Featured Analysis:
Sandeep Krishnamurthy
Singelyn Family Dean
College of Business Administration
Cal Poly Pomona
The existential angst about the impact of AI on economic mobility is widespread. Every job in the knowledge economy will be fundamentally informed by the presence of AI. Learning the new rules of the game is vital for every business leader.
The three As
Fundamentally, artificial intelligence is already reshaping work along three distinct vectors (acceleration, augmentation, and automation) each carrying different implications for the future of jobs.
Acceleration compresses time, enabling professionals to execute tasks (e.g., business analysis, coding, content creation) in minutes that once took hours, effectively increasing the velocity of output across industries. In a world where “time is money”, the reduction of the time it takes to complete a task is fundamentally valuable to businesses.
Augmentation goes further, pairing human judgment with machine intelligence so that managers, analysts, and designers operate with a form of “co-intelligence,” where AI generates options and humans refine, contextualize, and decide. In this set of tasks, AI is adding to the capacity of the human being. It is, as though, the human being has been provided with a team member to do a task.
Automation, the most disruptive force, removes entire categories of routine work (e.g., routines, data cleaning and integrity, basic reporting) forcing a reconfiguration of entry-level roles and traditional career ladders.
Together, these forces are not eliminating work so much as redefining it, shifting the premium from effort and knowledge accumulation to judgment, adaptability, and the ability to work fluently within and across intelligent systems.
Use of AI becomes fundamental
The three As create powerful incentives to use AI more.
As acceleration raises the baseline speed of execution, professionals who opt out quickly find themselves outpaced on both cost and responsiveness. Augmentation compounds this effect: those who work effectively with AI produce higher-quality outputs (specifically, more options considered, better-informed decisions) making non-AI workflows comparatively inferior. Automation, meanwhile, resets expectations entirely; once routine tasks are handled by machines, organizations reallocate human effort toward higher-value activities that themselves depend on AI-assisted insight. The result is a self-reinforcing cycle: the more AI improves productivity and quality, the more it becomes embedded in workflows, raising competitive standards and making its use not a differentiator but a necessity.
At the level of the individual, the logic is unforgiving: as acceleration compresses cycle times, augmentation improves decision quality, and automation removes routine scaffolding, the baseline for competent performance rises. A professional who does not use AI is no longer merely slower; they are structurally less capable, i.e., operating without the expanded search, synthesis, and simulation that define modern knowledge work.
At the team level, these gaps compound. AI-enabled teams coordinate faster, explore more alternatives, and execute with greater precision, creating a widening performance delta that cannot be closed through effort alone. Collaboration itself becomes AI-mediated, with shared tools shaping how information is generated, validated, and acted upon. In such environments, opting out degrades the effectiveness of the entire unit. We are already seeing the rise of hybrid teams- where AI and human beings work together to achieve goals. The implications of this for the creation of organizations and the framing of markets will be profound.
At the organizational level, the imperative becomes strategic. Firms that embed AI deeply into workflows reset benchmarks for cost, speed, and quality, forcing competitors to follow or fall behind. Automation shifts labor toward higher-order tasks that depend on AI-assisted insight, while augmentation enhances managerial decision-making across functions, from finance to operations to marketing.
Over time, AI adoption ceases to be an innovation initiative and becomes core infrastructure akin to electricity or the internet. The cumulative effect is a ratchet: once industry standards move, they do not revert. Organizations, like the individuals and teams within them, are compelled to use AI more because the competitive equilibrium continuously redefines “normal” upward.
The Power of Recombinant AI Fluency
If one equates AI to a tool, the impulse is to think about training. The previous logic of becoming fluent in one tool to advance becomes limiting in the world of AI. Instead, what starts to matter more is integration, combination and recombination. Rather than focusing on becoming fluent with one tool, the value will migrate to those that can recombine.
Then, recombinant AI fluency (i.e., the capacity to weave together the specialized strengths of multiple AI engines into a single, value-producing workflow) is 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 university students, the implication is clear: develop the mindset and toolkit of a systems integrator, not a single-platform (or single-tool) specialist. That learning journey spans four competencies:
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Systems thinking to map where each model adds unique value.
