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Like every business, we have the need to continually invest in our business processes in order to scale, to grow efficiently, and to allow our people to be as productive as possible, which means enabling them to focus on their highest value work. From processes that are time consuming to mismanagement of resources, breakdowns in the system always arise. How businesses address them is the differentiator. Each time I’m faced with an optimization problem, I ask myself the same question I presume most CIOs do: can we automate and digitize?
Now, that same question has a newer subtext: can we automate/digitize it with artificial intelligence? For a long time, the promise of AI has been in the future and not the present. But over the last year or so that has changed. The sophistication of algorithms – resulting from the massive quantity of ingested unstructured content – has made it possible for machine learning (ML) engines to provide automation through digital labor and to give instant, quantifiable value back to business processes.
When answering the question “can I digitize it, and should I consider AI” there are four key areas I consider:
- First, I need to know what the problem is by reviewing the data.
- Second, I have to consider whether it’s something that is repeatable and can therefore be automated.
- Third, I need to break down the issue to understand the extent of the problem, including all its facets.
- And lastly, do I have a modern IT architecture that allows for easy integration of ML services into process flows?
Finding diamonds in the data
As CIOs, we are tasked with finding opportunities to modernize and optimize our business processes through enabling technologies. With the reduced cost of storing information in the cloud, it’s now easier than ever to track and store almost any data point. I’m not advocating collecting data aimlessly but rather, the more data you have, the easier it is to identify patterns and issues where an AI solution could be useful.
Revealing an inefficiency ripe for automation
One area we took this approach was with how to enable a modern business process for Contract Lifecycle Management (CLM). When we discovered that the CLM process at Box was inefficient and struggling to scale under growing volumes, we tasked ourselves to think differently and we put digitization as a guiding principle to solution.
Breaking it down
We started by breaking down the end-to-end CLM process into its building blocks and found that – from the moment of receiving a contract to storing, reviewing, marking up, sharing, deliberating and finalizing – that there was far too much time wasted. Too much time elapsed between start and finish by mundane tasks that pulled talented people away from higher value work.
Rather than solving for the entire CLM process at once, we focused on key pain points and solving each with best of breed services. Because Box has built a modern reference architecture, we were well positioned to do this. This incremental and agile approach not only started to deliver value more quickly but also reduced the change management efforts as they were in smaller digestible steps that were spread out over time.
Best of breed foundation
The foundation for our process architecture is Box content management and workflow augmented with services that we were already using such as mxHero for email attachment intelligence and DocuSign for e-signatures, O365 for contract editing and negotiation, Crooze for reporting, analysis and obligations management, and Ephesoft for meta data intelligence extraction and tagging. Ephesoft also leverages AI and ML to add intelligence to unstructured data and seamlessly ties into the new Box Skills machine learning framework. For initial buy-side contract reviews, we are also exploring using other AI/ML based services like Lawgeex.
The outcome: maximum productivity
This is just one example of how we’re constantly touching base with our teams and reviewing process data in order to identify opportunities for automation and digitization. Our goal is to take this framework and apply it across every business process and work stream, ultimately empowering our employees to reach maximum productivity and do their highest value work.
While our approach may add a certain level of complexity due to the multiple relationships that need to be managed, ultimately it gives us more flexibility and agility in our architecture and processes. It enables us to easily switch out services if something new and/or better were to emerge and to continuously leverage the ecosystem of innovation that our partners provide. We consider this to be the North Star for rapid continuous improvement and digitization.
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