"We mapped problems to models. We mapped models to architectures. It was only a matter of time before we had to map problems to people"
And we are back to our regular programming. With seven things nonetheless, fully on brand. In Article 13, I introduced a framework for routing problems to the right AI model: characterise the problem first, then select the system. In Article 24, we completed the taxonomy by discovering Sparky, the divergent ideation “Dwarf” who had been hiding in plain sight. In Article 32, we elevated the routing logic from individual models to entire architectural typologies: Task Harnesses, Dark Factories, Metric Optimisation Engines, and Orchestration Frameworks. And it feels like yesterday, when we talked about the new capabilities coming down the pipe represented by the unreleased Mythos model. Each article has been an escalation of the same fundamental question: given this specific problem, what is the right system to point at it? Today, I want to complete the trilogy by turning the lens around entirely. We have mapped problems to models. We have mapped problems to architectures. Now we need to map the human competencies required to operate all of this. Because here is what I keep observing in transfer offices that have made progress on their AI journey: the technology works, the architecture is sound, and the team is standing in front of it comparable to a chef who has been given a professional kitchen but was never taught how to use the equipment. The bottleneck, as I warned in Article 28, is still you. But now I can be considerably more specific about what that means and how you can start closing those gaps.
1. Specification Precision, or the Art of Saying Exactly What You Mean
We have been building toward this since Article 18, when a certain CEO confessed that AI had accidentally made him a better manager, or where I argued that the unit of professional output has shifted from documents produced to intent expressed. Specification Precision is the formalisation of that insight into a discrete, teachable, assessable skill: the ability to communicate with absolute, literal clarity to systems that have no capacity for charitable interpretation.
Unlike your human colleague who has worked with you for seven years and can decode your vague gesture toward a pile of documents as "please prepare the technology characterisation using the standard methodology we discussed last quarter", an AI agent requires rigorous, exhaustive parameters. What is the scope? What are the boundary conditions? What constitutes an escalation? What does "done" look like? What does “good” or even “great” look like?
In technology transfer terms, this is the difference between telling an agent "evaluate this technology for commercial potential" and specifying: "Extract the key performance parameters from this patent document. For each parameter, identify the top three application domains using the materials science licensing database. For each domain, estimate the addressable market size using Statista and the OECD innovation indicators. Flag any domain where the estimated market exceeds 50 million euros annually and the competitive landscape includes fewer than five active players. Output in structured JSON with confidence scores for each estimate". The first instruction produces a smoothie of plausible-sounding generalities. The second produces actionable intelligence. The distance between them is not technical sophistication. It is Specification Precision. And this is not a rehearsal of prompt engineering. We already established early in this LinkedIn series, that prompt engineering used to move us forward, but it no longer does.
So what do we do about #1?
The biggest obstacle to Specification Precision is the "Curse of Knowledge": assuming the system knows what you mean. Sometimes domain expertise can stand in your way. The good news is, you can train for this. You can for instance use an "Adversarial Mirror" Exercise. Go ahead, google it. Whatever training you choose, it has to emphasise that precision is not about being "techy." It is about being a better Architect of Intent. If the AI's answer is "hallucinated" or "generic," do not blame the model's intelligence; look at the boundary conditions of your request. Did you define "good"? Did you define the "forbidden zone"?
2. Evaluation and Quality Judgement, or Knowing When the Machine Is Confidently Wrong
This competency connects directly to the Taste Repository from Article 21 and the judgement layer from Article 5. It is the ability to establish robust assessment frameworks for AI outputs, specifically the capacity to detect when a system presents incorrect information with absolute fluency and zero hesitation. And as we saw in yesterday’s emergency article, as models become more capable, THIS will be your main task supervising their work, as the errors will become increasingly trickier to spot.
We have all experienced this. An AI-generated market analysis that cites a competitor who does not exist. A state-of-the-art review that confidently references a paper that was never published. A royalty rate benchmark drawn from the pharmaceutical sector when the technology is clearly in specialty chemicals. The machine does not pause, does not hedge, does not exhibit the subtle uncertainty cues that a human colleague would display when operating at the edge of their knowledge. It always thinks it knows. It delivers the hallucination with the same serene confidence as the accurate analysis, and the only thing standing between that hallucination and a costly strategic error is the professional who can tell the difference.
