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AI produces business value only when it is fitted to a real operating problem and carried through adoption. A capable model can still fail when the team misunderstands the workflow, lacks usable data, ignores exceptions, cannot run the system reliably, or asks AI to do something it cannot do. Operational expertise connects the model to customer needs, frontline practice, infrastructure, ownership and measurable outcomes.
That is the central argument in Pratap Rao’s article published by Cybersecurity Insiders on August 6, 2026 (the same article was listed by TechSpective on September 21, 2026). Rao’s point is not that operational knowledge replaces engineering. It is that technical capability and operating knowledge must work together from problem selection through rollout.
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What does operational expertise mean in AI deployment?
Operational expertise is the practical knowledge of how work is actually done: the decisions employees make, the systems they use, the service levels they must meet, the exceptions that break a neat process and the outcomes customers or colleagues value. In an AI project, it also includes the ability to translate that knowledge into requirements, data definitions, controls, training and ownership.
It is broader than having a subject-matter expert attend a kickoff meeting. A useful operating team stays involved while the problem is framed, data is prepared, outputs are tested, workflows are redesigned and users adopt the result. A support manager, for example, may know that an escalation is triggered not only by keywords but by a combination of sentiment, customer history, product tier and regional communication preferences. Those details can determine whether an apparently accurate classifier is useful in practice.
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Rao captures the distinction in the sentence, “Technology alone does not create transformation.” His related observation is that “The challenge is no longer access to AI. The challenge is deployment.” The implication is a change in emphasis: select and shape use cases around durable business or service problems instead of treating every new model capability as a project.
Why do technically impressive AI projects fall short?
RAND’s 2024 report, based on interviews with 65 data scientists and engineers who each had at least five years of AI or machine-learning model-building experience, identifies five recurring causes. These are reported failure patterns, not a formula that predicts the outcome of every project.
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|---|---|---|
| The problem is misunderstood or miscommunicated | The model optimizes a proxy while the business needs a different result; success criteria change during development. | Can a frontline user and a technical lead state the same problem, decision and desired outcome? |
| Data is inadequate | Records are incomplete, inconsistently labeled, inaccessible or unrepresentative of the cases the system will see. | Do we have permissioned, traceable data for normal cases and important exceptions? |
| The team prioritizes technology over the user problem | A novel model is selected first, then a weak use case is created to justify it. | Would this problem still matter if the newest model were unavailable? |
| Infrastructure is inadequate | A prototype works in a notebook but cannot meet latency, security, integration, monitoring or availability requirements. | Can the organization operate the system at the required volume and risk level? |
| The problem exceeds AI’s practical limits | The task requires judgment, missing context, accountability or physical action that the proposed system cannot reliably provide. | Which decisions should remain human, and what evidence would justify automation? |
RAND also cautions that “AI is not a magic wand that can make any challenging problem disappear; in some cases, even the most advanced AI models cannot automate away a difficult task.” The report recommends that technical staff understand the project’s purpose and domain context, that organizations pursue enduring problems rather than novelty, and that leaders invest in data governance and deployment infrastructure while assessing AI’s limits.
How should an organization choose the right AI problem?
Start with the outcome and workflow, not with a model catalogue. A use case is a strong candidate when the problem is important, recurring, measurable and compatible with the information and decisions available to the system.
- State the operational problem. Describe who is affected, what decision or task is slow or inconsistent, and what happens when it goes wrong. “Use generative AI in support” is too vague; “reduce the time agents spend finding approved answers while preserving escalation rules” is testable.
- Define the desired outcome and baseline. Record the current handling time, error pattern, backlog, conversion, quality score or other relevant measure. A baseline prevents a pilot from claiming success merely because users like the interface.
- Map the workflow end to end. Identify inputs, handoffs, approvals, systems of record, service-level constraints and points where work leaves the documented path.
- Separate assistance from automation. Decide whether AI should retrieve information, recommend an action, draft content, prioritize work or execute a transaction. The required controls and evidence differ sharply by role.
- Check durability. Prefer a problem likely to remain important after a particular model or vendor changes. Technology can be swapped; a well-defined operational need should persist.
- Set a stop condition. Define the evidence that would show the approach is unsafe, too costly, too inaccurate or less useful than a simpler process.
This sequence keeps the team from confusing a demonstration with a deployment. It also makes it easier to explain why a promising capability should not be used in a particular workflow.
What workflow knowledge and frontline input are required?
Frontline employees often know where formal process maps are incomplete. Their observations can reveal escalation triggers, shifts in customer sentiment, regional communication preferences, bottlenecks and exception paths that are rare in a dataset but consequential in service.
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Ask users to walk through recent cases that required judgment or a workaround. Document the signal they noticed, the action they took, the information they needed and the risk of choosing incorrectly. These examples can become evaluation cases, routing rules or explicit human-review points.
