Start With the Problem, Not the Tool
Every week, a business somewhere rolls out an AI tool and calls it a strategy. A chatbot appears on a website. Copilot gets deployed tenant-wide. A workflow gets "AI-enhanced." And six months later, adoption is flat, staff are frustrated, and leadership is wondering what went wrong.
What went wrong is that nobody asked the right question first.
The right question is not "how do we use AI?" It is "what problem are we actually trying to solve?" Those sound similar. They are not. One starts with a tool and works backward. The other starts with a real business constraint and works toward a solution, which may or may not involve AI at all.
There is some telling data on this. A recent Salesforce survey of more than 1,500 desk workers across 14 countries found that workers in emerging economies - India, Mexico, Saudi Arabia among them - view AI as a tool for upward mobility. A companion KPMG study found that 90% of workers in those markets expect to benefit from AI. They report improved creativity, fairness, and outcomes. Meanwhile, American workers rank among the most skeptical, and they say their AI pilots fail because the tools produce generic, untrustworthy output.
The difference is not the technology. It is the context. Workers who are not handed a mandate are open to the tool. Workers who are told to use it or else are waiting for it to fail. If you want AI to actually take hold in your organization, remove "or else" from the equation. Coercion is not a change management strategy. People engage with tools that solve real problems for them, not tools that were chosen for them before the problem was defined.
That pattern shows up inside organizations too. When AI is introduced as a solution to a specific, understood problem, people engage with it. When it is introduced as a corporate initiative, people route around it.
When a client comes to ForgeNorth and says they want to explore AI, the first conversation is never about features or licensing. It is about operations. Where are your people losing time? Where does information get stuck? What decisions take longer than they should, and why? The answers to those questions define the scope of any solution worth building.
AI is genuinely useful, in the right context, applied to a well-defined problem, with the right data and process foundations underneath it. Without those conditions, it adds complexity without adding value. It creates something for employees to work around rather than with.
Good technology decisions follow the same logic they always have: understand the problem, define what success looks like, then choose the tools that can actually get you there. AI is not exempt from that process.
References:
KPMG, Trust in artificial intelligence, https://kpmg.com/au/en/insights/artificial-intelligence-ai/trust-in-ai-global-insights-2025.html
Salesforce, U.S. Workers Are More Wary of AI Than Their Global Peers. Here's How Leaders Can Help, https://www.salesforce.com/news/stories/what-leaders-should-do-about-american-workers-ai-skepticism/
McKinsey & Company, Superagency in the workplace: Empowering people to unlock AI's full potential, https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work
AMS, Consequences of mandated usage of innovations in organizations: developing an innovation decision model of symbolic and forced adoption, ttps://link.springer.com/article/10.1007/s13162-020-00164-x
