By Chris Jones, CTO of Eclipse AI

Introduction: Mapping the Changing Landscape

We stand at the edge of a vast plain, watching a dam breaking in slow motion. The AI revolution isn’t just another technological cycle; it’s a fundamental reshaping of our economic landscape. Through five distinct but interconnected visualisations, I aim to make tangible what many enterprise leaders feel but struggle to articulate: the profound disruption caused by the dual commoditisation of intelligence and product.

As a strategic advisor who has guided organisations through previous technological transitions, I’ve learned that abstract concepts often remain unactioned until they’re made visible. These visual frameworks aren’t merely illustrative—they’re navigational tools designed to help you plot your course through increasingly turbulent waters.

Let us begin this visual journey by surveying the topography of the challenge before us.

Chapter 1: The Rising Floodwaters Map

Picture a topographical map of a vast plain, with hills and mountains rising from the flatlands. Now imagine a blue gradient washing across this landscape—not in a uniform wave, but in varying depths and velocities. This is The Rising Floodwaters Map.

What makes this visualisation powerful is its rejection of the simplistic “AI will take all jobs” narrative. Instead, it shows commoditisation as an uneven process, affecting different functions, sectors, and geographies at dramatically different rates.

Look closely at the lowest ground on our map. Here, routine tasks like basic data entry, simple customer service interactions, and standard bookkeeping are already submerged. The water isn’t ankle-deep; it’s waist-high and rising rapidly. One multinational I advised recently has completed a prediction model that shows their £2 million investment in a customer service platform was rendered virtually worthless in eighteen months by a £20-per-seat AI solution that outperformed it on every metric.

The middle elevations—where we find junior coding roles, technical writing, and basic analysis—are experiencing the first lapping waves. These functions aren’t underwater yet, but the moisture is unmistakable. A publishing company I work with found that AI tools reduced their editing cycle from two weeks to three days—a compelling efficiency that simultaneously threatens their established workflow and staffing model.

Only at higher elevations do we find functions still relatively dry: strategic consulting, complex systems integration, creative direction, and deeply domain-specific advisory work. Yet even here, storm clouds gather on the horizon.

The geographic variations on our map tell another crucial story. The US market (west on our map) shows deeper, faster-rising waters, a reflection of its aggressive adoption patterns and relatively permissive regulatory environment. A Boston-based AI startup recently deployed an automated contract review system that achieved 87% of the accuracy of junior associates at 3% of the cost.

Meanwhile, European markets (east on our map) show shallower waters, temporarily slowed by regulatory dams like GDPR and the AI Act. A Frankfurt-based client found their AI implementation delayed by six months due to compliance requirements, with the German Workers Council, a frustration that simultaneously gave them more time to prepare their workforce.

Emerging markets display varied topography, with both severe vulnerabilities and unique opportunities. Some regions may leapfrog directly to AI-augmented solutions, skipping entire stages of technological development.

The red flags on our map mark the danger zone—areas where commoditisation is accelerating with particular ferocity. One such flag waves over mid-tier management consultancies, where standardised analysis packages face extinction-level pressure from AI solutions that can generate comparable insights in minutes rather than weeks.

The green flags indicate higher ground opportunities—places where human+AI combinations create new value rather than merely replacing existing functions. One such flag marks the emerging field of AI governance and ethics, where technical understanding must be wedded to human judgment in ways pure automation cannot replicate.

This map isn’t static. The waters rise daily, and yesterday’s safe ground may be tomorrow’s swamp. The question becomes: how quickly can you relocate your value proposition to higher ground, and what path will you take to get there?

Chapter 2: The Twin Commoditisation Vortex

Let us now zoom in on the mechanism driving these rising waters through our second visualisation: The Twin Commoditisation Vortex.

Imagine two massive whirlpools side by side, connected by a powerful current. The left vortex represents “Intelligence Commoditisation,” the right “Product Commoditisation.” Together, they form a self-reinforcing system that accelerates as it grows.

The outer ring of the Intelligence Commoditisation vortex represents traditional knowledge work—activities that once commanded premium fees based on specialised training or experience. As we move inward through progressive rings, we see specialisation being steadily drawn toward the centre, where AI capabilities increasingly replace functions once performed exclusively by humans.

