Why Before How: Adopting AI with Intention and Clarity
The world is consumed right now with the question: "How can we incorporate AI into our work?" Across nearly every sector, leaders are rushing to embrace the technology without first addressing why.
This mentality presupposes that AI has an intrinsic value, that its inclusion on a spec sheet, in an RFP, or in marketing will guarantee success. Organizations are deciding that AI is the solution they want to offer, then asking their teams to find a problem that can be solved with it. What they should be asking instead is for teams to identify the highest priority problem for their business or mission, then deliver the best possible solution.
Early in my product career, I was coached to fixate on the "why" behind any decision, not just at the outset but through every stage of ideation, delivery, and evaluation. Good product decisions have compelling, considered answers to "why" questions. Why is this the most pressing problem to solve for our users? Why have we decided to solve it in this manner?
Over the last year, I faced mounting pressure as a Senior Product Manager to find some way of incorporating AI into our portfolio. If anyone on my team asked for the "why," which I encouraged them to do at every turn, the most honest answers would have been: "Because leadership says so." We were being asked to work backwards from the preferred solution, searching for justification instead of leading with it.
While keeping up with trends may feel important to the bottom line in the short term, there is a real long-term risk to businesses when they pivot their development away from solving validated problems for their users, drawing on all available tools to build solutions, and toward a product that prioritizes a desired solution over all other factors.
I have seen this same struggle in the international development space. Scholars and experts predominantly based in high-income countries debate and decide on solutions for global challenges, funding becomes tied to those specific solutions, and local governments face the option of accepting a pre-determined program design or declining investment altogether.
When I worked as an Advisor to the Ministry of Education in Liberia, Randomized Control Trials (RCTs) were lauded as the "gold standard" of evaluation. There are good reasons to pursue this level of rigor, and for some interventions an RCT makes sense. However, when donors required a project to be evaluated with an RCT as a condition of funding, the Ministry and its partners found themselves planning an education intervention based on the desired evaluation protocol, rather than the desired impact. It should not have been surprising when those RCT results showed insignificant or inconclusive results.
In the last few months, I've noticed that most job postings, RFPs, or new initiatives in the Tech for Good space have pivoted from a broad embrace of "Tech" to specifying "AI." In my opinion, it is a mistake for any sector to fixate on a single tool as the solution. Both corporations and social organizations benefit from staff having a well-rounded understanding of the technologies available, and the trust to make strategic decisions for the problems they've been tasked to solve.
As the adage goes: "When you have a hammer, everything looks like a nail." AI is a powerful tool that will unlock new efficiencies, progress, and possibilities. For some previously intractable problems, it will be the solution we've been waiting for. But it will not be the best possible solution for everything.
If you are feeling stuck in a mad scramble to fit AI into your projects for its own sake, my advice is to:
Know your why - Always center the problem and the desired outcome over any particular solution. Ground your justifications in data and the human stories that drive your mission.
Know your stuff - Learn and upskill in AI so that you can identify with confidence when it's the right solution, and when it's not. Keep up to speed on other technologies as well.
Know your strengths - Rather than chase trends, evaluate your niche and the specialized expertise you've earned. Let AI be one more instrument in your toolkit, rather than an end goal.
Strong leaders during this time of dramatic technological change will prioritize strategic, thoughtful, and intentional applications of AI. They will ask "why" first, and answer "how" using their full arsenal of expertise, ability, and technical tools.
