Where will the company's growth come from in the period ahead? Customer intimacy, exports, operational resilience, new products, better decisions, or institutional memory? Clarifying this strategic need before choosing AI licenses establishes what purpose the investment will serve.
The next step is to describe the expected outcome as a testable value hypothesis. Which capacity will AI develop, which decision will it improve, or what new revenue will it make possible? In this assessment, I look beyond time savings to quality, risk, customer impact, employee development, and scalability.
The company's readiness is equally important. Alongside technology, we need to examine data, processes, governance, leadership commitment, and human capabilities. If the team cannot frame the problem or evaluate the output, part of the investment should first go toward developing those skills. Assessing use cases in terms of value, capabilities, data, risk, and scalability indicates where to begin.
Treating investments as a portfolio allows a company to learn as it goes without committing everything to one big bet. Short-term improvements can build confidence. Redesigning work from end to end creates room for larger gains, while experiments with future products and services offer new growth options. As we focus on today's efficiency, we need to make room for these options too.
The portfolio can balance improvement, redesign, and new areas of growth. Decisions to continue or expand an investment should rest on the evidence gathered.
Ask yourself
- Which strategic need does this investment address?
- What measures of value, beyond time, will we use to assess success?
- Do we have the human and management capacity to sustain this investment?