I remember visiting the London headquarters of a financial services firm in 2018, where sixty analysts occupied an entire floor, painstakingly preparing quarterly reports. Having caught up with them in 2024, I found six analysts remaining, now primarily focused on checking and contextualising AI-generated outputs. The dollar signs diminishing as they spiral inward on our diagram weren’t abstract to those forty-eight professionals whose roles had been commoditised.

The right vortex shows Product Commoditisation following a similar pattern. The outer ring contains proprietary software products—once reliable sources of recurring revenue and competitive differentiation. As we spiral inward, features become standardised, functionality gets absorbed into foundational models, and product lifecycles shorten dramatically.

A security software company I advised had built their entire business around proprietary algorithms for threat detection. Their CEO called me in a panic when OpenAI’s GPT-4 demonstrated comparable capabilities as a mere side effect of its general intelligence. “My product roadmap just collapsed from three years to three months,” he told me. “What do I do now?”

The connecting flow between these vortices is critical to understanding the acceleration we’re witnessing. As intelligence becomes commoditised, it feeds product commoditisation by making sophisticated features cheaper to implement. As products become commoditised, it creates pressure to automate more intelligence functions to maintain margins. Each vortex strengthens the other.

Surrounding these twin vortices are arrows indicating the acceleration factors: exponential growth in computing power, the expanding availability of training data, and breakthrough innovations in algorithms. Any one of these factors would drive commoditisation; together, they create a perfect storm.

What makes this visualisation particularly valuable is how it helps leaders anticipate which aspects of their business might be pulled into the vortex next. By plotting your current offerings and capabilities on this diagram, you can visualise their trajectory and likely timeframe for commoditisation—a crucial first step in developing strategic responses.

The question becomes not whether your current value proposition will be pulled toward the centre, but how quickly, and what new value you can create at the periphery to compensate.

Chapter 3: The New AI Talent Stack Pyramid

If the first two visualisations help us understand the problem, our third framework—The New AI Talent Stack Pyramid—begins to outline the solution, at least at the individual and team level.

Picture a pyramid with five distinct layers. At its foundation lies Domain Expertise—the deep, specialised knowledge of a particular field, industry, or function. This isn’t arbitrary; it represents a deliberate choice to build our talent strategy on the bedrock most resistant to commoditisation.

I recently advised a healthcare technology firm struggling with their hiring strategy amidst AI disruption. Their instinct was to prioritise technical AI skills above all else. “Stop,” I told them. “You’re building on sand. Hire for deep healthcare expertise first; we can teach the AI skills later.”

The second layer of our pyramid represents AI Fluency—not necessarily the ability to build AI systems, but to understand their capabilities, limitations, and effective ways to direct them. This includes knowing when to trust AI outputs and when to question them, how to craft effective prompts, and how to integrate AI tools into existing workflows.

A pharmaceutical research team I work with now requires all scientists to demonstrate competency in directing AI systems to accelerate literature reviews and generate hypotheses. They don’t need to understand the mathematics of large language models, but they must know how to effectively collaborate with these systems.

The third layer—Adaptability & Learning Agility—acknowledges that specific AI tools and techniques will continue evolving rapidly. The half-life of technical knowledge grows shorter each year. What matters isn’t mastery of today’s tools, but the ability to quickly adapt to tomorrow’s.

The fourth layer—Collaboration—encompasses both human-human and human-AI teamwork. As AI handles more deterministic tasks, human roles increasingly focus on coordination, negotiation, and integration of diverse perspectives.

At the pyramid’s apex sits Strategic Synthesis & Innovation—the ability to identify novel opportunities, combine insights from disparate domains, and create new value that wouldn’t exist without human creativity. This becomes the ultimate high ground as commoditisation rises.

Along the sides of our pyramid, arrows indicate Value Creation Potential increasing toward the top, illustrating why organisations should invest in developing these higher-level capabilities. More striking is the dotted “Commoditisation Risk Line” crossing the lower portion of the pyramid. Skills below this line face rapid devaluation in the market.

This pyramid isn’t merely descriptive; it’s prescriptive. It suggests specific development priorities for individuals and organisations seeking to remain above the commoditisation line. It helps answer questions like: Which skills should we prioritise in hiring? Where should we invest our learning and development resources? How should we restructure teams to maximise complementarity with AI systems?

