The most common question I get from business school students is this- “How do I position myself around AI in a tough labor market?”
This is my advice to them- “Value will be created through the use of multiple AI systems- e.g., Elicit + NotebookLM + Claude. Cultivate the mindset of combining outputs of multiple AI systems.”
Recombinant AI fluency—the capacity to weave together the specialized strengths of multiple AI engines into a single, value-producing workflow—is rapidly emerging as the differentiator that will separate tomorrow’s business leaders from yesterday’s power users. In an environment where data, analytics, and automation now converge at enterprise scale, relying on a single model or platform is no longer sufficient. Competitive advantage will belong to professionals who can architect and orchestrate “model mashups,” moving seamlessly from discovery to decision without handing tasks back to manual processes.
The business impact is already visible. Imagine beginning with Elicit to harvest and synthesize academic evidence in minutes, piping the distilled findings into NotebookLM to structure causal chains and scenario models, and then handing that output to Claude to generate investor-ready narratives or tailored customer proposals. By chaining these capabilities, a team compresses research, insight generation, and storytelling cycles from weeks to hours—unlocking faster product launches, more precise risk assessments, and hyper-personalized engagement at scale. As organizations recognize this leverage, new roles are emerging: AI workflow designer, multi-model analyst, and insight product manager—positions tasked explicitly with commercializing multi-engine AI ecosystems and delivering step-change productivity gains.
For business students, the implication is clear: develop the mindset and toolkit of a systems integrator, not a single-platform specialist. That learning journey spans four competencies—
Systems thinking to map where each model adds unique value.
Cross-platform prompt engineering to obtain optimal, combinable outputs.
Lightweight automation skills (APIs, no-code connectors) to knit tools together.
An ethical framework for governing composite results.
Business schools can accelerate mastery through capstones that require chaining at least three models, and co-ops with firms experimenting at the AI frontier. Graduates who embrace this agenda will move directly into the vanguard of an AI-driven economy—architecting the next generation of growth engines rather than merely operating yesterday’s tools.
Agentic AI Accelerates the Need for This
Recombinant AI fluency is quickly evolving into agentic AI mastery—the ability to design networks of autonomous agents that negotiate with one another to plan, reason, and act on a firm’s behalf. Google’s new Agent-to-Agent (A2A) architecture on Vertex AI illustrates the shift: developers assemble specialized agents (a data-retrieval agent, a constraints-checking agent, a language-generation agent, etc.) and allow them to hand off tasks over a secure protocol, choosing the right tools at every step.
The same architecture is leaping from commerce to pure academic research. Google’s AI Co-Scientist, built on Gemini 2.0, treats each hypothesis-generation, literature-synthesis, and protocol-design module as a cooperating agent. In benchmark studies, it collapsed months-long biomedical research cycles into days by iteratively proposing experiments, critiquing them, and refining the next round—demonstrating that multi-agent reasoning can accelerate not only go-to-market timelines but fundamental innovation itself.
For today’s business student, the curriculum therefore expands from “prompt engineering across models” to architecting agent economies. Essential competencies now include multi-agent systems thinking, negotiation-protocol design, API choreography, and AI ethics at the collective level (e.g., resolving agent-agent conflicts and ensuring auditability of emergent plans). Capstone projects that require students to deploy at least three specialized agents—with explicit service-level objectives and governance rails—will turn graduates into the orchestrators of tomorrow’s autonomous enterprises. Those who master agentic AI will not merely operate toolchains; they will build self-optimizing ecosystems that continuously discover new revenue, reduce cost, and invent the products that redefine their industries.
Sub-components of Recombinant AI Fluency
Critical Evaluation: Understanding the need to obtain outputs from multiple AI systems and how they each partially help with the solution.
Comparison: Deeply knowing the strengths and weaknesses of each AI system and what is can offer to solve a particular problem.
Curation: Carefully including the set of tools that will be used.
Daisychaining Outputs: Simply thinking of ways by which the output of one model becomes the input to the next one.
System Level Ethical Reasoning: Understanding the ethical and bias issues at the level of the system. For instance, what are the principles and process to resolve inter-agentic conflict? How can one AI system (say, Gemini) debias the output of another (say, Llama)?
Bottom line
The literature converges on one insight: fluency with whole, dynamic systems—not just point tools—has become a foundational graduate outcome. Embedding project-based, cross-disciplinary and AI-integrated experiences across the curriculum is now the evidence-based route to producing graduates who can learn, unlearn and recombine technologies at the pace industry demands.
Beautiful and very relevant to stay future proof, "learn, unlearn and recombine technologies at the pace industry demands"!!!