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Cross-platform prompt engineering to obtain optimal, combinable outputs.
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Lightweight automation skills (APIs, no-code connectors) to knit tools together.
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An ethical framework for governing composite results.
The New Economic Mobility Code
The new economic mobility code is no longer written in credentials alone; it is written in capability formation under conditions of intelligent abundance. In an AI-mediated economy, access to tools is widespread across the workforce, but outcomes diverge based on how employees use those tools in real organizational contexts.
The pathway to economic mobility therefore rests on four reinforcing pillars: experimentation, engagement, evaluation, and experience. These are not training preferences—they are labor market imperatives. They determine how quickly employees translate access to AI into differentiated performance and upward mobility.
Experimentation is the entry point. Employees must operate in environments where they can test ideas, iterate with AI systems, and explore multiple solution paths without excessive risk aversion. This is followed by engagement—direct interaction with clients, cross-functional teams, live data, and operational constraints. Engagement ensures that experimentation is grounded in value creation, not abstraction. Together, experimentation and engagement create organizational velocity: employees move from task execution to problem-solving, from instruction-following to initiative-taking.
The differentiator, however, lies in evaluation and experience. Evaluation is the discipline of judgment—interrogating AI outputs, identifying errors, assessing trade-offs, and making accountable decisions under uncertainty. It separates superficial AI usage from professional-grade contribution. Experience then compounds these cycles into durable capability: repeated exposure to meaningful problems, refined through reflection and feedback.
The result is an employee who does not merely complete tasks, but consistently demonstrates validated, context-aware, and AI-augmented performance. That is the new currency of economic mobility inside organizations.
The implications for how we educate are immense. Universities must redesign learning models to operationalize the four Es as a system, not a set of add-ons: curricula should be organized around continuous experimentation (AI-enabled labs, rapid iteration cycles embedded in every course), deep engagement (live industry problems, client-facing projects, and cross-disciplinary teams as default pedagogy), rigorous evaluation (assessment frameworks that reward judgment, audit AI workflows, and require students to defend decisions under uncertainty), and cumulative experience (sequenced, portfolio-building work across the degree that compounds in complexity and real-world stakes).
This requires moving from course-centric delivery to workflow-centric learning, where students repeatedly design, execute, critique, and refine AI-augmented work in authentic contexts—producing graduates whose capabilities are not inferred from transcripts but evidenced through sustained, evaluated, real-world performance.
Final Takeaways
AI has crossed the threshold from innovation to necessity. The combined forces of acceleration, augmentation, and automation create a ratchet effect: once industry standards rise, they do not revert. Firms that fail to embed AI into core workflows will not merely lag—they will become structurally uncompetitive on cost, speed, and quality.
AI is compressing execution time, augmenting decision-making, and automating routine work. The value of human labor is migrating away from task completion toward judgment, contextualization, and decision accountability. CEOs must redesign roles, performance metrics, and incentives around decision quality, not output volume. Your competitive advantage will hinge on how well your people think with AI, not how much they produce.
AI creates a flywheel: faster execution → better outputs → higher expectations → deeper adoption. At the team and organizational level, this compounds into widening performance gaps that cannot be closed through effort alone. The frontier is not proficiency in a single AI tool, but the ability to orchestrate multiple models into integrated workflows (“model mashups”). This compresses entire value chains—from research to insight to execution. Build organizational capability in AI orchestration. Invest in roles like AI workflow designers and multi-model analysts. The winners will be system integrators, not tool users.
Economic mobility inside a company now depends on the “Four Es”
Performance—and therefore advancement—is increasingly driven by:
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Experimentation (testing with AI)
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Engagement (real-world problem exposure)
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Evaluation (judgment and critique of AI outputs)
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Experience (compounded, applied learning)
Rethink talent development. Move beyond training programs toward AI-enabled capability systems. Promotions should be based on demonstrated, evaluated, real-world performance—not credentials or tenure.
Bottom Line for a CEO
This is not merely a technological shift. It is a redefinition of the production function of knowledge work. Firms that redesign workflows, roles, and talent systems around AI will reset industry standards. Those that do not will be competing with a structurally inferior operating model.