Evaluation and Quality Judgement is not merely "checking the output". It is the systematic construction of verification protocols that the licensing manager, the patent analyst, and the technology scout can all apply consistently, based on explicit, shared criteria rather than individual intuition. This is where the Taste Repository earns its operational weight: the accumulated record of corrections, quality rejections, and the reasoning behind them becomes the institutional standard against which every output is evaluated. Without it, quality judgement remains trapped in individual heads, and every personnel change resets the institutional learning to zero.
So what do we do about #2?
If Specification Precision is about how you talk to the machine, Evaluation and Quality Judgement is about how you listen to it. Specifically, with a "trust, but verify" mindset that is systematically enforced. How can you train for this? Let us call the exercise "Red Team" Hallucination Hunt: Provide staff with an AI-generated report, for example for a commercial prospect. Before the meeting, a facilitator intentionally inserts two "synthetic" errors into the report: one fake company name that sounds plausible and one real company with an invented product line. Staff must audit the document using primary sources. Or you can establish "Chain of Verification" (CoV) Protocols. Go ahead, google it. Training should emphasise that in the AI era, the Licensing Manager’s value has moved. They are no longer the "author" of the first draft; they are the Editor-in-Chief and the Guarantor of Truth. Make scepticism a must. I know you will not have trouble finding AI sceptics in KTOs today. Tell your team: “If you did not find at least one thing to correct, you probably were not looking hard enough", to foster these practices in daily work. Remember, high-capability models are so fluent that they require more scepticism, not less.
3.     Task Decomposition and Algorithmic Delegation, or Managing the Tireless Intern at Industrial Scale
This is the competency I have been circling since Article 18, when I described AI adoption as fundamentally a management skill, and since Article 32, where we discussed how the Task Harness architecture fails when a human tries to supervise five agents simultaneously without a Planner layer. Task Decomposition is the advanced form of what every good manager does: breaking complex objectives into discrete, well-defined work packages. Except that the work packages are now being executed by systems that require a level of precision in their brief that no human junior colleague ever demanded.
In the context of the Orchestration Framework from Article 32, this competency becomes architectural. The professional is not just decomposing a single task but designing an entire workflow where the output of one specialised agent becomes the input for the next, where the hand-offs must be meticulously choreographed, and where the failure of any single node must be anticipated and handled gracefully. Consider the end-to-end licensing workflow: disclosure intake, technical characterisation, prior art analysis, market assessment, company scouting, term sheet drafting, compliance verification. Each stage requires a different decomposition strategy, a different set of inputs, a different quality threshold. The professional who can design this chain is not performing traditional project management. They are performing what we would call control logic design, and it is the highest-value activity in the entire architecture. To be honest, after yesterday’s emergency article, this may be one of the skills to deemphasise as models become more capable. However, when you are revising the work being done by AI agents, watching how the agent broke down the problem can also help inform your judgement about the correctness of the chosen solution approach.
So what do we do about #3?
While Specification Precision is about the sentence and Quality Judgement is about the result, Task Decomposition is about the system. How do you train this? Take a common KTO process (e.g., "Assessing a New Software Disclosure"). Ask the team to break it into 5–8 discrete "nodes". This is funny, because those of us in software development do this all the time. Or you could also do "Assembly Line" Audit (Inter-Node Testing). Go ahead, google it. As models get better at planning, your team's role shifts to "Goal Verification." They do not need to build every step, but they must be able to audit the "Control Logic" the AI proposes to ensure it does not violate institutional policy or legal constraints.
4.     Systemic Defect Diagnosis, or Why Your Pipeline Broke at Three in the Morning
As automated workflows scale, they encounter failure modes that are specific to AI systems and that no traditional IT training prepares you for. The taxonomy of failures is worth naming explicitly, because each one has a different signature and a different remedy.
Context degradation is what happens when an agent's performance deteriorates over a long operational session, producing increasingly incoherent outputs as the context window fills with accumulated state. If your Dark Factory pipeline from Article 32 runs a fifty-technology characterisation batch and the quality of the last ten is noticeably worse than the first ten, this is likely the culprit. Specification drift occurs when the agent incrementally deviates from its original mandate over successive iterations, a particularly insidious failure because each individual step looks reasonable but the cumulative trajectory has departed entirely from the intended destination. Sycophantic confirmation is when the system validates and builds upon flawed input data rather than flagging it, telling you what it thinks you want to hear rather than what is true. And then there are silent failures: the output looks semantically plausible, reads fluently, hits all the right structural notes, but is functionally incorrect. A technology characterisation that identifies the right application domains but assigns them the wrong market sizes. A company scouting report that lists real companies with fabricated technology profiles.