A retail executive recently asked me how to future-proof her organisation against AI disruption. My answer began with this pyramid. “Don’t try to compete with AI on its terms,” I advised. “Build upward from your domain expertise to create value that requires the full stack—that’s your sustainable advantage.”

Chapter 4: The Acemoglu Direction Matrix

Our fourth visualisation—The Acemoglu Direction Matrix—steps back to examine the broader socioeconomic implications of AI development, drawing inspiration from the work of economist Daron Acemoglu.

Imagine a 2×2 matrix. The horizontal axis represents the “Direction of AI Development,” spanning from “Replacement-Focused” on the left to “Augmentation-Focused” on the right. The vertical axis shows the “Distribution of Benefits,” ranging from “Concentrated” at the bottom to “Dispersed” at the top.

This creates four distinct quadrants, each representing a potential future for our AI-transformed economy:

Quadrant 1 (Replacement/Concentrated) depicts “Technological Feudalism”—a world where AI primarily replaces human labour while concentrating benefits among the few who control these systems. This produces a handful of winners amid widespread displacement.

I witnessed a disturbing example of this trajectory when a manufacturing firm deployed automation that eliminated 200 shop floor positions while concentrating productivity gains among shareholders and a small technical team. The community impact was devastating, with no corresponding investment in reskilling or transition support.

Quadrant 2 (Replacement/Dispersed) presents the “Safety Net Society”—where AI similarly replaces human work, but institutional mechanisms redistribute the resulting productivity gains. This might involve concepts like universal basic income or significantly expanded educational opportunities.

Quadrant 3 (Augmentation/Concentrated) shows “Elite Amplification”—where AI augments human capabilities but does so primarily for a privileged minority, creating superstars who leverage AI to dramatically outperform their peers.

I’ve observed this pattern emerging in fields like law, where elite firms provide their partners with sophisticated AI tools that multiply their productivity, while smaller practices lack both the tools and the know-how to compete.

Quadrant 4 (Augmentation/Dispersed) represents “Shared Prosperity”—the most optimistic scenario where AI primarily augments human capabilities, and these benefits are widely distributed through thoughtful institutional design.

A healthcare network I advised achieved elements of this quadrant by deploying AI tools that enhanced the capabilities of all practitioners, from specialists to community health workers, while simultaneously improving access for underserved populations.

What makes this matrix particularly valuable is the directional arrows. The solid arrow shows our current trajectory—moving from the centre toward Quadrant 1 (Technological Feudalism). This isn’t inevitable, but it’s the path of least resistance given current economic incentives and institutional structures.

The dotted arrow indicates an alternative path toward Quadrant 4 (Shared Prosperity). This doesn’t happen automatically; it requires deliberate steering through policy choices, business strategies, and institutional innovations.

For enterprise leaders, this framework provides crucial context for strategic decisions. It helps answer questions like: How might our AI investments affect our workforce and communities? What responsibility do we have to steer toward more broadly beneficial outcomes? How might regulatory responses to growing inequality affect our business model?

A CEO recently asked me whether their AI strategy should prioritise cost reduction or capability enhancement. My answer drew on this matrix: “If you focus exclusively on replacement, you may see short-term gains but contribute to a future that ultimately threatens your social licence to operate. Consider how to use AI to augment your entire workforce—that’s the path to sustainable advantage.”

Chapter 5: The Strategic Response Timeline

Our final visualisation—The Strategic Response Timeline—brings together the insights from previous frameworks to create an actionable roadmap for enterprise leaders.

Imagine a horizontal timeline stretching from the present to 2030, divided into multiple swim lanes representing different sectors: Financial Services, Healthcare, Manufacturing, Professional Services, and so on.

Across this timeline, colour-coded intervention points mark critical moments requiring strategic action:

Red points indicate critical junctures demanding immediate pivots—moments where continuing on the current path leads to rapid value destruction. For retail banking, one such point appears in mid-2025, when AI-powered financial assistants are projected to disintermediate traditional customer relationships.

I recently advised a regional bank whose leadership dismissed these assistants as “toys.” After walking them through this timeline, showing how rapidly similar technologies had transformed adjacent industries, they initiated an urgent strategic review that likely saved their retail business.

