One of the prime reasons for the hype around AI in recent years has been that organisations have been obsessed with buying the highly advertised ‘AI label’. In response to that obsession, vendors slapped the AI label on everything (regardless of whether it actually was AI). This led to the unrealistic expectations amongst those who purchased the vendors’ products that magic-like results would ensue as soon as they deployed the product.
Was there over-hype, over-marketing, and over-promise around AI on the part of some vendors? Undoubtedly. Did this feed into unrealistic expectations on the part of customers about what AI could achieve, and how quickly it could achieve it? Quite possibly.
For those who may be questioning the value of this technology, hold off on writing any obituaries for AI just yet. Instead, it’s time for a reset. Amidst the AI hype, a dash of realism is called for.
AI might not be some magical force that can solve a myriad of problems as soon as you take it out of the box, like a genie granting wishes. However, it is a hugely useful technology that is fully capable of tackling a number of business use cases, particularly when it is approached as a ‘long game’ rather than a quick fix.
Make no mistake, the long game is the most valuable one to play when it comes to AI. While there are certainly some out-of-the-box point solutions that provide a quick-start and a way to get familiar with AI and tackle specific tasks, AI really starts to flex its muscle – and enable true organisational change – when the knowledge gained on an initial AI project is built upon and refined for subsequent projects. With each project, firms can use the models they’ve developed and honed to tackle increasingly sophisticated business problems within the organisation in new and innovative ways.
Putting AI to Work
So, what are some of these business use cases where AI is already delivering value? Try contract intelligence – an area that has only increased in importance in the age of COVID-19 and its ensuing economic fallout.
Contract intelligence is all about using AI to efficiently extract key pieces of information like clauses out of hundreds or even thousands of documents, helping organisations better understand the potential risks or opportunities that lie within their contract estate. As the COVID-19 pandemic swept across the globe, it was more critical than ever for organisations to figure out – for example – where they did or didn’t have force majeure clauses, while gathering details around termination clauses, change of payment terms, and other crucial aspects of their contractual agreements.
Knowledge management is another key area where AI is delivering real value. It’s an old business problem, of course: figuring out where knowledge exists within the organisation and then making it easy for people to find and tap into that knowledge. What’s new is that AI allows this task to be achieved much more efficiently and productively.
Many of these knowledge projects have been attempted in the past but in a very ‘human-like’ manner that required an inordinately large number of people. This means that there were certain projects that weren’t feasible because of the large number of human hours required. These same projects are now becoming possible because AI enables users to quickly search and find more data and automate key parts of the curation and knowledge-finding that powers knowledge management. Also, the technology can make intelligent connections across organisations and connect people to data and projects.
The success of AI in these areas necessitates briefly mentioning some key barriers that can prevent organisations from fully deriving the benefits of this technology. One of those barriers is simply not taking the time to understand the problem they’re trying to solve. What is the value of that problem – and is it worth deploying AI against it, or is it better tackled by other approaches (or by a combination of AI and other approaches)?
Once this has been determined, many organisations stumble because they don’t have the people and processes in place to support their AI initiative. The lesson? Change management matters – AI doesn’t occur in a vacuum. A realistic approach to AI requires a comprehensive plan that encompasses people, processes, and technology equally.
What is ‘Success’?
Organisations expecting to immediately double their revenues or identify 100% more opportunities overnight should temper their expectations when they’re determining how to measure the success of AI in their business.
For example, using AI to identify 4% revenue leakage across contracts might not sound like that impressive of a result for an organisation that’s gone through the trouble of implementing AI to tackle that specific business problem. But if it’s a large multinational organisation, that 4% number might represent millions of pounds of savings every year, making the implementation – by any realistic assessment – a resounding success.
Meanwhile, the increasing democratisation of AI stands as a key success metric for this technology on a global level. Once a luxury that only the larger, multi-region firms could afford, today AI is being adopted by organisations of all sizes, including the smaller businesses that just a couple of years ago, simply couldn’t make a business case for investment in this capability.
Perhaps more than anything else, this democratisation cuts through the hype and shows that AI, when approached in a realistic manner, is a technology that is only poised to continue growing in importance for the wide array of organisations who are turning to it.
About the author:
Alex Smith, Global Product Management Lead for iManage RAVN, has over 20 years of experience in product management and service design, including new and emerging technologies such as artificial intelligence, semantic search and linked data, as well as content management. Prior to iManage RAVN, Alex has held positions at Reed Smith LLP and LexisNexis UK.
