As generative AI lowers the cost of producing plausible first-pass answers, organizations need to rethink where expertise, verification, judgment and accountability create value.

Generative AI has made it dramatically easier to produce an answer.

That does not mean every answer is correct, useful or decision-ready. It means the cost of producing something that looks like an answer—a summary, recommendation, analysis, draft, forecast, explanation or plan—has fallen sharply.

That distinction matters.

A recent interview with Đỗ Mai Anh, founder of Vietnamese education company MAAS Edtech, surfaced the issue particularly clearly. MAAS is responding to generative AI by moving toward research and publishing advisory while also developing tools around structured academic evaluation. Its Grade Check service assesses a student’s work against the assignment brief and marking rubric rather than simply producing another piece of fluent text. In the TNGlobal interview, Mai Anh argued that the difficult part increasingly lies in determining whether an apparently credible output actually meets the relevant standard.

That is an EdTech example, but the underlying question applies much more broadly.

If organizations can generate research summaries, business cases, campaign ideas, product concepts, financial explanations, market analyses and recommendations in seconds, what becomes scarce?

The answer is unlikely to be simply “human expertise.”

AI can improve expert work as well as novice work. Research with customer-support agents and management consultants has documented substantial productivity gains from AI assistance under the right conditions. But the benefits are uneven, and the boundary between tasks where AI improves performance and tasks where it can make performance worse is not always obvious.

The more useful proposition is therefore narrower:

As generating plausible answers becomes cheaper, more of the value in consequential decisions may move toward framing the right question, supplying context, evaluating evidence, detecting error, exercising judgment and carrying accountability.

That has implications for how organizations design AI-enabled work.

Key Takeaways

Generative AI is reducing the cost and time required to produce plausible first-pass outputs, but generation is only one part of a decision process.
Research shows that AI can materially improve productivity and quality on some knowledge tasks while degrading performance on others.
Expertise remains important partly because someone must recognize when a task, source, assumption or AI output is outside the system’s reliable operating range.
Fluency, confidence and completeness should not be confused with evidence quality.
The relative value of framing, context, verification, judgment and accountability increases as raw generation becomes easier to obtain.
Organizations should determine the appropriate role for AI according to both the consequence of being wrong and the difficulty of independently verifying the output.
High-value AI adoption should redesign decision systems rather than simply accelerate answer production.
The important management question is no longer only “Can AI do this task?” but “Which parts of this decision should AI perform, which should people verify, and who remains accountable for the outcome?”

Cheap Answers Are Not the Same as Cheap Decisions

An answer and a decision are different products.

An answer can be generated from a prompt.

A consequential decision usually requires several additional steps:

determining what problem is actually being solved
identifying relevant evidence
understanding context and constraints
generating alternatives
evaluating evidence quality
comparing tradeoffs
deciding under uncertainty
accepting responsibility for the outcome
learning from what happens next

Generative AI can participate in many of these stages.

But participation does not mean the stages disappear.

This is especially important because the output most visible to users—the answer—is often only the final representation of a much larger reasoning problem.

A polished market-entry recommendation, for example, might still depend on:

incomplete market data
an incorrect assumption about the customer
stale competitor information
an unrealistic distribution model
an unverified regulatory interpretation
an objective that leadership never clearly defined

Improving the prose does not repair those weaknesses.

The economics of answer production may therefore be changing faster than the economics of decision quality.

AI Can Create Real Productivity Gains

The argument for judgment should not depend on pretending AI is less capable than it is.

In a large field study of customer-support work, researchers found that access to generative AI increased productivity on average, with particularly large gains among less-experienced and lower-skilled workers. The researchers interpreted part of the effect as AI helping diffuse practices associated with stronger performers.

A separate experiment involving 758 Boston Consulting Group knowledge workers found substantial improvements on tasks that fell within the tested AI system’s capabilities. Participants with AI completed more tasks, worked faster and produced higher-quality results on those tasks.

That evidence matters.

AI can make knowledge work better.

