Why AI? Eleven reasons it’s yours to understand
In January 2024, Terry Sterling of the Balanced Scorecard Institute listed eleven reasons a leader needs to understand AI. Eleven is a long list to hold in a meeting. Grouped, it is four, and each of the four maps to a question you already ask about anything you manage.
Eleven reasons, or four questions you already ask
Terry Sterling, a Senior Associate at the Balanced Scorecard Institute, called AI literacy “a strategic imperative for survival and growth” for leaders who already track KPIs, OKRs and strategy maps. Two years on, the claim reads less like a prediction and more like a description of where most boardrooms already sit.
His list runs to eleven items, which is too many to hold in a single meeting. Grouped, it collapses to four, and each of the four is a question you already ask about anything you manage: does it change your market, does it change how you run the business, does it change who you can trust and hire, and does it change what you are legally and environmentally on the hook for.
What follows takes each cluster in turn, then ends with the part the original list does not attempt: which of the eleven deserves a small business's attention this quarter, and which can wait.
Does it change your market?
Three of the eleven reasons sit here: adaptation to change, competitive advantage and innovation. All three answer the same question. Is the ground under your market moving faster than your read on it?
Adaptation to change
AI reads shifts in demand and behavior faster than a quarterly review does. Missing the shift is the actual risk, not the technology.
Competitive advantage
Early, sensible adopters cut cost and lift efficiency. The edge is temporary, so it rewards the leader who moves first, not the one who moves best.
Innovation
AI now supports forecasting and product development directly, pushing informed teams toward the front of their industry.
For a twenty-person Dubai retailer, none of this looks like a forecasting model. It looks like noticing, three weeks before a competitor does, that customers keep asking staff for something the shop does not stock, because someone finally read the pattern sitting in the till data instead of guessing from memory.
Does it change how you run the business?
Three more reasons: data-driven decisions, resource optimization and customer experience. These are the ones with the fastest payback, because they touch operations you are already running, not markets you have yet to enter.
Data-driven decisions
AI processes far more of your own data than a person reasonably can, turning a gut call into a checkable one.
Resource optimization
Better allocation of staff, stock and budget follows directly from seeing the pattern in how they were used last quarter.
Customer experience
Chatbots and recommendation tools personalize engagement at a scale no headcount you could afford would match.
This cluster is where most of this guide's own client work lives: a bank reconciliation that used to take a day, a supplier statement that used to be retyped by hand. None of it is glamorous. All of it pays back inside a quarter.
Does it change who you can trust and hire?
Ethical considerations, talent, and risk management. All three are about the same underlying asset: whether people, customers, employees and regulators, continue to trust how you use the tool.
Ethical considerations
Algorithmic bias and data privacy are not abstractions once a model is making a real decision about a real customer.
Talent
Understanding AI well enough to evaluate it helps you recruit and keep the people who can run it.
Risk management
Fraud detection and anomaly analysis let a small finance team catch what used to need a much bigger one.
Not in a headline-grade scandal. In a credit-scoring tool trained mostly on your existing customer base that quietly scores new applicants from a market segment you have not served yet as higher risk, because the model has never seen enough of them to know better. The fix is not a policy document. It is asking, before you trust the score, who was in the training data and who was not.
Does it change what you are on the hook for?
The last two reasons: regulatory compliance and sustainability. Both matter less on day one and more with every border you cross or every year you scale.
Regulatory compliance
AI-specific rules are arriving fastest in the markets a growing Gulf business is most likely to expand into next, the EU and increasingly the GCC itself.
Sustainability
AI can trim resource use and waste. For most small operators this is a real but secondary benefit of the other ten reasons, not a reason on its own yet.
Which of the eleven deserves this quarter
The original list does not rank itself. This is one reading of it, built for a business under roughly fifty people rather than the multinational the framework often gets written for. Treat it as a starting map, not a verdict.
Do this quarter
Data-driven decisions, resource optimization, customer experience. All three touch operations you already run, and all three pay back before the next board meeting.
Watch closely
Ethical considerations, regulatory compliance. Not urgent alone, but expensive to discover late, especially the first time a decision made by a model gets challenged by a customer or a regulator.
Nice to have, on your timeline
Talent, innovation. Worth building toward deliberately rather than reacting to, since both compound over a longer horizon than a single quarter.
Not yet, revisit next year
Adaptation to change at the market-forecasting level, competitive-advantage tooling, risk-management automation, sustainability reporting. Real, but they assume the first two clusters are already working.
This is a judgment call for a small operator, not a universal ranking. A regulated business, a public company, or one already selling across five countries should read the “watch closely” box as “do this quarter” instead.
The vocabulary behind the eleven reasons
- Which of the four clusters does this belong to?
- Is this a “do this quarter” problem or a “not yet” one for a business our size?
- Who was in the training data, and who was left out?
- If this decision were challenged by a customer or a regulator, could we explain it?
What to do with this
Four moves, in order
- Sort your current AI ideas into the four clusters. Most companies discover their list is lopsided, all market position and no people and trust.
- Pick one item from “do this quarter.” Data-driven decisions, resource optimization or customer experience, whichever already has the most usable data sitting behind it.
- Write one page on what you are on the hook for. Which customer-facing decisions would need to be explained if challenged, and who owns that explanation.
- Revisit the “not yet” box in a year, not never. What is premature now becomes overdue once the first cluster is working.
📚 Read the source in full
Sterling, T. (2024). “11 Reasons Why Leaders Need to Understand Artificial Intelligence (AI).” Balanced Scorecard Institute, a Strategy Management Group company. Published 30 January 2024. The original eleven reasons, in the author's own order and language, with more detail on each than this guide's grouped version carries.
Eleven reasons collapse into four questions you already ask about anything you manage, and the only new leadership skill is knowing which of the eleven earns your attention this quarter and which can honestly wait.
Read the rest of the series
This is one of three “Why AI” guides: the mechanism for directors, the eleven reasons for leaders, and the research on what it changes for children.