For a transfer office running automated pipelines, this competency is the difference between catching the error before it reaches the inventor and discovering it after the investor has read the business plan. In the power plant analogy, this is the engineer who can read the alarm panel and distinguish between a sensor malfunction and a real process deviation, and who knows which one requires immediate intervention.
So what do we do about #4?
If the previous skills were about the Training Manual (how to spot the errors), this is the Architectural Playbook (how to prevent them from happening in the first place). To stop these specific failure modes, a Technology Transfer Office (TTO) must train its staff to stop acting like "users" of a chat interface and start acting like Systems Reliability Engineers. You could introduce a TTO Defect Prevention Playbook and work out how to spot and prevent issues. Or you could have an "Alarm Panel" Taxonomy Workshop. Go ahead, google it. This competency is about Operational Resilience. As your KTO moves toward higher volumes of disclosures, the ability to distinguish between a "sensor malfunction" (AI hallucination) and a "process deviation" (a lack of market for a technology) becomes the office's most valuable risk-mitigation asset.
5.     Trust Architecture and Security Blueprinting, or Deciding What the Machine Is Allowed to Touch
This competency connects directly to the sovereignty thread running through this series since Article 14, and since Article 20, when the US Department of Defense demonstrated what happens when a government decides it wants unrestricted access to AI capabilities. Trust Architecture is about defining the precise operational boundaries between automated execution and human oversight, and it has both a technical and an institutional dimension.
The technical dimension involves calculating what security professionals call the "blast radius" of an error. If an agent autonomously sends a technology offer to an industrial partner using the wrong licensing terms, what is the worst-case consequence? If the system automatically classifies an invention disclosure and routes it to the wrong evaluation pathway, how far does the error propagate before someone catches it?
The institutional dimension is about encoding the answers to these questions into the architecture itself. Which actions are fully automated? Which require human confirmation? Which are completely forbidden for the machine? The professional who determines that company scouting can run autonomously but licensing term generation requires human approval, who specifies that pre-filing invention disclosures must never leave the sovereign infrastructure while published patent analyses can use commercial platforms, is performing an act of institutional design that will govern the office's operations for years. Get it right, and the system runs with the reliable autonomy that the agentic orchestration layer from Article 22 provides. Get it wrong, and you have built an expensive machine that produces errors at machine speed. So what do we do about #5?
If Task Decomposition is building the engine, and Systemic Defect Diagnosis is reading the dashboard, Trust Architecture and Security Blueprinting is designing the brakes, the seatbelts, and the steering locks. To train or develop this skill among staff you can design "Authorisation Gates", where for each Task you define clear AI Role, the Human Role and an Architectural Rule. You could also expose your team to a Data Sovereignty Triage Training, and yes you can go ahead and google that too. The most vital lesson here is that restricting the AI is not a sign of technological backwardness; it is a sign of operational maturity. The value of a KTO professional in the AI era is not just executing tasks faster. It is knowing exactly where to place the "firewalls" between the machine's speed and the institution's liability, which as we saw with the previous Article about Claude Mythos, will only become more important as more capabilities get deployed into these models.
6.     Contextual Architecture and Data Taxonomy, or Building the Digital Basement That Works
We have been here before. Article 3 was literally titled "You cannot build AI on paper files," and the digital basement has been a recurring theme across the entire series. But Contextual Architecture elevates the data problem from a cleanup exercise to a strategic design discipline.
This is not about digitising your patent portfolio. That is table stakes, and if you have not done it yet, I refer you back to Article 3 with the same urgency I expressed twenty-nine articles ago. Contextual Architecture is about structuring your institutional information specifically for machine retrieval. It is the discipline of building the data taxonomy that allows your agents to seamlessly access the right information at the right time, without being confused by irrelevant data, without missing critical context, and without the kind of data pollution that produces the smoothie of plausible-sounding nonsense I have been warning about since the beginning.