But the BCG experiment revealed another part of the problem.

For a complex task deliberately positioned outside the system’s capability frontier, participants using AI were 19 percentage points less likely to produce the correct solution than participants without it. The researchers described this uneven boundary as a “jagged technological frontier”: tasks that appear similarly difficult to a person may sit on very different sides of AI capability.

The practical challenge is therefore not simply adoption.

It is knowing when assistance is improving the work and when it is creating a more persuasive route to the wrong answer.

The New Scarcity May Be Evaluation

When producing an acceptable-looking draft required substantial time and expertise, generation itself carried economic value.

Now organizations can generate:

dozens of campaign concepts
multiple positioning alternatives
competitive summaries
preliminary customer segments
financial scenarios
product requirements
research syntheses
strategic recommendations

at extremely low marginal cost.

That creates abundance.

But abundance creates another problem: someone still has to decide what deserves belief, attention and investment.

The bottleneck moves.

A company that can generate 100 strategic options does not automatically possess a better strategy.

It may simply possess 100 things requiring evaluation.

This is one reason AI and decision intelligence should be considered together. A stronger generation capability increases the importance of designing the system that evaluates what is generated.

The Praxable Decision Value Stack

A useful way to understand this shift is to separate the stages of AI-assisted knowledge work.

1. Frame

What question actually needs to be answered?

Framing determines:

objective
decision
scope
time horizon
constraints
stakeholders
acceptable risk

AI can help refine questions.

But leadership still has to determine which question is commercially consequential.

A company deciding whether to launch a product does not merely need an answer to:

Is there consumer interest?

It may need to determine:

Is there enough addressable demand, at achievable acquisition economics, among customers we can serve better than available alternatives?

Those are different questions.

2. Ground

What context must the system understand?

Generic intelligence becomes more valuable when it is connected to specific evidence.

That evidence may include:

customer conversations
product usage
financial performance
market conditions
competitor behavior
operational constraints
previous decisions
company capabilities
regulatory requirements

The MAAS example is useful here.

Its Grade Check proposition does not merely ask a model whether an assignment is “good.” The service uses the student’s draft together with the actual assignment brief and rubric, anchoring evaluation to a defined standard. MAAS explicitly presents the resulting grade as an estimate rather than an official academic mark.

The broader lesson is not that every organization needs a rubric.

It is that context changes the quality of the question an AI system is being asked to answer.

3. Generate

What can AI produce efficiently?

This is where generative systems are strongest and where organizations often begin adoption.

Typical uses include:

summarization
drafting
classification
ideation
comparison
extraction
synthesis
scenario generation
preliminary analysis

Generation matters.

But as it becomes cheaper, it becomes less useful as the sole source of differentiation.

The competitive question becomes what the organization can do with the generated output.

4. Verify

How do we determine whether the output is supported?

Verification may require:

checking primary sources
recalculating numbers
validating citations
testing assumptions
comparing against customer behavior
consulting a specialist
reproducing the analysis
running an experiment

NIST’s AI Risk Management Framework emphasizes evaluation, measurement and risk management rather than assuming that model outputs are trustworthy by default. Its generative-AI guidance is specifically intended to help organizations identify and manage risks associated with deploying these systems.

Verification becomes more important when a plausible error is inexpensive to produce but expensive to act upon.

5. Judge

What does the evidence mean for this particular decision?

Judgment involves more than determining whether an individual statement is factually correct.

Leadership may need to decide:

whether the evidence is sufficient
whether uncertainty is acceptable
whether an opportunity fits strategy
whether one source should outweigh another
whether an outlier represents noise or an emerging signal
whether to act, test, scale or wait

This is where business experimentation, market intelligence, customer intelligence and domain expertise become complementary to AI generation.

Judgment converts evidence into commitment.

6. Own

Who carries responsibility for the outcome?

An AI system can recommend.

It cannot absorb the organizational consequences of a bad capital allocation, product launch, hiring decision, compliance failure or strategic bet.

