5 Brutal Truths No One Admits About Why Most ‘AI in BI’ Projects Fail Before They Even Start

Read time: 2.5 minutes

What Breaks AI In BI (And How To Fix It)

1. Over-engineering delays true value.

• Using complex pipes and frameworks does not guarantee usage by users or businesses.

• Systems designed without decision-making impact are most likely to go unused by end users or others involved in business decision-making processes.

Fixes:

• Build a thin end-to-end flow and ship it first.

• Make sure it connects AI to structured output to BI to decision.

2. Under-engineering destroys trust.

• Due to the lack of structured data, rapid result production can undermine structure and yield inconsistent or low-quality, unvalidated results.

• A missing validation schema requires rework on outputs.

Fixes:

• Establish a minimum viable structured workflow.

• Include a schema, validation, and store output from day 1.

3. AI and BI mixed causes both to fail.

• By directly embedding AI into BI dashboards, performance is significantly slowed, and the lack of defined logic may leave users without a clear understanding of the overall system.

Fixes:

• Separating the roles:
   — AI is responsible for computation and enrichment.
   — BI is responsible for providing visualization and establishing decision-making criteria.

4. Successful AI models will not have a business impact.

• Working models do not necessarily affect the user's workflow in the context of the business.

• If no action is taken on the model's output, it has no business value.

Fixes:

• Everything in relation to:
   — One workflow
   — One decision
   — One measurable KPI

5. Trust is a foundational aspect; it's not optional.

• Users will reject systems in which they do not understand how to use.

• Once trust is lost, it is rarely regained.

Fixes:

• Include lineage, confidence scores, and an explanation of how things work to the user.

• Be transparent with users regarding the output from the beginning.

💡Key Takeaway: 

Artificial Intelligence does not fall short in implementing business intelligence due to difficulty… rather, it falls short because it is disassociated from decision-making and lacks trust.