5 Skills That Become More Valuable With Greater AI Adoption
A framework from Elevation Capital has been gaining traction in conversations about how AI changes the nature of work. It identifies five human skills that increase in value as AI adoption deepens: taste, context synthesis, judgement, strategic instinct, and trust building. Each is defined through a clear division of labour. AI generates content, the human decides if it fits the brand. AI handles discrete tasks, the human connects the dots across functions. AI produces options, the human picks the right one. AI optimises a solution, the human questions whether it is solving the right problem. AI drafts messages, the human builds the relationship.
The framework is useful. It captures something real about where human value concentrates as AI takes over execution. But it describes the destination without mapping the route. It tells you what the human-AI relationship should look like. It does not tell you how to build an organisation, a workflow, or an AI system that actually produces this relationship in practice.
The gap between the framework and reality is where most AI deployments live. And it is a gap worth examining seriously.
Taste Is Accumulated, Not Announced
The first skill, taste, is described as the human deciding whether AI-generated output is right for the brand. This is accurate as far as it goes. But taste in an organisational context is not a single person's aesthetic preference. It is an accumulated understanding of what the organisation values, what its customers respond to, what its culture permits, and what its market position requires.
This understanding rarely exists in any documented form. It lives in the heads of people who have been in the organisation long enough to have absorbed it. A marketing director who knows that a particular tone will not work for this audience. A product manager who recognises that a feature, while technically impressive, does not align with the company's positioning. A senior engineer who understands that a design choice, while efficient, violates an unwritten principle about how the team builds things.
When AI generates output and a human applies taste to evaluate it, the quality of that evaluation depends entirely on how much institutional context the human carries. In organisations where this context is concentrated in a few individuals, the taste bottleneck simply shifts from production to evaluation. The AI generates faster, but the senior person still reviews everything, and the review becomes the constraint.
The operational question is whether the AI system can learn from the taste decisions made by senior people, not to replace their judgement but to reduce the volume of output that requires it. A system that generates ten options, where eight are obviously wrong, and two require human evaluation, is less useful than one that generates three options, all within the range the organisation's taste would accept, and the human selects among them. The difference is whether the AI has been exposed to enough of the organisation's accumulated context to narrow its output intelligently.
Context Synthesis Requires Structured Information
Context synthesis, the second skill, is described as connecting dots across functions while AI handles discrete tasks. This is perhaps the most important skill in the framework and also the one most poorly supported by typical AI deployments.
In most organisations, context is fragmented by design. The sales team sees customer interactions. The operations team sees delivery performance. The finance team sees cost structures. The compliance team sees regulatory requirements. Each function operates with its own data, its own systems, and its own understanding of what matters. The person who synthesises across these functions, who sees that a customer complaint pattern is connected to a supply chain issue that is connected to a regulatory change, is typically a senior leader with enough organisational tenure to have built relationships across silos.
AI systems that operate within functional silos cannot support context synthesis because they reproduce the fragmentation. A sales AI that optimises customer interactions without visibility into operations will optimise for promises the organisation cannot keep. An operations AI that optimises throughput without visibility into customer commitments will optimise for efficiency that damages relationships.
Context synthesis as a human skill becomes more valuable only when the AI system provides a foundation for it. That means the AI must operate across functional boundaries, surfacing connections that no individual function would see independently. The human then synthesises not from raw, unprocessed information scattered across departments, but from a structured layer that has already identified patterns across the organisation's operations.
This is an architectural choice, not a feature. It requires the AI system to be designed from the outset to ingest information from multiple sources, structure it into a coherent layer, and present cross-functional patterns to the humans whose job is to interpret them. Most AI deployments do not do this because they are scoped to individual functions or use cases. The context synthesis skill that the framework celebrates is, in practice, undermined by AI deployments that deepen functional silos rather than bridging them.
Judgement Improves with Better Options
Judgement, the third skill, is described as choosing the right option from those that AI creates. This framing assumes that AI's contribution is generating options and the human's contribution is selecting among them. In practice, the quality of human judgement is inseparable from the quality of the options presented.
A decision-maker choosing between three well-structured options, each with clear trade-offs, supporting data, and risk assessments, will make better decisions than one choosing between ten poorly framed alternatives with inconsistent information. The AI system's role is not just to generate options but to structure them in a way that makes human judgement effective. This means presenting trade-offs explicitly, flagging uncertainties, providing the reasoning behind each option, and making the decision criteria transparent.
This is where the concept of explainability moves from a compliance requirement to an operational necessity. A system that recommends an option without explaining why does not support human judgement. It is asking for human rubber-stamping. A system that presents three options with clear reasoning for each, identifies the assumptions behind each recommendation, and highlights where its confidence is low, is genuinely augmenting judgement.