Accountability therefore remains part of the decision architecture.

The OECD’s 2026 review of generative-AI experimentation in government found that human oversight and accountability recur across national guidance. The report describes approaches that preserve the ability for meaningful human intervention and distinguish responsibility through concepts including answerability, auditability and liability.

This does not require a human to manually perform every task.

It requires clarity about who can explain, challenge and take responsibility for an AI-supported decision.

7. Learn

What does the outcome teach the organization?

AI-enabled decisions should generate new evidence.

Did the recommendation work?

Was the forecast accurate?

Which assumption failed?

Where did the AI help?

Where did human review catch something important?

Which verification step added no value?

Without that feedback, organizations may scale AI usage without improving their understanding of where the technology actually works.

That turns an AI implementation problem into an organizational learning problem.

The AI Judgment Matrix

Organizations need a practical way to determine how much AI autonomy and human review a task deserves.

Two variables are particularly useful:

How consequential is an incorrect output?
How easy is the output to verify independently?

	Easy to verify	Hard to verify
Low consequence if wrong	Automate — use AI extensively with lightweight checks	Assist — use AI, but sample and review outputs
High consequence if wrong	Validate — AI can do substantial work, but require explicit verification and ownership	Judge — keep the decision human-led; use AI for evidence, alternatives and challenge

The matrix is deliberately simple.

It does not classify entire professions.

It classifies tasks and decisions.

A lawyer might use AI aggressively to reformat notes but require independent verification for legal authorities.

A product team might automate the clustering of thousands of customer comments but retain human judgment over whether the resulting pattern justifies a roadmap investment.

A marketing team might generate dozens of creative variations automatically while imposing stronger review on a claim that creates regulatory or reputational exposure.

The appropriate operating model changes with the task.

Why Verification Difficulty Matters

Organizations often focus on the probability that AI will be wrong.

That is only one dimension of risk.

The second is whether someone can recognize the error.

Consider two AI-generated outputs:

Output A: Convert these figures from dollars to euros using the specified exchange rate.

Output B: Determine whether an unfamiliar decline in customer retention is temporary, competitive, behavioral or structural.

Both can be wrong.

But Output A is easy to check.

Output B requires evidence, interpretation and potentially significant market intelligence and customer context.

The second problem deserves more oversight even if the model appears equally confident.

This is why domain expertise may remain valuable even when AI performs much of the underlying work.

Expertise helps determine whether an answer is self-verifying or merely persuasive.

Confidence Is Not Evidence

Generative systems introduce another management problem: fluent outputs can make uncertainty difficult to perceive.

Research on human-AI decision-making increasingly focuses on calibration—whether users rely on AI appropriately rather than simply trusting it more or less.

A 2024 study of AI-assisted decision-making found that improving users’ calibration of their own confidence could improve human-AI team performance and encourage more appropriate reliance on AI.

More recent experimental work examining advice from generative AI found that exposure to AI advice could increase confidence even when participants did not thoroughly verify the output.

The operational lesson is important:

A decision system should not treat confidence—human or artificial—as a substitute for evidence.

Organizations need mechanisms that force important outputs back toward evidence.

These may include:

source requirements
confidence ranges
independent calculations
explicit assumptions
dissenting interpretations
review thresholds
experiments
escalation rules

The objective is not skepticism for its own sake.

It is calibrated reliance.

Expertise May Change Before It Disappears

There is a tempting binary around AI and expertise:

AI replaces experts.

Or experts remain indispensable.

Neither framing is particularly useful for designing an organization.

AI may reduce the value of some expert activities while increasing the leverage of others.

A specialist who previously spent hours:

compiling information
formatting analysis
writing standard explanations
retrieving known procedures

may increasingly delegate those activities.

Their higher-value contribution may shift toward:

detecting unusual cases
recognizing missing evidence
defining evaluation criteria
interpreting ambiguity
challenging assumptions
resolving contradictions
understanding consequences
taking responsibility for recommendations

This does not prove that expert employment, compensation or market power will necessarily increase.