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Make domain language explicit
Teams should agree on definitions for terms such as “urgent,” “resolved,” “qualified,” or “high risk.” If different regions or departments use the same label differently, the data may combine incompatible outcomes. A glossary and ownership for each field are simple forms of operational governance.
Design feedback into the interface
Users need a clear way to correct an output, explain why it was wrong and indicate whether the correction should affect future evaluation. Feedback that disappears into a chat box cannot reliably improve the process. The product owner should decide which feedback is treated as a safety issue, a data-quality issue or a model-quality issue.
Is data and infrastructure readiness really a gating factor?
Yes. A model cannot compensate for unavailable, unauthorized or poorly defined data, and a successful prototype is not evidence that the organization can run a dependable service. Readiness should be checked before scaling rather than discovered after users depend on the system.
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- Data provenance: Know where each important field originated, when it was last updated and whether its use is permitted.
- Coverage and quality: Test missing values, conflicting records, label consistency and representation of the regions, products and customer groups affected.
- Access controls: Limit who and what can retrieve sensitive information, and log material actions and outputs.
- Integration: Confirm that the system can read required inputs and return an output to the workflow’s system of record without manual re-entry.
- Reliability: Establish expected response time, availability, capacity, fallback behavior and a process for handling outages or degraded model performance.
- Monitoring: Track data drift, error categories, escalation rates, user overrides and operational outcomes, not only model scores.
- Change management: Assign a process for updating prompts, models, policies, data pipelines and user guidance while preserving an audit trail.
These controls are not solely an IT concern. Operations must specify what “good enough” means for the service, security and customer experience; technical teams must explain what the architecture can and cannot guarantee.
How can leaders test whether AI can solve the problem?
Feasibility is a joint judgment about technical performance and operational consequences. A high benchmark score does not establish that a system can make the required decision in the organization’s actual context.
Test representative cases
Build an evaluation set from ordinary work, difficult cases and known exceptions. Keep a holdout set that is not used to tune the system. Review false positives and false negatives by business impact: a low-frequency error may be unacceptable if it causes a safety, privacy or regulatory incident.
Define the human role
Specify when a person must approve, correct, investigate or take over. Human review needs enough context and time to be meaningful; merely placing a “human in the loop” after an opaque recommendation does not guarantee control.
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Compare with simpler alternatives
Rules, search, better data entry, workflow automation or a revised policy may solve the problem with less complexity. AI is justified when it improves the outcome enough to warrant its operational, security and maintenance burden.
Check limits openly
Document conditions under which the system should abstain, defer or provide a warning. RAND’s warning that some tasks cannot be automated away is a practical design requirement, not an argument against using AI where it fits.
What does workflow redesign and adoption involve?
Deployment changes how people make decisions and how work moves between systems. The organization must redesign the surrounding workflow instead of dropping an AI output into an unchanged process.
McKinsey’s 2026 State of AI survey reports that 44% of respondents say AI is scaling across their enterprise, while 37% attribute at least some organizational EBIT impact to AI. Those are respondent-reported survey results, not audited causal estimates. McKinsey’s description of higher-performing respondents associates them with pursuing growth or innovation as well as efficiency, redesigning workflows enabled by AI, and backing deployments with leadership commitment and operational rigor.
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For each output, specify who receives it, what action follows, what evidence is required and how an exception is escalated. Remove duplicate data entry and clarify which system is authoritative.
Train for judgment, not button-clicking
Training should include normal cases, failure examples, uncertainty signals and the procedure for overriding or reporting an output. Supervisors need guidance on coaching people who appropriately reject a recommendation, not just on maximizing usage.
Align incentives and capacity
If employees are measured on speed while the AI adds review work, adoption will be superficial. Adjust staffing, service targets or quality measures where necessary, and give users time to learn the new process.
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Who should own AI from design through adoption?
Named ownership prevents a pilot from becoming an orphaned tool. One executive may sponsor the outcome, but responsibility must be assigned across the lifecycle.
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| Business owner | Defines the outcome, priority, acceptable risk and budget; decides whether the result merits expansion. |
| Operational or domain lead | Explains workflow realities, exceptions, terminology and user acceptance criteria. |
| Technical lead | Chooses and integrates the approach, documents limits, reliability and security requirements, and maintains the system. |
| Data or governance lead | Controls provenance, access, quality, retention, evaluation and audit evidence. |
| Adoption lead | Coordinates training, communications, feedback, support and measurement of sustained use. |
| Risk, legal or compliance reviewer | Assesses obligations and escalation thresholds appropriate to the domain. |
Ownership should continue after launch. Models, data, policies and user behavior change; optimization and adoption are operating responsibilities, not closing tasks on a project plan.
How should organizations compare AI implementation approaches?