The distinction matters because organisations that deploy AI for decision support often measure success by whether the human agrees with the AI's recommendation. Agreement rates of 90% or higher are celebrated as evidence that the system works. But high agreement rates can also indicate that the human has stopped exercising independent judgement and is simply deferring to the system. The J-PAL research from Ghana presented at the India AI Impact Summit 2026 showed exactly this pattern: human evaluators given AI recommendations were slowed down without improving their decisions. They were not synthesising AI input with their own judgement. They were either deferring or ignoring.
Genuine judgement support requires designing the system to present options in a way that activates human reasoning rather than bypassing it.
Strategic Instinct Cannot Be Delegated to a System
Strategic instinct, the fourth skill, is described as questioning whether AI is optimising for the right problem. This is the most consequential skill in the framework and the one least amenable to systematic support.
AI systems optimise. That is what they do. Given an objective function, training data, and feedback mechanisms, they will find increasingly efficient paths to the defined objective. The risk is not that AI optimises poorly. The risk is that AI optimises brilliantly for the wrong thing.
A customer service AI optimised for resolution time may learn to give customers quick answers that do not actually solve their problems. A manufacturing AI optimised for throughput may learn to prioritise volume at the expense of quality tolerances that the optimisation function does not capture. A financial services AI optimised for compliance may learn to reject legitimate transactions that fall outside the patterns it was trained on.
In each case, the AI is performing exactly as designed. The failure is in the design, specifically in the choice of what to optimise for. Strategic instinct is the human capacity to recognise that the optimisation target is wrong, that the metrics being maximised do not capture what actually matters, or that the objective has shifted since the system was designed.
This skill cannot be embedded in the AI system because it requires questioning the system's premises. But it can be supported by making those premises visible. An AI system that explicitly states its objective function, shows what it is optimising for, and reports on how its behaviour changes as it learns gives the human strategist the information needed to ask whether the optimisation is pointed in the right direction. A system that operates as a black box, producing optimised outputs without revealing its logic, makes strategic questioning almost impossible.
Trust Building Is the Constraint on Autonomy
Trust building, the fifth skill, is described as the human building relationships while AI handles communication. This framing is correct but understates the depth of what trust requires in an enterprise context.
Trust between organisations is not built through messages. It is built through consistent behaviour over time, reliable delivery on commitments, transparent handling of problems, and the demonstrated willingness to prioritise the relationship over short-term advantage. AI can draft communications, but it cannot make the decision to absorb a cost rather than pass it to a client because the relationship matters more. It cannot judge when transparency about a problem will strengthen trust and when the timing is wrong. It cannot read the political dynamics of a client organisation and adjust its approach accordingly.
In an AI-augmented environment, trust building becomes the governing constraint on how much autonomy the AI system is given. The system can handle routine communication. It can process standard requests. It can generate reports and updates. But the moment an interaction involves ambiguity, conflict, or the potential for misunderstanding, the human must be in the loop, not because the AI cannot generate a reasonable response, but because the trust the organisation has built with its counterpart depends on the quality of judgement applied in that moment.
This is why the progression from human-in-the-loop to human-in-command, discussed at length in the governance sessions at the India AI Impact Summit 2026, is not a technical transition. It is a trust transition. The AI system earns expanded autonomy by demonstrating that its handling of routine interactions is reliable enough that the human can focus on the interactions where trust is at stake.
How Mitochondria Builds for This
The five skills Elevation identifies are real and valuable. But they only compound within an AI system that is designed to support them.
ATP is built around this understanding. The Stimuli phase structures institutional knowledge that otherwise lives only in people's heads, creating the foundation for taste to be exercised efficiently rather than bottlenecked at senior individuals. The system operates across functional boundaries by design, providing the structured information layer that context synthesis requires. Every ATP deployment surfaces trade-offs and reasoning transparently, supporting genuine judgement rather than rubber-stamping. The objective function is defined collaboratively during design and revisited at each phase transition, making strategic questioning possible because the premises are always visible. And the progressive autonomy model, where the system earns expanded scope through demonstrated reliability, is how trust building is operationalised rather than assumed.
The framework describes what humans should be doing alongside AI. The architectural question is whether the AI system is designed to make those five skills effective, or whether it leaves them as individual capacities that the organisation hopes will somehow coexist with automation. Hope is not architecture. The skills become valuable only when the system is built to activate them.
—
Mitochondria builds ATP — agentic AI for operations. It learns your workflows, earns autonomy in stages, and runs with governance built in. Your data stays yours. Based in Amsterdam and Pune, working with organisations across Europe and India.