AI is no longer some fascinating side project that a handful of data geeks are tinkering with. Instead, it’s more like a compass guiding the entire business vessel now. However, it’s important to remember that AI can’t steer a company alone. Savvy executives are combining some skills and tools to translate computer-driven insights into sound moves that enable them to outmaneuver the competition. Three of these skills are absolutely crucial.

Be Data-Literate to Move Numbers to Narrative

Modern-day strategies live and die on data fluency, as any generative model, forecasting engine, or customer-insight dashboard is fed by structured information. Executives who were once happy to leave “the numbers” to others now read, question, and interpret data sets as naturally as they negotiate top-line targets. Many combine business and data within an MBA online program that couples classes in machine learning basics with traditional finance and strategy modules.

The online format is reflective of the way these executives will operate in the future, as cloud-based dashboards, cloud notebooks, and real-time BI software power day-to-day decision-making. To make data more useful, managers begin by questioning each metric with the same level of scrutiny they reserve for a balance sheet:

• Where did this number come from? 
• How was it scrubbed? 
• What assumptions might be distorting it? 

They then master the art of translating a narrative and describing a model’s findings in simple language that motivates finance, marketing, and product teams to take action. With time, the discipline creates a culture in which insights seep all the way down through the firm rather than getting stuck within the analytics group, speeding the feedback loop between experimentation and profit.

Develop AI-Driven Market Intelligence Solutions to Find Hidden Opportunities

When data literacy helps identify the questions, leaders require a tool that figures answers quicker than any human analyst can. AI market-intelligence platforms rely on LLMs with retrieval-augmented generation to search patents, news, social buzz, and proprietary research for emerging patterns.

Traditional strategy cycles are based on quarterly reports and consultant slide decks that are stale the moment they hit the printer. An AI platform refines its knowledge graph in real time, so leaders see a competitor’s price shift or a supply-chain disruption before the market responds.

It takes more than a log-in to master these tools. Executives must first establish high-value strategic hypotheses and then instruct the system to watch for specific signals. A useful tip is to create saved queries that deliver daily insight digests directly to a shared Slack channel with product and finance stakeholders, as group review avoids the tunnel vision interpretation. 

Ensure Ethical AI Governance to Build Trust

The last requirement isn’t an algorithm but a guardrail. Good ethical leadership powers innovation, and that can be achieved through robust AI frameworks, which help turn lofty ideals into concrete checkpoints at each step of a model’s life cycle.

To strategists, leadership matters because a great market-entry strategy collapses if regulators, customers, or staff don’t have faith in the AI that designed it. It’s vital for executives to chart risk early on focusing on the following: 

• Is the training data representative? 
• Is the recommendation engine explainable? 
• Who owns remediation when bias is uncovered after launch? 

Asking these questions during regular go-to-market check-ins prevents expensive retrofits.

Endnote

When leaders learn what questions to ask, AI systems respond promptly, and governance ensures the response is correct, only then will you be able to notice strategic transformation happening at the highest level. Be sure to focus on all three skills mentioned here and AI will become your best friend in growing your business.