Friday, June 26, 2026
FRIDAY – AI FOR THE C SUITE®
Read time: 12-13 min · Read online
Hi, it’s Chad. Every Friday, I serve as your AI guide to help you navigate a rapidly evolving landscape, discern signals from noise and transform cutting-edge insights into practical leadership wisdom. Here’s what you need to know:
1. Sound Waves: Podcast Highlights
The risk you didn’t put on the register. You’ve planned for outages. You’ve budgeted for price hikes. Neither playbook covers what happened on a Friday night in June: a government letter landed at 5:21pm, and within hours Anthropic had to take its two most capable models dark for everyone, paying enterprise customers included, with no notice. The model didn’t fail. The vendor didn’t fail. A regulator simply switched it off. This week on AI for the C Suite®, I break down why a “regulatory shutoff” now belongs on your risk register as its own line item, what it means for any workflow built on a single frontier vendor, and the questions to raise before your next board meeting. If your AI strategy assumes your best tool will always be there, this one is worth your time. Hit one of the below links to check it out:
Apple · Spotify · iHeart · Amazon · YouTube
Subscribe for free today on your listening platform of choice to ensure you never miss a beat.
2. Algorithmic Musings: The Difference Between a Prompt and a System Is Structured Context
What a recent research paper on AI ontologies reveals about the projects you’ve already built.
Several times each day I hand an AI assistant a four-word instruction, “See attached and execute.” It might involve a transcript for analysis, a strategic document or a dense academic paper. 99% of the time, what comes back is exactly what I need. With academic papers I receive a finished, validated business deliverable: a formatted report, embedded charts, a disclosure section, the correct brand colors, the right voice for the audience. No follow-up questions. No clarifying back-and-forth.
That result has almost nothing to do with the cleverness of the prompt. Four words can’t carry that much intent. It works because everything the assistant needs was already sitting around the prompt, written down and waiting to be pulled in: what the deliverable should contain, who the audience is, which colors and which tone, what counts as finished. Remove that surrounding material and the same four words produce something generic and roughly useless.
That gap, between the prompt you type and the context that makes it work, is the subject of a recent paper from a research team spanning the company Gamaizer and two French universities, From Prompts to Context. (See this week’s “Research Roundup” below for summary and link). Their argument is straightforward and worth internalizing for anyone deploying AI in a serious workflow: the disappointing results people get from AI usually trace to missing context rather than a weak model. And context can be engineered.
The researchers build a formal model, an ontology, that records the tasks, roles, resources, and constraints behind a piece of work, then pulls that recorded context into each prompt automatically. The mechanics are technical. The takeaway is simple. When the team compared ordinary prompting against prompts enriched with retrieved context, plain prompting left most of the relevant detail unstated, while the structured approach made nearly all of it explicit. Same model, same people, very different inputs.
You may already be doing this. If you run a well-built project in a tool like Claude, you’ve started building exactly what the paper describes. You just might not be treating it that way.
Two principles you already follow
The paper distills its approach into three design principles. A good AI project delivers two of them almost by default.
The first is to make context explicit instead of assuming the model can infer it. In a Claude project, this is what your governing documents, your standing instructions, and your saved preferences are doing. They state, in writing, the things you’d otherwise have to repeat in every conversation. The four-word prompt I described earlier only works because a set of files behind it specify the report structure, the word counts, the palette, and the standards for what “done” means.
You can watch this principle fail in real time when the context is missing. The paper documents an incident where an AI generated a component for a user profile page, assumed a generic database schema, and ignored the project’s actual fields. The fix cost the team about half a day. The same failure mode shows up in ordinary business work. Ask an assistant for a branded chart without telling it your brand colors, and it will confidently invent its own. The assistant isn’t wrong to do that. It’s filling a gap you left open. Structured context is what closes the gap before it becomes rework.
The second principle is to treat that shared context as a common reference point, what the researchers call a boundary object, something both the humans and the AI consult and can correct. This is the quiet strength of a project file. The brand standards a person reads are the same brand standards the model reads. There’s no translation layer where intent gets lost, and no single person holding the rules in their head. The document is the agreement, and everyone works from it.
