software system, organisations will likely apply multiple uses of AI – to perform everything from image analysis to knowledge mining to video analysis to speech transcription to predictive maintenance.
That’s a lot to coordinate. To make sure AI applications are working with all appropriate processes – and not functioning as one-off tasks – organisations need an overarching mechanism to orchestrate processes from end to end.
Process orchestration coordinates the various moving parts of a business process, and sometimes even ties multiple processes together. In this way, it helps organisations to integrate AI with the people, systems and devices they already have – ensuring that it generates the value organisations expect.
By Jakob Freund
Freund is co-founder and CEO of Camunda and is responsible for the company’s vision and strategy. He’s also the driving force behind Camunda’s global growth and takes responsibility for the company culture. He has an MSc in Computer Science and co-authored the book Real-Life BPMN. He is a sought-after speaker at technology and industry events.
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