We have a list of five different AI projects our team wants to build, but we have limited capital. How do we filter these ideas to ensure we are not paying a dumb tax on cool but useless technology?

Question

Tyler Smith · Accepted Answer

It's easy for a leadership team to get excited about multiple AI projects. However, pursuing the wrong ones will result in a massive "dumb tax" of wasted time and capital. To filter your AI ideas and identify which ones will actually build enterprise value, you must establish a rigorous evaluation process.

Evaluate AI Projects Against Strategic Goals

Start by framing high-value questions during your Thinking Time, such as: "How might we use AI in this specific seat to reduce delivery time by fifty percent without losing customer trust?" This helps to move beyond simply "cool" technology to practical applications.

Evaluate each proposed project against your V/TO (Vision/Traction Organizer) to ensure it directly supports your long-term goals and [rewriting your 3-Year Picture](/qa/customer-expectations-shifting-ai-vto) to adapt to market shifts.

You should also assess:

• The expected return on investment.
• How the project will impact your Scorecard metrics. For instance, AI can help in [optimizing EOS Scorecard metrics](/qa/ai-in-optimizing-eos-scorecard-metrics-and-accountability) and even [turn those metrics into predictive, proactive tasks](/qa/turn-scorecard-metrics-proactive-ai).

If an AI project does not directly:

• Improve your profit margins.
• Solve a major bottleneck.
• Enhance your 3 Uniques.

Then it's best to table it. By staying disciplined and only greenlighting projects that solve real business problems, you protect your cash flow and build a streamlined, highly valuable operation. This approach also helps avoid the [invisible flow cost by delaying AI transition](/qa/real-options-flow-cost-ai-delay) without falling for every new tool.

Related questions

• [How do we use disciplined Thinking Time to make smarter technology bets?](/qa/thinking-time-ai-software-fatigue)
• [How do we rewrite our 3-Year Picture and core processes to survive customer expectation shifts without burning out our people?](/qa/customer-expectations-shifting-ai-vto)
• [What is the best way to leverage AI to optimize EOS Scorecard metrics and improve accountability?](/qa/ai-in-optimizing-eos-scorecard-metrics-and-accountability)
• [Our EOS Scorecard is great at tracking lagging numbers, but how can we use AI to turn those metrics into predictive, proactive tasks for our team?](/qa/turn-scorecard-metrics-proactive-ai)
• [Is it safer to wait for AI technology to mature before we invest, or are we paying an invisible flow cost by delaying our transition?](/qa/real-options-flow-cost-ai-delay)