Most teams build these two things without naming them. The value of the paper is that it gives you the vocabulary to see what you’ve built and to build it on purpose.
The principle you have to construct yourself
The third principle is the one a Claude project won’t hand you.
It’s provenance by default: every significant output should be linked, automatically, to the prompt that produced it, the sources that informed it, and the person or system responsible for it. The goal is that a reviewer can later reconstruct exactly how a result came to be by following explicit connections, rather than reassembling the story from memory and scattered chat logs. Responsibility becomes a matter of record instead of recollection. For any work that might later be audited, questioned, or defended, that’s the difference between a clean answer and an uncomfortable one.
A standard Claude project doesn’t give you this out of the box. It makes your context explicit and shared, which is most of the battle, but it doesn’t maintain a queryable trail tying each deliverable back to the exact inputs and decisions that produced it. If someone asks six months from now why a particular report framed a finding the way it did, the project alone won’t answer. You’ll be reconstructing.
So the accurate claim is narrower than “your AI project already does what this research describes.” Your project delivers two of the three principles by default, and the last is a discipline you have to add. Knowing which is which is what separates a team that is in control of its AI workflows from one that only feels in control.
The cost is keeping it current
The researchers are candid about the price of all this structure, and so should anyone selling it internally. A shared context model is only as good as its upkeep. The paper records a case where a resource changed but the model wasn’t updated, so the prompt carried an outdated reference and a person had to step in and correct the output.
This isn’t a hypothetical for me. In the very project I’ve been describing, a saved preference and a governing standards file disagree about a small detail of how a brand name should be written. The preference says one thing, the authoritative file says another, and until a human resolves it, every run quietly inherits the conflict. That’s the paper’s limitation reproduced exactly, in miniature, in a live system. Structured context doesn’t eliminate maintenance. It relocates the work from re-explaining yourself in every conversation to keeping one shared model accurate. That’s a far better trade, but it’s still a job, and it needs an owner.
The ladder you can climb
The paper also describes three levels of working with AI, and they read as a maturity ladder. At the lowest level, the AI is a tool: tightly instructed, doing bounded subtasks under close supervision. In the middle, it’s a partner: proposing options and shaping intermediate decisions while you frame the goal. At the top, it operates as an autonomous agent: you set high-level goals and constraints, and it plans and executes multiple steps with light intervention.
The four-word prompt sits at the top of that ladder. Most people run their AI projects at the bottom, instructing every step, and assume that’s simply how the technology works. It isn’t. The thing that lets you climb is the quality of the structured context behind the prompt. The more your tasks, standards, and constraints are written down and kept current, the less you have to specify in the moment, and the more you can safely delegate. The prompt gets shorter as the system gets richer.
What to do with this
For a leader deploying AI across real work, the research points to a short, practical list.
Write the context down once and treat it as an asset rather than a chore. The hours you spend specifying tasks, standards, and constraints in a durable place are hours you stop spending in every individual conversation.
Put the rules where both people and the AI can see them. A shared, inspectable document is worth more than tribal knowledge, because it can be corrected before errors propagate rather than after.
Assign an owner to keep that context current. Staleness is the predictable failure, and it scales with how fast your underlying work changes. Match the rigor to the stakes: light upkeep at milestones for low-risk work, tighter synchronization where mistakes carry cost.
Keep a person on substance. Structured context improves what goes into the AI. It doesn’t guarantee what comes out, which can still look right and miss the deeper requirement. Review remains the control that matters.
Start with one workflow that matters, prove the approach, and earn the right to delegate more. The research grounds its claims in a single bounded project rather than a broad rollout, and that restraint is a model worth copying.
The difference between a prompt and a system is structured context. Most of what AI can do for your work is waiting on the context you’re willing to write down and keep current. A better model is rarely the thing standing in the way.
A note for the builders
If you operate these projects rather than just commission them, the provenance gap is the part worth your attention, because it’s the one principle the platform won’t hand you.
The research captures provenance semi-automatically by pulling from code repositories, issue trackers, and generated outputs, then linking each artifact to its task and responsible agent. You can approximate a useful fraction of that without any of the machinery.
