Ten years ago, a good strategist could get by on instinct, industry experience and a solid grasp of the numbers. Today, that is no longer enough.

AI has moved from the IT department to the boardroom. It is shaping decisions about where companies compete, what they build, how to allocate resources and who they hire. The leaders shaping companies over the next decade will need more than an understanding of AI tools. They will need to know where AI can create business value, how to evaluate its risks and how to translate technical possibilities into strategic decisions.

If you are thinking about a career in business leadership, understanding this shift is not optional. Here are a few ways in which AI is changing business strategy and the capabilities you will need to brave that shift.

1. AI is turning strategy into a continuous process

For most of business history, strategy was something you set once a year and revisited when things went wrong. A leadership team would build a five-year plan, present it at an offsite and spend the next twelve months executing against it.

That model is breaking down. Markets now move faster than annual planning cycles can track. The strategic advantage is shifting from making better annual plans to making better decisions, faster. McKinsey's 2026 global survey¹ found that nearly nine in ten organisations now use AI regularly in at least one business function, and 44 percent report AI scaling across the entire enterprise, up from 38 percent just a year earlier. That's not a niche of early adopters. That's most of the corporate world treating AI as infrastructure, not experiment.

Imagine working as a strategy analyst for an international retailer. You could use AI to analyse purchasing behaviour, pricing patterns, inventory and customer feedback across markets, then model how different decisions might affect demand.

What this means for you is simple. If you are stepping into a strategy or management role in the next few years, you will be working with systems that surface signals in real time. Hence, you will need to know how to read them, question them and act on them faster than your competitors do. That makes AI literacy increasingly relevant to careers in strategy, consulting, operations and product management.

2. AI is making customer strategy more predictive

Companies are moving from understanding what customers did to anticipating what they may do next. Machine learning can identify patterns across purchasing behaviour, browsing activity and customer interactions. Generative AI can then help teams interpret those patterns and personalise customer experiences.

Take a streaming platform, for instance. Its recommendation engine analyses viewing behaviour to influence what a customer sees next.

The same principle applies across industries. Banks use machine learning for fraud detection, retailers use recommendation systems, telecommunications companies analyse churn and logistics businesses optimise routes.

For you, the value is seeing how AI moves from theory into commercial decisions. You begin to understand not just how an AI system works, but where it can change a business outcome.

3. Data quality is becoming a leadership issue

AI can process enormous amounts of information, but poor data can still produce poor decisions. This makes data governance and data quality increasingly important to business strategy. IBM's 2025 CEO study² found that 72% of the surveyed CEOs viewed proprietary data as important to capturing value from generative AI.

For instance, an AI system might flag a customer likely to leave or identify an attractive investment opportunity. A generative AI model might produce several possible market strategies. However, none of those outputs automatically becomes a good business decision. Leaders still need to consider ethics, regulation, customer trust and the consequences of acting on incomplete information.

This points to a valuable combination: technical understanding paired with judgement and strategic thinking.

4. Implementing AI is a business transformation challenge

Having a promising AI system is very different from getting an organisation to use it effectively. Two companies may have access to the same AI models, but that does not mean they will create the same value from them.

Think about an e-commerce company that uses AI to predict demand more accurately than its competitors. Better forecasts could reduce excess inventory, improve delivery times and help the company respond faster to changing customer preferences.

This is backed by a Boston Consulting Group (BCG) study³ released in 2025. More than 1,250 companies worldwide participated and BCG found that only 5% are achieving AI value at scale, while 60% report minimal revenue and cost gains despite significant investment. The rest are stuck somewhere in the middle, scaling their efforts but admitting they are moving too slowly to matter.

The gap between these two groups is not about who has better software. It is about which company has the people who can connect AI capability to business outcomes. This is one of the less glamorous parts of AI strategy, but it can determine whether an investment creates value or not.

For you, this is the real opportunity. Companies are not short of AI tools. They are short of people who can sit between the technical team and the boardroom and make the technology actually count for something.

Employers are already hiring for this gap

If you want proof that this shift is structural and not a hype, look at what employers themselves are telling researchers.

The World Economic Forum⁴ surveyed over a thousand global employers representing more than 14 million workers and found that 86% expect AI and information processing technologies to transform their business by 2030. Half of companies say they plan to reorient their entire operations around AI, and two-thirds are actively trying to hire AI-skilled workers.

Put those two facts together, and you get a clear picture. Demand for leaders who understand both AI and business strategy is rising fast, and supply has not caught up. That gap is exactly where your career gets built, if you position yourself for it now rather than later.

For someone considering an AI and business degree, that combination is particularly valuable. SP Jain Global’s Master of AI in Business (MAIB) program, for instance, connects technical knowledge with the decisions you may eventually make in the workplace. The program will help you build the ability to move beyond understanding what AI can do and learn how to turn it into decisions that create measurable business value.

Download the program brochure here and see exactly how the curriculum, faculty and projects at SP Jain Global can put you ahead of that gap.

Sources:

1. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai#/
2. https://newsroom.ibm.com/2025-05-06-ibm-study-ceos-double-down-on-ai-while-navigating-enterprise-hurdles
3. https://www.bcg.com/publications/2025/are-you-generating-value-from-ai-the-widening-gap
4. https://www.weforum.org/publications/the-future-of-jobs-report-2025/digest/
   
   

Also read:

The rise of automation: Will robots take over business jobs?

A Journey of Skilled Transformation from Mechanical Engineering to Data Analytics - Sushanth Sugunan's MAIB Experience

Why better business questions lead to greater AI solutions: Insights from Vidit K Bhatnagar