Stop Limiting the Power of your AI

In today's rapidly evolving digital landscape, the power of Artificial Intelligence (AI) is undeniable. However, many organizations may be inadvertently limiting the potential of their AI solutions by not providing the necessary context. I think of this situation as, "Without providing context to your AI, it’s like using a paper map to get somewhere when you could be using a modern GPS system.”?

For those of us who were driving a couple of decades ago, remember navigating with a paper map? Pulling over to figure out where to turn next? And then after that, years of navigating with turn-by-turn directions printed out from the internet. For those who were not eligible to drive at the time, maybe you had to rely on a paper map in a remote location with no GPS or phone signals. The map, though a useful tool to carry, offers no awareness of your location, your destination, or any traffic conditions. In contrast, a modern GPS system knows where you are, where you want to go, and can make real-time adjustments based on traffic and road closures.??

Similarly, AI without context lacks the necessary information to be truly effective. It doesn’t understand your data, isn’t aware of your goals and priorities, or your business strategy. It won’t make great recommendations with such limited context. On the other hand, AI with context understands your data better, uses your business priorities, and makes recommendations based on your business rules and workflows.?

So how do we enhance the power of AI at our disposal? Here are some best practices based on implementation of several AI initiatives in my organization and from my interactions with customers.?

1. Knowledge Management: Your AI tools are only as valuable as the data you feed them. Unprocessed, unorganized data is like an out-of-date GPS that doesn’t have refreshed maps – it can be misleading and even harmful. By providing enriched and updated data, you can ensure that your AI tools are making useful and trustworthy recommendations. Some ways in which you can enrich data are: tagging it per categories that are relevant to your business, ensuring that it is formatted and stored in a way that is readable by your AI tools, and having systems in place to audit your data regularly for staleness. Many file storage and content management systems like SharePoint have excellent guidance on how to best do this. The best practice is prioritizing teams to follow the guidance and having some lightweight governance system in place to make sure!??

Let’s look at an example of someone who works in customer service and is trying to find a pattern of top customer issues in their region. Here’s how responses may look like at the two extremes of AI: without strong knowledge management context and with context.?

2. Rules Engine: Aligning strategy across teams is critical. Just as a GPS with trip preferences prioritizes the fastest route or avoids toll roads based on your preferences, a well-developed rules engine allows your AI to reason with your data and apply your business strategy and priorities. Going back to the GPS analogy, if you don’t take a few minutes to optimize settings, you won’t get the full personalized benefit of the tool’s capabilities. You might find yourself driving through a route full of tolls you didn’t expect, or stuck in a frustrating ferry wait line when you would have preferred a different option.??

Let’s take an example of a tool that helps engineers prioritize the bugs they need to address. If the tool does not have any rules available to filter the bugs (by the engineer’s skillsets and by the most critical business areas, it will provide generic guidance vs taking the engineer directly to the most critical bugs he or she has the capability and business need to address.??

3. Digital Culture: Last, but not least, support AI like you would any new major initiative: by working closely with your talented people to lead the way, and supporting them through the change. By viewing AI as a major cultural change, you can shift our approach from seeing AI as a mere tool to viewing it as an integral part of your workforce, which requires investment in its development. Build on the momentum and energy of people who are already experimenting with AI tools and provide space for experimentation. On the flip side, you may notice that certain types of scenarios might be extremely promising to reduce duplication and improve business processes but face some resistance or fear in the organization. A cultural shift this may demand is for employees to see AI as a collaborator and tool for career growth rather than competition.??

Consider a broad range of workflows across teams, roles, and business use cases. Make sure your organization’s strategy is not limited to productivity and efficiency use cases but you are also looking at revenue generation use cases. Another key element of cultural change is the prioritization of secure and responsible AI usage. While you are at it, make the learning process engaging and generate a culture of fun-filled curiosity. This era will be led by people who figure out the most valuable upsides of this new technology. Be one of those leaders!?

I invite you to share your experiences and insights on AI implementation in the comments section. Let's learn from each other and push the boundaries of what's possible with AI and drive innovation forward.?