A few lightweight habits get you most of the way. Version-stamp your governing files, so you always know which edition of the rules produced a given output. Keep a short decision log alongside the project that records consequential choices and why they were made, rather than leaving the reasoning buried in a conversation that will scroll away. When you produce a deliverable, record the inputs and the file versions that fed it, even if only in the filename or a one-line header. If you run the same pipeline repeatedly, maintain a simple tracker that logs each run, its source, and its outputs.
None of this is a knowledge graph, and none of it needs to be. Provenance isn’t about technical elegance. It earns its place by letting you answer, later and with confidence, how a result came to be and who stood behind it. The platform gives you explicit, shared context for free. The trail back from the output is the part you build, and for anything you might one day have to defend, it’s the part that earns its cost.
3. Research Roundup: What the Data Tells Us
TRACEABLE AI: CAN YOU EXPLAIN HOW YOUR AI GOT THE ANSWER?
An AI tool produces something, you ship it, and nobody can reconstruct how it got made. New research builds a fix. It attaches structured context to every request, so each output carries a record of the task, sources, rules, and owner.
The numbers that matter: The study ran one real software project across six phases. Plain prompts left most of the task-linked context unstated. The structured approach made nearly all of it explicit. Same request, a trail instead of a black box.
What this means for your Monday morning: When an auditor, regulator, or board member asks how an AI decision was made, “we trust the tool” is not an answer. This framework turns responsibility into something you can query rather than something you have to remember. For a firm without a big compliance team, that trail keeps you audit-ready cheaply.
The catch: This is a single case study, not a benchmark, and the authors claim no speed or productivity gains. They’re honest about the cost: the context model goes stale. In one case a resource changed, the model lagged, and someone fixed it by hand. Upkeep is permanent.
Action item: Ask your AI vendor this: when your tool produces an output, what record does it leave of inputs, sources, and owner? If the answer is none, that’s your governance gap.
Read our full analysis of this research at AI for the C Suite®.
4. Radar Hits: What’s Worth Your Attention
Home Depot launched one AI assistant to two audiences. One loved it, one revolted. Magic Apron, the retailer’s gen AI shopping helper, won over DIY consumers and bombed with the pros. It kept asking questions too basic for people who do this for a living, so Home Depot yanked the pro version offline to retune it. The takeaway if you’re rolling out an assistant: one model rarely serves every user segment well. Test by audience before you trust the launch.
The new way engineers run AI agents can quietly blow up your compute bill. “Loop engineering” is this month’s hyped idea. Essentially, it’s a shift from typing prompts to building loops that run agents on autopilot until a task finishes. Boris Cherny, who leads Claude Code at Anthropic, says he no longer writes prompts. Yet there’s a catch: loops burn far more compute than one request, and they fail silently. If your team is adopting agentic coding tools, ask who owns the compute budget and who can kill a runaway agent.
OpenAI is preparing to roll out bidirectional voice mode for ChatGPT this week. The upgrade lets the model listen and talk at the same time, absorb interruptions without freezing, and hold context across a full conversation. That fixes the robotic, turn-by-turn feel that’s kept AI voice agents out of serious customer-facing work. Real-time translation is reportedly next. If you’re scoping a voice support tool or about to sign a vendor, don’t lock into a multi-year deal yet. The bar just moved.
5. Elevate Your Leadership with AI for the C Suite®
Most teams can make their AI context explicit and shared. Almost none can answer, six months later, exactly how a given output got made. If your team is heading toward an audit, a board question, or a regulator, that provenance gap is worth closing before someone asks. My Q3 schedule is full, but I’m taking a limited number of consultation slots now for engagements starting in Q4 2026 or Q1 2027. If that timeline fits a problem you’re already seeing, reply and let’s get it on the calendar. And if this issue was useful, forward AI for the C Suite® to the person on your leadership team who owns the AI decisions.
A quick note: there will be no edition next Friday, July 3, 2026, in observance of the Independence Day holiday. See you the following week.
Stay safe. Stay healthy. Be strong. Lead well.
Chad