There is little doubt about AI’s impending influence on business. As of 2026, artificial intelligence has infiltrated almost every industry and promises to continue to transform the way we work and the competitive landscape for businesses across the globe. 

But it hasn’t come with a roadmap. We know AI as a tool will impact everything from automation and productivity to predictive analytics and decision-making. But to use it as a sustainable application to solve business problems as they arise and as organizations grow and change, requires contextual understanding. Leaders will need to apply AI and related technologies situationally in real time. 

To date, the conversation around AI has been on what technology can do rather than how it should be applied within real organizational systems. Now the focus on AI capability will shift to encompass circumstance, and that means leaders in organizations will need new skills to interpret and apply context (organizational, human, or ethical) to technology decisions. 

From silos to bridges 

So how do we learn to do this? Traditionally, computer science students and graduates focus on only one side of the equation: building and understanding technology. But they’re not taught to translate these tools into business solutions. Likewise, leaders and middle managers understand the problems but can’t always build the tools to solve them.  

The path forward is not to blur the lines between business and technology but to bridge them intentionally and thoughtfully. This is the rationale behind Hult’s new Computer Science for Business degree. We must educate strategic leaders to think through technology—so they understand how systems work, ask better questions, make informed decisions, and connect technical possibilities to business impact. True computer science literacy is no longer just about coding; it’s about knowing how technology drives business forward. We need to learn to apply AI in ways that are more sustainable and meaningful—this is how we future-proof Hult graduates.  

AI has only intensified the need for this shift. While AI tools are powerful, they do not operate with inherent understanding. They exist in silos, dependent on the goals, constraints, and assumptions defined by the humansdeploying them. Without context, even sophisticated systems struggle to create lasting value.

AI integration is not one-size-fits-all. Thoughtful integration comes from education about how to do it effectively in context.  

Traditional computer science education excels at teaching students how to build tools. For example, a more precise wrench, a stronger hammer, or a more efficient machine. These tools may be technically elegant and powerful in isolation. But a tool’s value is never inherent. It only emerges when the tool is applied in the right environment, for the right purpose, and with an understanding of the surrounding system.  

A perfectly engineered hammer used in the wrong context can still produce poor outcomes. AI has magnified this reality. As tools become more powerful and accessible, the consequences of applying them without context become more significant.  

The challenge now isn’t simply whether we can build sophisticated tools, but whether we know how and when to use them to create meaningful business impact.
 

Decentralize and contextualize 

For decades, being “technical” was synonymous with being able to code at a high level. Technical expertise was centralized in a small group of specialists, and innovation depended on access to that expertise. AI has fundamentally disrupted this model. 

Today, the ability to engage with technology is increasingly decentralized. Tools that once required deep technical mastery are now accessible through higher-level interfaces, enabling faster experimentation and lowering the barriers to innovation. 

The fear of coding, which previously excluded many capable problem-solvers from technical work, is diminishing. What once took months to prototype can now be explored in days or even hours. But this shift does not reduce the importance of technical thinking. It changes it. 

With the mechanics of building becoming easier to access, the human responsibility for deciding what to build, why to build it, and how it should be used becomes more critical. There is no replacement for human judgement anddecision-making. 

AI is now a thought partner that can accelerate ideation, analysis, and execution. But it cannot provide intent, judgment, or accountability. These remain human responsibilities, and this is precisely why today’s computer scienceeducation belongs in a business school.  

Business schools expose students to a wide range of real-world contexts: markets, organizations, regulations, ethics, and human behavior. When technical foundations are taught within these environments, students don’t just learn how technology works, but how it drives business forward. They learn to contextualize technical capabilities within strategic goals. 

In this sense, being “technical” is no longer defined solely by the ability to write code. It is defined by the ability to apply technology effectively within complex systems, to move quickly without losing direction, and to translate possibility into impact. As the time to create shrinks, the importance of contextual judgment grows. 

“Future-proof” capabilities 

This shift requires a new set of capabilities to support organizational resilience and long-term relevance. The ability to bounce back from disruption, to recalibrate when conditions change, and to use AI for the right reasons, in the right context, become defining professional skills. Adaptability and agility are no longer abstract traits; they are necessary competencies. 

At the core of this approach to technology are five enduring human capabilities—the 5 Cs—which shape how technology is understood and applied: 

• Critical thinking: Evaluating tradeoffs, questioning assumptions, and recognizing unintended consequences. 

• Communication: Translating between technical possibilities and business needs across diverse stakeholders. 

• Collaboration: Working effectively across disciplines, cultures, and functional boundaries. 

• Creativity: Using technology as an enabler of new solutions, not just as a mechanism for efficiency. 

• Curiosity: Investing in lifelong learning as a way to continue both professional and personal development. 

Putting new knowledge into context 

Artificial intelligence does not possess intent or awareness. Its usefulness depends on how it is configured, constrained, and guided within a given environment. This is where business schools come in. The right environment, also known as contextual understanding, is something business schools are in a unique position to offer.  

The same system can produce very different outcomes depending on the goals it is given, the data it is trained on, and the decisions humans make around its deployment. Preparing students for this reality means giving them more than exposure to tools. It means helping them understand where intelligence can and should be applied, and where human oversight remains essential.  

This approach becomes clearer when viewed through real industry contexts: 

• Banking: AI systems can help identify potential customers or act as personalized financial advisors, but only when designed with a clear understanding of risk, regulation, and trust. Context determines whether personalization enhances relationships or undermines them. 

• Healthcare: AI tools can generate personalized recommendations based on individual health data or function as conversational assistants, supporting patients and providers. Their effectiveness, however, depends on ethical safeguards, interpretability, and careful integration into clinical decision-making. 

• Education: AI increasingly serves as a thought partner—automating lower-level tasks and providing rapid feedback, so learners and educators can focus on higher-value activities such as critical thinking, creativity, and mentorship. 

As AI continues to accelerate, the defining challenge for organizations will not be in accessing the technology but in applying it wisely. The future will favor leaders who can place intelligence—human and artificial—within the right context, guided by purpose. Preparing for that future means educating for technical proficiency alongside judgment, adaptability, and resilience.  

In a world where tools evolve quickly, it is context that gives them meaning, and it is context that ultimately will determine their impact. 

Anusha Vissapragada is Academic Director, Computer Science for Business at Hult International Business School.