Executive Summary
Every business transformation ultimately rises or falls on one outcome: margin improvement. There are only two durable ways to improve margins. Organizations can lower costs through efficiency, or they can increase pricing power through differentiation.
Generative AI (GenAI) has dramatically accelerated the first lever. It automates tasks, compresses timelines, and can reduce labor costs. These efficiency gains are real. However, as AI becomes broadly used, its margin benefits will be constrained.
This is for two reasons. First, the homogeneity of GenAI output undermines the differentiation businesses need to create competitive distinction. Second, absent training and clear role matching, human workers are unlikely to separate themselves from GenAI’s sameness.
Therefore, what determines success in the AI era is not whether an organization adopts GenAI, but whether it preserves and expands differentiation while doing so.
This white paper advances a simple argument: GenAI drives efficiency, but Original Intelligence drives profitability. Sustainable AI transformation requires deliberately pairing AI’s cost-reduction power with the human capacity to create value that competitors cannot easily replicate.
Hupside’s Hupchecker provides the first operational system to allow organizations to identify and capture human-derived novelty. By measuring Original Intelligence Quotient (OIQ) alongside Personal Originality Value Intensity (POVI) and Role Originality Value Intensity (ROVI), organizations can align talent, roles, and operating models to capture both margin levers simultaneously: lower costs and higher pricing power through differentiation.
The OIQ is a direct predictor of an individual’s key attributes for performance in a Post-AI organization:
• Predilection to create differentiable output.
• Likelihood of using AI, and other tools, to create differentiable output.
• Tolerance for ambiguity, optimism, self-reliance and resilience.
• Consistency in performance across varying roles.
Measurement of a person’s OIQ, combined with understanding constraints on their ability to exercise it, is the mechanism through which organizations can obtain the efficiency gains of AI while maintaining or increasing pricing power. OIQ is the only business metric that matters for the Post-AI organization.
Why Current AI Adoption Strategies are Often Failing to Create Advantage
AI has rapidly become the dominant efficiency technology of modern enterprises. Where it is being adopted, there is significant data demonstrating that it accelerates execution, increases knowledge output and can substitute for human labor in specific functions and roles. Accordingly, there are significant economic advantages for organizations to use GenAI to gain efficiency benefits.
There are, however, several emerging trends that are preventing many GenAI deployments from generating expected benefits. These are due to the following primary reasons:
• Resistance to adoption by individuals: “what’s in it or me?” “Why work with something that is just going to take my job?”
• Efficiency gains are being experienced as increases in output, but decreases in utility (i.e., “work slop”).
• Organizational fatigue: “Every week IT has another AI tool for me to use, and the ones I already have are too much to handle.”
• Failure to promulgate successful adoption across the organization.
• Ineffective matching of AI and job role.
Accordingly, even for organizations with high AI adoption rates, durable improvements in profitability remain elusive. The issue is not execution. It is using GenAI and humans together more effectively.
Why GenAI Adoption On its Own Causes Long Term Value Erosion
There is a growing awareness that GenAI has a predisposition to information homogeneity. It is an architectural limitation that is being reinforced by AI Narcissism – GenAI being trained on AI generated output. While there are promises being made of future development GenAI to the point where it can overcome these limitations, these improvements, if possible, are not evident today. Moreover, GenAI has an additional limitation: when it generates something novel—something that hasn't been seen before—that novelty is absorbed into the model and becomes available to everyone. This is the concept of “shared novelty” where what is novel to a single human observer is not in fact novel.
Even if GenAI eventually overcomes its current limitations, the complexity and cost of developing and deploying these models will likely lead to two outcomes: owners of proprietary AI systems will either reserve the highest levels of performance and novelty for themselves or ration access to those capabilities based on customers’ ability to pay. This means that while only AI homogeneity is a challenge for businesses today, the likelihood of GenAI being at best a long-term source of differentiation for only a small number of highly capitalized businesses is very strong.
For both reasons, nearly all businesses who adopt GenAI as an efficiency tool will see their margins compress as competitors adopt similar tools. A race to the bottom of efficiency in the long run will result in a new equilibrium where AI-enabled businesses are producing fungible output and little differentiation.
Where Differentiation Actually Comes From
If AI drives convergence, the next question is straightforward: where does true novelty come from, and how will most businesses differentiate?
That differentiation comes from people — specifically from Original Intelligence (OI): the human capacity to generate ideas that fall outside AI’s statistical center.
Original Intelligence is not creativity as a personality trait or a cultural aspiration. It is economically relevant originality: reframing problems, making unexpected connections, and producing insights that cannot be easily replicated by the same probabilistic engine competitors are using.
This reframes a debate often treated as cultural or ethical. Retaining humans in the value creation loop is not about optics or social responsibility. It is about economics.
Organizations that suppress or fail to deploy human originality may achieve short-term efficiency gains, but they sacrifice differentiation, and with it, pricing power.
Original Intelligence as a Strategic Variable
In an AI enabled enterprise, the technology value stack includes four components: data, infrastructure, AI systems, and Original Intelligence. As AI dominates the first three layers, OI is the defining source of differentiation. Where efficiency becomes ubiquitous — the spoken promise of AI — few organizations will succeed by competing on efficiency alone. Differentiation through OI is becoming increasingly important.
The mix between the four components will vary by the business model and strategy of an organization. For example, the use of AI agents will, if successfully deployed, result in a higher value add portion being derived from AI systems spend, and likely a lesser focus on Original Intelligence. Conversely, a business model that relies upon expert insight will incorporate a higher level of Original Intelligence in its technology value chain to provide for differentiation.
Due to the inherent limitations of AI, Original Intelligence will always be required in the portion of a business model that requires real differentiation. This is why OI is always part of the value stack in a post-AI organization, though most organizations have yet to recognize it. What will differ is the degree and style in which each individual’s OI capabilities match their role. This means that effectively matching the individual’s capability for Original Intelligence to their role becomes more important, not less, post-AI deployment.
It also means that organizational units will need to optimize the relationship of OI and AI to maximize return on investment. When organizations systematically measure originality capability using the Hupside framework, they can design teams and workflows that amplify human creativity and encourage effective utilization of AI’s efficiency benefits to ensure that AI augments rather than replaces OI.
...to read more, download the whitepaper