Those are separate economic questions.

It suggests something narrower:

The composition of valuable expertise can change when generation becomes abundant.

The Business Decision Underneath AI Adoption

Many organizations still evaluate AI by asking:

Where can we use it?

That question encourages tool deployment.

A stronger question is:

Where does AI change the economics or quality of a decision enough to justify redesigning the workflow?

That leads to very different analysis.

Consider a commercial team evaluating a new market.

AI might reduce the cost of:

collecting initial information
translating sources
organizing competitors
summarizing reviews
generating hypotheses
drafting scenarios

The organization could then redirect scarce human attention toward:

determining which sources are credible
interviewing customers
challenging the assumptions
comparing strategic options
estimating downside
deciding whether to invest

The benefit is not simply faster research.

The potential benefit is more attention available for the parts of the decision that deserve judgment.

That outcome, however, is not automatic.

If the organization uses the saved time simply to generate more output, it may become faster without becoming better.

A Practical AI Decision Review

Before placing AI inside an important workflow, leadership teams can use the following template.

Decision

What decision will this work influence?

Consequence

What happens if the output is materially wrong?

AI Role

What exactly will AI do?

retrieve
summarize
classify
generate
analyze
recommend
execute

Evidence

What information is the AI expected to use?

Verification

How can the output be independently checked?

Expertise

What domain knowledge is required to recognize a plausible error?

Judgment

Which part of the process still requires interpretation or tradeoffs?

Accountability

Who owns the final decision?

Escalation Trigger

What uncertainty, anomaly or level of consequence requires additional review?

Feedback

How will actual outcomes improve the process next time?

What Should Organizations Automate First?

The strongest candidates generally combine:

high repetition
clear inputs
defined standards
inexpensive mistakes
easy verification
strong feedback loops

The weakest candidates generally combine:

ambiguous objectives
incomplete evidence
unusual situations
difficult verification
major downstream consequences
unclear ownership

This does not mean AI should be excluded from difficult decisions.

It may be extremely useful there.

But its role may be to:

broaden the evidence
challenge a hypothesis
identify alternatives
surface inconsistencies
model scenarios

rather than make the final commitment.

The distinction matters.

Implementation Checklist

Identify the business decision before selecting the AI use case.
Separate answer generation from decision-making.
Document the evidence the AI is expected to use.
Determine the consequence of a materially wrong output.
Determine how easily the output can be independently verified.
Identify where domain expertise is required to recognize error.
Define which tasks AI can automate and which require review.
Require stronger verification as consequence and ambiguity increase.
Separate model confidence, user confidence and actual evidence.
Assign a human owner for consequential decisions.
Establish escalation triggers.
Measure quality and business outcomes, not only time saved.
Record where AI materially improved or degraded the decision.
Update the workflow as AI capabilities and organizational evidence change.

Common Mistakes

Treating Fluent Output as Evidence

A polished explanation may still rest on poor assumptions or unsupported information.

Putting Human Review Everywhere

Mandatory human approval adds little when reviewers cannot realistically detect the error. Oversight should be designed around verification, not ceremony.

Measuring AI Adoption by Usage

More prompts, users or generated documents do not establish business value.

Asking Experts to Reperform the AI’s Work

The objective should be to move expert attention toward verification, exceptions and judgment—not create duplicate workflows.

Automating a Bad Decision Process

AI can accelerate an unclear workflow without improving it.

Using One Review Standard for Every Task

The appropriate level of oversight should change with consequence and verifiability.

Assuming Expertise Means Doing Everything Manually

Expertise may become more valuable precisely because AI allows specialists to spend less time on routine production.

Assuming AI Eliminates the Need for Domain Knowledge

When users cannot evaluate the output, access to more sophisticated generation can increase rather than reduce exposure to plausible errors.

Frequently Asked Questions

Is AI making expertise more valuable?

Not universally. AI may reduce the value of some expert tasks while increasing the leverage or importance of others. The strongest case is for expertise involved in framing problems, evaluating evidence, recognizing errors, interpreting unusual conditions and taking responsibility for consequential decisions.

