The sophistication of your AI strategy is irrelevant. It can have more bells and whistles than a train station at rush hour, but it won’t help save you money, enhance your offering or deliver the efficiencies you need just by being complex and convoluted.
Successful legal AI strategies are not about the complexity of the algorithm or the quality of the data – although it’s right to think that these things are vital; it’s about the outcome that AI can deliver. And most critically – the quality of the outcome. By starting here and working backward, you stand a greater chance of achieving cost savings, efficiencies, or myriad other goals you seek.
There is a huge difference between an outcome-driven approach and the quality of that outcome. Most projects have a set outcome to achieve and are measured based on whether that target is hit. The challenge is that business conditions change and change rapidly.
Your original outcome target may not be as relevant in the changing world, whether that’s market conditions, socio-economic factors, or something closer to home such as a change in corporate strategy. Ultimately, it’s the quality of the outcome, not the outcome itself that should determine the success of the project.
For example, if you are looking for AI to help provide a process improvement or a change in the way you do your contracting, the outcome shouldn’t be confined to just providing improvements within legal. It should look at where the business is developing and what the wider outcomes could be. For instance, how could better contracts improve frictionless business? How could it accelerate sales? How could R&D be more involved? For AI to be truly successful and demonstrate a healthy return on your investment, it’s not just about hitting a milestone at the end of a project and being able to tick that box.
Consider this scenario; when the pandemic hit, companies across the world needed accurate and fast analysis of which agreements had hardship clauses or force majeure provisions. So, they did the analysis and reached an outcome; an understanding of which of those clauses are relevant in their supply chain, given the current situation.
However, a wider analysis would drive a higher-quality outcome for the business. For example, analyzing those clauses historically and finding out how many of these agreements were drafted by the same person. Also are they continuous? Are they consistent? What do the payment provisions look like?
Broadening the thinking, and widening the analysis drives greater value than just knowing which agreements are subject to termination because of a particular clause that that can be invoked in the current time of crisis. Instead, a pattern could emerge within the contracts/clauses that will help drive future supply chain decisions and have a financial impact across the business.
One crucial aspect of developing a value-based outcome with an AI strategy is getting the right stakeholders on board. A strategy is much wider than just the project sponsor, so working on outcomes together from the outset will help drive those more valuable cross-organizational outcomes.
Not only will this automatically help to break down silos, but it will give you more data that the AI can use to give a value-based outcome. This takes some getting used to – inter-departmental projects are often tortuous in their laborious nature and fraught with internal politics. But increasingly companies are seeing the value of uniting their data into a data lake – particularly legal with finance – and pushing forward for integrated projects with great success.
Another sticking point has been the distribution of data sitting across 10 or 20 different subsystems spanning different countries. But the latest technology has alleviated that concern; now there are commonly approved secure methods of getting the information into one place. APIs – computing ‘connectors’ that allow data to travel securely over the globe – are now sophisticated and fast enough to pull the data from almost any source, ready for analysis.
It’s become increasingly popular, once this data is pooled inside a data lake and analyzed, to then use a visualization tool to ensure stakeholders and c-suite sponsors fully understand the value-based outcome the strategy is delivering – ongoing.
This could be as simple as a dashboard showing where the firm is leaking revenue, or how many agreements are with a particular supplier or service, or even what the likelihood is of litigation based on historic data. Presented in a consumable way, stakeholders will see the benefit of the outcome – not the complexity or sophistication of the AI.
AI is already using traditional legal data in conjunction with other departments to drive value across the business. Developing your strategy further with the right process and support will result in a value-based outcome that the whole organization will appreciate. Contact us to learn more about our AI Powered Contract Solutions.