Amber points represent preparation and capability-building phases—periods where the immediate threat remains moderate, but organisations must develop new skills and assets to prepare for coming disruption. For healthcare providers, an amber point in early 2026 marks when diagnostic AI is expected to reach human parity across major specialities.

Green points highlight opportunity windows for market leadership—moments when organisations with the right capabilities can leapfrog competitors by embracing new AI-enabled business models. For manufacturing, a green point in late 2025 marks the projected convergence of AI with robotics and IoT to enable truly adaptive production systems.

Key milestones dotted along the timeline—“AGI Capabilities,” “Regulatory Enforcement,” “Mass Adoption Thresholds”—provide critical context for strategic decisions. These aren’t merely technological markers but inflection points where market dynamics fundamentally shift.

Strategic responses appear at appropriate points along each sector’s journey: “Pivot to Services” as product commoditisation accelerates; “Micro AI Build” as the talent stack evolves; “New Talent Stack Development” as organisational capabilities must transform.

What makes this timeline particularly valuable is its sector-specific granularity. It acknowledges that while the overall pattern of commoditisation affects everyone, the timing and specific manifestations vary dramatically by industry. Financial services face more immediate disruption than healthcare; consumer products more than industrial manufacturing.

A diversified conglomerate I work with used this framework to prioritise their transformation investments across business units, focusing immediate resources on divisions facing near-term critical junctures while implementing longer-term capability building in less immediately threatened areas.

For individual enterprise leaders, this timeline provides a crucial reality check on the urgency of response. It helps answer questions like: How much time do we have? Which capabilities must we develop first? Where are the windows of opportunity amid the disruption?

A professional services firm recently asked whether their three-year digital transformation plan was adequate. After mapping their journey on this timeline, the answer was clear: “You’re planning a leisurely stroll through a landscape that’s changing at a sprint. The window for effective action in your sector closes in eighteen months, not three years.”

Conclusion: From Visualisation to Action

These five visualisations—the Rising Floodwaters Map, the Twin Commoditisation Vortex, the New AI Talent Stack Pyramid, the Acemoglu Direction Matrix, and the Strategic Response Timeline—form an integrated framework for understanding and responding to the AI commoditisation challenge.

Together, they help us see this disruption not as an amorphous threat, but as a structured, predictable pattern with clear strategic implications. They transform abstract concerns into tangible landscapes we can navigate with purpose.

But visualisation without action is merely fascinating rather than useful. The true test of these frameworks lies in how they inform your strategic choices:

1. Where do you currently stand on the Floodwaters Map? Are you already ankle-deep, or still on seemingly dry ground? How quickly is the water rising in your sector?
2. Which aspects of your value proposition are being pulled into the Commoditisation Vortex? What new value can you create at the periphery?
3. How developed is your AI Talent Stack? Where are the critical gaps between your current capabilities and those required to remain above the commoditisation line?
4. Which quadrant of the Acemoglu Matrix are your AI investments steering toward? Are you focused primarily on replacement or augmentation? How might that affect your long-term sustainability?
5. What critical junctures appear on your Strategic Response Timeline? How prepared are you for the red intervention points in your sector?

The commoditisation of intelligence and product is not a future possibility—it’s a present reality advancing at an accelerating pace. These visualisations help us move beyond paralysis or denial toward strategic action. They help us see not just the rising floodwaters, but the higher ground we can reach by making the right moves at the right time.

Those who understand these patterns—who can see beyond immediate technical capabilities to the deeper economic reorganisation underway—position themselves not as victims of disruption but as architects of transformation. They find opportunities amidst upheaval, higher ground amidst the rising tide.

As your organisation navigates this changing landscape, remember: the goal isn’t to outrun the flood indefinitely. It’s to build new capabilities that thrive in the transformed terrain—to evolve from terrestrial creatures to amphibious ones, finding advantage where others see only threat.

The waters are rising. The time for strategic elevation is now.

Sofia Salazar is a Strategic Technology Advisor specialising in helping enterprise leaders navigate technological disruption. With a background spanning both technology implementation and economic policy, she focuses on developing resilient strategies for organisations facing rapid change.