There is no universal vendor or model ranking that guarantees results. Compare an approach against the same operational criteria so that a polished demonstration does not outweigh a poor fit.
| Comparison axis | Questions to ask | Evidence to request |
|---|---|---|
| Problem importance | Does it address a costly, risky or strategically meaningful problem? | Baseline, target outcome and executive owner. |
| Workflow fit | Does it handle handoffs, exceptions and regional or departmental variation? | Process map, exception cases and user walkthroughs. |
| Data and infrastructure | Can it access trustworthy data and operate within security, latency and availability needs? | Data inventory, architecture plan, controls and fallback design. |
| Domain knowledge | Are frontline experts involved throughout testing and change management? | Named participants, acceptance tests and feedback process. |
| Technical feasibility | Are accuracy, uncertainty and failure modes acceptable for the decision? | Representative evaluation, error analysis and abstention rules. |
| Ownership and measurement | Who maintains it and how will adoption and business impact be measured? | Lifecycle responsibilities, dashboard definitions and review cadence. |
RAND supports the problem, data, infrastructure and feasibility dimensions; McKinsey’s survey supports attention to workflow redesign and operational rigor. Neither source supplies a guaranteed implementation recipe.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which metrics show whether deployment is creating impact?
Use a small set of measures that connect system behavior to the intended outcome. Track quality and risk before declaring financial impact.
- Outcome: the business or service result the project was created to improve.
- Process: cycle time, backlog, handoff count, resolution rate or rework.
- Quality: accuracy by case type, customer experience, override rate and escalation quality.
- Adoption: eligible cases using the workflow, repeat usage, training completion and reasons for non-use.
- Risk: privacy incidents, unsafe outputs, policy violations and unresolved complaints.
- Economics: operating cost, avoided cost, revenue or margin contribution, with assumptions stated clearly.
Review these measures by region, customer segment, product and exception type where relevant. An average can improve while a vulnerable group or high-severity case worsens.
How should a deployment be phased?
A phased rollout limits exposure while producing evidence from real work. The sequence below can be adapted to the risk and complexity of the use case.
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- Frame: agree on the problem, baseline, users, constraints and stop conditions.
- Prepare: establish data access, definitions, security controls, evaluation cases and an operating owner.
- Prototype: test the smallest useful workflow with representative and exception cases; record failure modes.
- Shadow: let the system generate outputs without changing decisions, so the team can compare it with current practice.
- Controlled launch: introduce the workflow to a limited group with explicit human review, support and rollback procedures.
- Evaluate: inspect outcome, process, quality, adoption and risk measures against the baseline.
- Scale or stop: expand only when the evidence and operating capacity support it; otherwise redesign, narrow the scope or retire the approach.
- Operate: monitor drift, feedback, incidents, costs and policy changes, then update the system through a governed process.
How should leaders interpret AI-project failure statistics?
Failure statistics need provenance and definitions. RAND’s 2024 report describes “more than 80% of AI projects fail” as an estimate cited from an earlier source; it is not a failure rate measured by RAND’s 65 interviews. The article also repeats an 85% estimate attributed to Gartner and a projection of up to US$15.7 trillion in potential global economic contribution by 2030 attributed to PwC, but those exact figures are not independently established here and should not be presented as verified facts without checking the original dated publications and their scope.
The practical lesson does not depend on a dramatic percentage. Projects can fall short through poor problem definition, weak data, inadequate infrastructure, unrealistic expectations or lack of adoption. Treat any headline number as a prompt to inspect definitions, population, date and measurement method.
What is the central lesson for an AI program?
Operational expertise does not make success automatic, and it does not replace engineering, security or governance. It supplies the context that lets those disciplines solve the right problem, within the limits of the actual workflow, with people accountable for the result.
Rao’s thesis is therefore best read as a deployment discipline: begin with a meaningful outcome, involve the people who perform the work, verify data and infrastructure, test AI’s limits, redesign the workflow and keep named owners responsible for adoption and improvement. That is how an innovation becomes an operating capability rather than an impressive but isolated demonstration.
Frequently Asked Questions
Can a small organization apply this approach without building its own AI model?
Yes. Operational discipline is independent of whether the technology is built internally, configured from an existing service or supplied by a partner. A small team can still define the workflow, test representative cases, assign an owner and require clear controls and fallback behavior.
How long should an AI pilot run before a decision is made?
Run it long enough to cover the workflow’s normal operating cycle and meaningful exception cases, rather than choosing an arbitrary calendar length. The decision point should be tied to baseline comparisons, safety evidence, user feedback and the team’s ability to support the process.
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What should managers do when employees distrust AI recommendations?
Invite skeptical users into case selection and evaluation, publish known limitations and make overrides straightforward. Track why people reject outputs; repeated, well-founded overrides may indicate a workflow or data problem rather than a motivation problem.
Does operational expertise reduce the need for data scientists and engineers?
No. Technical specialists are still needed for architecture, evaluation, security, integration and maintenance. Operational experts make sure that technical work targets the right decisions and reflects how the organization actually serves customers or performs its mission.
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