Does AI reduce the value of knowledge?

AI can make access to information and routine explanations much cheaper. But knowledge remains important when users need to evaluate whether the generated output is correct, relevant or appropriate to a particular context.

What is the difference between an AI answer and an AI-supported decision?

An answer is an output. A decision requires connecting evidence to an objective, weighing uncertainty and tradeoffs, selecting an action and accepting the consequences.

When should AI make decisions automatically?

Automation is easiest to justify when consequences are limited, rules or standards are clear, outputs are easy to verify and strong monitoring exists.

When should humans remain responsible for the decision?

Human ownership becomes more important as consequences increase, evidence becomes ambiguous, verification becomes difficult and value judgments or strategic tradeoffs become material.

Why does AI sometimes make experts worse?

AI performance is uneven across tasks. Research on knowledge workers has shown that reliance on AI outside the system’s capability frontier can reduce accuracy even among highly skilled professionals.

Does human review solve AI risk?

Not automatically. A reviewer must have enough information, expertise and time to identify an error. Human review without meaningful verification can become procedural rather than protective.

What is confidence calibration?

Confidence calibration concerns whether confidence corresponds appropriately to actual correctness. In AI-assisted work, both excessive and insufficient reliance can reduce performance.

How should companies measure AI productivity?

Time savings matter, but organizations should also measure output quality, error rates, customer or business outcomes, rework, escalation rates and whether the resulting decisions improve.

What should organizations teach employees about AI?

Beyond tool operation, employees need to understand evidence quality, model limitations, verification, task selection, escalation and when domain expertise should override or challenge an AI-generated recommendation.

Will AI replace experts?

The evidence does not support one universal answer across professions and tasks. Current research shows significant productivity gains in some forms of knowledge work and performance deterioration in others. The economically important question is how the bundle of tasks performed by experts changes over time.

Final Thoughts

Generative AI has made producing an answer easier.

That is already valuable.

But organizations do not ultimately compete on the number of answers they can generate.

They compete on the quality of the products they build, investments they make, customers they understand, markets they enter and decisions they execute.

As generation becomes abundant, the management problem moves downstream.

Which question deserves attention?

Which evidence should be trusted?

What context is missing?

Where is the system likely to fail?

When is additional verification worth its cost?

What should the organization do?

Who owns the consequence?

Those questions are harder to automate because they depend on the particular decision, evidence and consequences involved.

The opportunity is therefore larger than replacing human work with cheaper generation.

It is to redesign knowledge work so AI performs more of what it can do well while scarce human attention moves toward the parts of the decision where judgment has the highest economic value.

That is a much harder organizational problem.

It is also where the largest gains may ultimately be found.

Research Sources

TNGlobal / Discover Vietnam Tech — MAAS Edtech interview. The August 18, 2026 interview with founder Đỗ Mai Anh provided the initial market signal for this article and MAAS’s argument about expertise, evaluation and AI-generated answers.
MAAS Edtech — Grade Check. Primary product information on the use of assignment briefs, rubrics and criterion-level feedback.
Brynjolfsson, Li and Raymond — Generative AI at Work. Field evidence on productivity effects and differences across worker experience levels.
Dell’Acqua et al. — Navigating the Jagged Technological Frontier. 2026 Organization Science publication reporting experimental evidence from knowledge workers using GPT-4.
NIST AI Risk Management Framework. Guidance on risk, measurement, evaluation and trustworthy use of AI systems.
OECD — Generative AI Experimentation in Government. 2026 review covering experimentation, verification, human oversight, accountability and evaluation.
Ma et al. — Human Self-Confidence Calibration in AI-Assisted Decision Making. Experimental research on confidence calibration and appropriate reliance.
Colombatto, Rintel and Tankelevitch — Metacognition and Confidence Dynamics in Advice Taking from Generative AI. Research on confidence and reliance when people receive generative-AI advice.