How Artificial Intelligence will continue the evolution of business processes
By Chris Ellis, Director Pre-Sales at Nintex
Rapid advances in artificial intelligence (AI) during the past 12 months have caused many people to wonder whether the pace of development can be sustained.
Generative AI models such as ChatGPT have rocketed from virtual obscurity into mainstream use. Massive investments by companies such as Microsoft and Google have led to the tools continuing to evolve at breakneck speed.
Attention is now focusing on whether this frantic pace of evolution is likely to continue or become more gradual. Some argue that, following this rapid spurt, the ongoing evolution of AI will follow a more gradual path. Even if that happens, the benefits for business will continue to grow.
The theory is that the compute structures that underpin AI will become larger and more competent over time. As this happens, the tools will be able to augment or complete an ever-increasing number of processes and roles.
The evolution of ‘intelligence’ in business
To fully understand what generative AI can deliver, it is worth taking a broader view of the evolution of ‘intelligence’ generally in business, and particularly in its relation to processes. The evolution of capabilities in this space has occurred over a period of years rather than months.
Intelligence was added to process automation in a significant way about six years ago. In this context, intelligence took the form of rules-based workflows that were applied to automation.
Business rules logic was used to handle repetitive tasks and deal with exceptions. It was also used for simple decision-making that, up until that point, had been performed by people.
As an example, when an internal business unit received a request, either from a different business team or perhaps an external customer, intelligent process automation became the first point of call. It determined whether the request met conditions that required it to be routed via an executive for approval, or whether it was simple enough to be approved on-the-spot.
Both paths ran automatically. This was intelligent for the time period, and take-up and adoption rates reflected the efficacy of the approach, as well as the general enthusiasm in business for the adoption of such tools.
Since that time, gradualism has redefined what businesses expect from the application of intelligence to their processes. When business leaders talk about intelligent process automation today, they more than likely expect some form of AI capability to be providing the intelligence component.
As this occurs, what is actually possible is evolving and this is where attention is currently focused. Businesses of all sizes are seeking to understand what step change in capability AI-powered process automation will produce and where application of this evolution of the technology will add the most value.
One element missing from earlier iterations of intelligent process automation was the ability of the algorithm or engine to ‘learn’ from every task it performed, and to iteratively improve its performance based on those learnings.
By leveraging such historical knowledge that’s been built up over time as a training dataset, and taking into account each additional job it handles in real-time, AI-augmented intelligent process automation tools can create a path to process improvement that has what a lot of people would understand today as ‘intelligence’.
Such learned intelligence can be applied to processes so that they are always in the best possible state and can also improve further thanks to an ongoing pipeline of learnings. This will naturally lead to better automation and the emergence of adaptive workflows.
With these continuously learning processes, business units that rely on them will become more efficient, and decision-making will be more transparent, documentable, and auditable.
The question that remains is over what period this change will occur. The process of gradual evolution is driving experimentation and improvement of the underlying technology, but businesses are still in a relative stage of infancy in terms of understanding what this will mean for overall operations.
Currently, AI-powered tools are mostly being used to finesse existing processes and automations. This makes sense as these are the processes that are perhaps the best understood internally, that have been mapped to some extent, and have the highest rates of usage.
The business units and domain experts that live and breathe the processes know how they run, and their current challenges and pitfalls. They can carefully monitor how AI tools tackle the processes and the outputs they create.
The high level of internal knowledge about these processes marks them as strong candidates for additional improvement. This is because the business has a baseline for how they perform today, and so can better understand whether AI augmentation will create an incremental improvement or a step change.
Once the business benefits of AI-augmented intelligent process automation are better understood, application of the technology will naturally shift to more ‘greenfield’ processes. Over time, this will add even more business value through efficiency improvements.
The use of AI in business is still very much in its infancy. With ongoing evolution set to continue, the way in which it will add value in coming years will be immense.