In practical terms, this means designing structured schemas for your technology profiles that an agent can query against explicit constraints, as we discussed in Article 29 when we talked about agent-first interfaces. It means categorising your institutional knowledge so that the Taste Repository corrections for materials science licensing do not contaminate the evaluation criteria for biomedical spin-offs. It means building the Agent Cards that declare your institutional capabilities in machine-readable form. And it means doing all of this with the sovereignty constraints from Article 14 firmly in mind.
So what do we do about #6?
If Task Decomposition is building the engine and Trust Architecture is designing the safety systems, Contextual Architecture and Data Taxonomy is about laying the pipeline for the fuel. For a KTO, the "fuel" is institutional knowledge. The hardest truth for a KTO to accept is that a shared drive full of 10.000 digitised PDFs is not a "digital basement", it is a digital landfill. An AI cannot effectively reason across unstructured, untagged prose without hallucinating or cross-contaminating contexts. So how do we train to improve this skill? KTOs naturally communicate in academic and legal prose. AI agents need structured parameters (schemas). Staff must learn to translate one into the other. Or you could conduct a "Blind Retrieval" Stress Test or RAG Auditing. And yes, google can probably help you figure this one out. The overarching lesson for your staff is that AI is only as smart as the data structure beneath it. Thus, your and your team’s job is no longer to just "store" files; your job is to build a library where the AI knows exactly which shelf to pull from.
7.     Computational Resource Economics, or Why Your AI Budget Disappeared Before Lunch
This is the competency that nobody in knowledge and technology transfer is currently thinking about, and that everyone will be thinking about within the next eighteen months. Computational Resource Economics is the senior architectural skill of conducting rigorous cost-benefit analysis for AI deployments, and it connects directly to the strategic calculus we discussed in Article 32 about when to deploy which architectural type.
When I argued that building an Orchestration Framework for twelve annual disclosures is a waste of resources, I was making a Computational Resource Economics argument. When I warned that a Dark Factory amplifies both your excellence and your negligence at the same speed, I was arguing that the cost of a badly specified pipeline is not just the direct compute expense but the downstream cost of correcting errors that compounded at machine speed. Every model inference costs money. Every agent workflow consumes tokens. Every Dark Factory batch run has a price tag that must be justified against the value it produces. If you are not currently maxing out your Claude quotas every day, you are not doing it right. We have the highest level of sponsored or subsidised intelligence of all time. Who knows how long this situation is going to last for?
For a transfer office planning its AI investments, this competency means being able to answer questions such as: is it more cost-effective to run this technology characterisation through a high-end reasoning model at a higher per-token cost, or through a cheaper model with more extensive human review? If we deploy a Metric Optimisation Engine to tune our agent-facing technology profiles, what is the expected return in terms of increased discovery rate, and does that return justify the computational cost of continuous experimentation? The professional who can answer these questions is not just an AI user. They are an AI economist, and their contribution to institutional strategy will become increasingly consequential as AI moves from experimental pilot to core operational infrastructure.
So what do we do about #7?
If Contextual Architecture is laying the pipeline, Computational Resource Economics is deciding whether the oil is actually worth the cost of extraction. In the current landscape, as frontier labs race to dominance, AI feels free or heavily subsidised. But as KTOs move from individual chat-like interfaces to automated, API-driven workflows, such as the "Dark Factory" model, every single step carries a micro-transaction, particularly if running with self-hosted sovereign models. If you build sloppy, token-heavy pipelines now, their operating budgets will implode tomorrow. How do you hone this skill? You can put your team on a "Token Diet" (Context Pruning Exercise). This exercise combats the costly habit of dumping massive, unedited documents into AI models by forcing staff to complete complex tasks under a strict word-count budget. By requiring them to manually extract and filter information beforehand, the approach teaches teams that precise data curation is an economic necessity for efficient AI operations. Or you could establish a Model Triage and The "Right-Sizing" Matrix, as a living document to provide guidance to your team. If you do not know what I am talking about, you know what to do. When the true cost of compute is passed down to enterprise users, the offices that know how to optimise their pipelines will thrive; the others will have to turn their AI off. The core lesson for your staff is that AI is not a software license; it is a utility bill. Every time you press "Enter," you are spending institutional money. Treat compute as a finite resource, not an infinite magic trick.
The Map: Where Skills Meet Problems
Now let me build the mapping I promised, because this is where the practical value lives. The question every KTO director should be asking is: given the seven types of problems my team faces daily, which competencies do I need in the room to address them with AI? So let us map these seven onto the other seven.
