AI applications range from simple rule-based systems to self-learning models, to complex multi-agent systems that autonomously reason and execute tasks. And the cool AI technology evolves practically every day.
But what can AI really do to solve real business problems? It turns out, not much without human involvement.
Real business value generation begins by defining a pragmatic approach and execution plan. Both require real human knowhow and ingenuity.
Determining where to start
From a preparation perspective, start with an audit of your critical business processes:
Based on the results of the four steps above, pick the top three processes to start your journey. They should have the following attributes:
The last bullet is key. Too much system and data work is like starting your journey in a tar pit. Don’t underestimate the effort to truly assess technical and data feasibility!
Now, choose one of the top three processes to start. Put the others into a simple roadmap that you can use to share your vision and help build momentum.
Position the first process automation effort as a pilot. Build something, then test it in a controlled environment with a small AI-friendly process owner and group of process doers.
Don’t try to alter a real core process with any level of automation until you, the process owner and doers, feel comfortable that the pilot is an improvement, preferably one that is quantitatively and qualitatively better.
A huge part of AI success is internal marketing and change management. It's critical to partner with real people that do the work today. They should be your biggest advocates, not rejectionists.
Sounds easy, right?
Actually, the approach you take is one of the hardest things to get right. If you pick a process that doesn’t truly generate meaningful value for your business, then there will be no return on the investment and your journey will likely end abruptly (and likely not start again for years). If you choose a complex process with poor system or data support, then your AI journey will lose steam because it will take too long or require too many resources to demonstrate value. And if you pick a process where everyone involved today is fearful of AI, then you’ll be pushing a boulder uphill that will likely roll you over just when you think you’ve completed the hard part.
For a great example of a core process AI automation effort, read AI Return on Investment. This article I wrote talks about the practical application of AI to the AP collections process in a large business services company.
Ideally you select a process that you can automate in 90 days or less. And, once you can demonstrate value with process cheerleaders to boot, it becomes much easier to continue improvements on your first process or move on to others in your roadmap.
So, choose wisely!
Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation.
Contact us if you have a problem to solve, a process to refine, or a question to ask.
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