Start with the Problem, Not the Tools: Why AI Projects Stall

Featuring

Watch Now to Learn:

  • The starting mistake – Adoption fails when a company buys a capability and then looks for somewhere to apply it. Evan opens every engagement by asking where people are struggling, and the tool decision comes after that answer.
  • The readiness sequence –  Data that agrees with itself across systems, a documented process, a team fluent enough to spot opportunities, and a written AI policy. Evan asks for the policy first, and in his experience it does not exist about half the time.
  • The adoption multiplier – A 2025 study by Protiviti and the London School of Economics found employees without AI training save about five hours a week, while trained employees save eleven! Access to the tool is not the variable that moves, proper training is the catalyst.

Speakers:

  • Evan ConroySVA Consulting
    Evan Conroy
    Principal
    SVA Consulting
  • Ganesh GandhieswaranConverSight
    Ganesh Gandhieswaran
    CEO and Co-Founder
    ConverSight

Plenty of companies have bought AI and still can’t say what it changed. Evan Conroy, Principal at SVA Consulting, meets those leaders every quarter in Madison and Milwaukee, in rooms full of people who think AI matters and have never once watched a peer make it work. Ganesh Gandhieswaran has spent nearly a decade building the platform layer that turns a good idea about AI into something a business can run every day.

In this episode of the Data Insights Podcast, Ganesh, CEO and Co-Founder of ConverSight, sits down with Evan. They skip the question of which AI tool to buy and work backward to the two steps that come first: naming the problem, then checking whether the business is in any shape to hand it over.

Evan describes what he finds when he walks into a company that already bought AI and can’t say what changed. Ganesh explains what has to be true, structurally, before an agent can be trusted to act.

💬 Why Listen?
If your company has AI tools in place and nobody can point to what they changed, this episode explains why, and lays out the order that works: data, then process, then people, then policy. It also covers what happens after rollout, when usage data by department shows whether a team dislikes AI or whether their manager quietly told them not to touch it.

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