“Thinky” problems, those demanding novel convergent reasoning, require above all Specification Precision and Evaluation and Quality Judgement. The reasoning model is only as good as the specification it receives, and the output of a complex analytical task demands the kind of rigorous quality assessment that catches the subtle errors a frontier model can still produce at its reasoning limits.
“Sweaty” problems, the massive-volume tasks that bury your team under sheer quantity, require Task Decomposition, Contextual Architecture, and Computational Resource Economics. Breaking three thousand contract audits into agent-manageable batches, ensuring the data infrastructure supports retrieval at that scale, and calculating whether the compute cost justifies automating the entire backlog versus a targeted subset: these are the skills that turn a Sweaty mountain into a manageable pipeline.
“Dancey” problems, the coordination challenges of keeping multiple parties synchronised, require Task Decomposition, Trust Architecture, and Systemic Defect Diagnosis. Orchestrating a sixty-day spin-out timeline across the PI, external counsel, university legal, and a corporate sponsor is an exercise in designing handoff protocols, defining which decisions each agent or human owns, and detecting when the coordination pipeline has silently broken.
“Shrinky” problems, the emotional intelligence challenges that remain stubbornly human, require Trust Architecture above all else. Not because AI can solve them, but because the critical skill here is knowing what the machine must never touch. When the automated characterisation pipeline determines that a professor's technology has no commercial viability, the system must not deliver that verdict. It must flag it for human handling.
“Choosy” problems, the judgement and willpower challenges where professional courage matters more than analytical capability, similarly require Trust Architecture combined with Evaluation and Quality Judgement. The AI can prepare the analysis that informs the decision, but the decision itself, to accept or reject, to hold the line or compromise, remains human.
“Foggy” problems, the strategic ambiguity challenges where even the question is unclear, require Specification Precision, Evaluation and Quality Judgement, and Systemic Defect Diagnosis in combination. Three contradictory market reports fed into a reasoning model will produce a synthesis that sounds authoritative but may simply be averaging the contradictions rather than resolving them.
And “Sparky” problems, the divergent ideation challenges from Article 24, require Specification Precision and Contextual Architecture. This may seem counterintuitive for problems about creative lateral thinking. But "What industries could use this biofilm?" produces generic lists, while "This biofilm exhibits a shear adhesion strength of 15 MPa at temperatures up to 200 degrees Celsius in aqueous environments with pH ranges of four to nine: what industrial processes currently struggle with adhesion failure under precisely these conditions?" produces novel cross-domain connections worth pursuing. The Specification Precision drives the quality of the creative input. The Contextual Architecture ensures the model has access to the structured, cross-domain data that makes the lateral connections possible.
Now here this mapping in short form for convenience:
§ "Thinky" (Novel reasoning) ➡️ Specification Precision, Evaluation & Quality Judgement
§ "Sweaty" (Massive-volume tasks) ➡️ Task Decomposition, Contextual Architecture, Computational Resource Economics
§ "Dancey" (Coordination challenges) ➡️ Task Decomposition, Trust Architecture, Systemic Defect Diagnosis
§ "Shrinky" (Emotional intelligence) ➡️ Trust Architecture
§ "Choosy" (Judgement & willpower) ➡️ Trust Architecture, Evaluation & Quality Judgement
§ "Foggy" (Strategic ambiguity) ➡️ Specification Precision, Evaluation & Quality Judgement, Systemic Defect Diagnosis
§ "Sparky" (Divergent ideation) ➡️ Specification Precision, Contextual Architecture
What This Means for Your Team
Let me draw the practical conclusions, because the map is only useful if it tells you where to invest.
If your team is strong on Evaluation and Quality Judgement but weak on Specification Precision, you have people who can catch errors in AI output but who cannot specify the tasks well enough to produce good output in the first place. You are spending your quality budget on correcting problems that should never have occurred.
If your team has strong Task Decomposition skills but lacks Systemic Defect Diagnosis capability, you can design complex agentic workflows but you cannot maintain them when they break in production. This is the team that builds an impressive Orchestration Framework in the pilot phase and then watches it degrade over the following months because nobody can diagnose why the company scouting agent started returning irrelevant results after the patent database was updated.
