Don’t Use AI When BI Will Suffice!

Scaling AI Claire Carroll

Artificial Intelligence (AI) offers organizations immense power, from predictions to recommendations to smarter decision making - but organizational resistance to this shift is a huge problem. When data-driven engines present findings that differ from executives’ intuition, nine out of ten executives ask for more data instead of trusting the engine.

Implementing AI isn’t going to solve all your business problems, but if you don’t take advantage of the technology, you risk being left behind by competitors. To help, we commissioned a report from Ovum - an independent analyst and consultancy firm - about cross-industry best practices for AI integration and monetization. The report features findings from interviews with a select sample of Global 2000 organizations with the most experience with AI projects in production.


Align Your Goals

Before even asking if you need AI for a project, you need to ensure that you are asking the right questions. Instead of a completely top-down approach, business leaders should approach their data teams with problems, rather than suggesting solutions. This way, the data experts can recommend the best solutions to pain points rather than spending time on data projects that don’t align with your most pressing business needs.

goal setting

When BI Might Be Enough...

Business Intelligence (BI) is often loaded with problems. Humans make decisions based on false information, emotional bias, prejudice, or inappropriate transference (where you imagine that a new situation can leverage the same skills as a different domain). Even when you don’t think you are influenced by the guy that cut you in line at Starbucks this morning, it might be heightening your aggression.

But at the same time, don’t discard business intelligence as inherently foolish. You’ve gained experience in your industry and have robust decision-making skills. BI is great for establishing reads on people and timing. And better than any digital model, you understand the big picture.

Since AI models are built (or should be built) to target specific pain points or goals, they are rarely holistic enough to understand and recognize your organization’s vision and dreams for the future. While AI can predict industry trends to inform your decisions, creative thinking is needed to establish the best path forward.

And when You Need AI

Ovum surveyed top executives in diverse industries, from Aerospace to Media, to establish the best metrics to understand when AI is actually critical to a project:

  • Complexity and Scale - AI is meant to help solve problems too complicated for human capacities, so when a project depends on too many moving parts or is too large, let AI do the analysis, and focus your team’s energy on finding new relevant data or evaluating next steps based on the model results.
  • Prescriptive Solutions Needed - When you need to move beyond predictive analytics and need a true recommendation system that weighs your business needs against model outcomes, you need AI. Whether you actually trust and leverage the recommendations or not will depend on your organization’s flexibility.
  • High Stakes and Strategic Need - When there’s no room for error and your competitors are closing in, AI can make the difference between pulling ahead and losing revenue. AI models aren’t always perfect and require good data input and conscious revision, but if you can establish robust models, you’ll be able to get proactive insights quickly and efficiently.
  • Low Hanging Fruit - We’ve established that AI isn’t right for every project, but there are projects that benefit from AI. With this in mind, you should get proactive about AI integration. Attempting to create a “low hanging fruit” initial use case is a great way to see if your organization has the capabilities and skills to leverage AI and how it responds to the results. The project isn’t a waste, and shouldn’t be inconsequential. But if you can simplify a common process, you’ll save time, money, and hassle, while learning about AI. This way, you can pre-empt urgent hiring and organizational needs so that when you do have a high-stake strategic project that requires AI, you’ll know what to do.

artificial intelligence