If your team lacks Trust Architecture capability, you are either automating too much, exposing the institution to errors whose blast radius nobody has calculated, or automating too little, retaining human-in-the-loop requirements at every step because nobody has done the analysis to determine which steps need them. Both failure modes are expensive. The first costs you in errors. The second costs you in the very overhead we celebrated eliminating in Article 23.
If your team lacks Computational Resource Economics, you are making AI investment decisions based on enthusiasm rather than analysis. You are deploying Dark Factories for workflows that a Task Harness could handle. You are running frontier models on tasks where a mid-tier model would produce equivalent results at a fraction of the cost. You are building the articulated truck from Article 32 when a bicycle would have sufficed.
The honest assessment for most transfer offices is that the competency portfolio is uneven. Most teams have reasonable Evaluation and Quality Judgement, because that is what experienced professionals have been doing their entire careers, applied to human-generated rather than machine-generated output. Many have developing Specification Precision, particularly those that have taken the discipline of Articles 18 and 28 seriously. Few have Task Decomposition at the level required for multi-agent orchestration. Almost none have Systemic Defect Diagnosis, because the systems that produce these defects are too new for the failure patterns to have been widely encountered. And Computational Resource Economics is virtually absent everywhere, because the cost structures of AI deployment have not yet been a primary concern for teams still in the experimentation phase and what they are currently using, tends to run on simple inexpensive 20 dollar/month subscriptions.
Connecting Back to the Architecture
The skills do not float in abstract space. They inhabit specific layers of the automated transfer office from Article 22.
Specification Precision and Evaluation and Quality Judgement live primarily at the HMI layer, the control room where human professionals interact with the automated workflows. These are the skills the transfer manager exercises when reviewing the consolidated output of a Dark Factory batch run or when specifying the parameters for a new Task Harness deployment.
Task Decomposition and Trust Architecture live at the boundary between the HMI and the Agentic Orchestration layer, the design interface where human professionals define how the automated control system should operate. These are the skills of the control logic designer who determines what the system does autonomously and what it escalates.
Systemic Defect Diagnosis lives at the Agentic Orchestration layer itself, the operational monitoring capability that detects when the automated workflows are deviating from their intended behaviour. This is the alarm management skill of the AI-first transfer office.
Contextual Architecture lives at the Data Infrastructure layer, the foundation that feeds every other layer. Without it, the agents at the orchestration layer have nothing to work with, and the professionals at the HMI layer have nothing to review.
And Computational Resource Economics lives at the Institutional Strategy layer, where resources are allocated, investments are justified, and the business case for the entire AI infrastructure is maintained.
Every layer needs its competencies. Every competency needs its layer. And the transfer office that builds the full stack, from a well-architected digital basement through a properly specified control layer to a strategically governed business layer, staffed at each level with professionals who possess the right mix of these seven skills, is the office that will process at the throughput the sandwich from Article 31 demands while maintaining the quality standards that the Taste Repository encodes and the sovereign control that the geopolitical reality requires.
In a nutshell
The Seven Dwarfs taxonomy was designed to map problems to AI models, but the same framework maps the human competencies a transfer office needs to operate those models in production. Seven strategic AI competencies, Specification Precision, Evaluation and Quality Judgement, Task Decomposition, Systemic Defect Diagnosis, Trust Architecture, Contextual Architecture, and Computational Resource Economics, correspond to the seven problem types with structural precision. Thinky demands Specification and Evaluation. Sweaty requires Decomposition, Architecture, and Economics. Dancey needs Decomposition, Trust, and Diagnosis. Shrinky and Choosy require Trust Architecture to define what the machine must not touch. Foggy demands the combination of Specification, Evaluation, and Diagnosis to navigate ambiguity without false confidence. Sparky requires Specification and Contextual Architecture to drive quality divergent output. Most offices have reasonable Evaluation skills, developing Specification Precision, and almost nothing else on the list. The gaps map onto specific layers of the architectural model from Article 22, and addressing them requires deliberate investment in skills that do not develop from casual AI usage any more than control logic design develops from reading about electricity.
In my next article, we will keep going down the rabbit hole of AI safety and explore how that dimension interacts with the skills and the agent types. With the recent developments from Anthropic, this topic is going to be pushing further to the top of things we will need to discuss deeper moving forward. It is going to be fun. Until then, keep your competency gaps mapped, your training investments targeted, and your team development driven by the problem taxonomy, not the hype cycle. You can train for this.