AI Strategy

The Future of AI Work Needs Builders and Sensemakers

AI is not only changing the speed of work. It is changing how work should be designed, explained, and led.

Tim Hillegonds at Six06 Strategy recently published a thoughtful piece called "Forward-Deployed Engineers, Parallel Org Charts, and the Case for Hiring More Writers". It is worth reading because it gets at something many leaders are sensing but have not yet put into words: AI is not simply making work faster. It is changing the shape of work itself. That distinction matters. For the last couple of years, much of the AI conversation has centered on productivity. How much faster can we write, summarize, research, respond, code, analyze, or create? Those are useful questions, but they are not the deepest questions. The more important leadership question is this: if AI can now participate in the work, how should the work be designed in the first place? That is where Tim's article lands well. He argues that companies will need new roles and new ways of thinking about organizational design. In particular, he makes the case for two profiles: the internal forward-deployed engineer and the strategic writer. One makes AI real inside the business. The other makes the work understandable, aligned, and meaningful. I think he is right. And I would take the argument one step further. The Risk Is Automating the Old Org Chart Tim introduces the idea of a "parallel org chart," where each person in the organization has an AI agent counterpart. It is a useful mental model because it forces leaders to think beyond scattered tools and toward a more systematic view of AI adoption. But I would be careful about taking the metaphor too literally. The greatest value of AI will not come from cloning every existing role and giving each person a digital assistant. That may help around the edges, but it can also preserve the very friction companies should be trying to remove. If the current workflow is unclear, duplicative, political, slow, or poorly measured, adding AI to it may simply make the dysfunction move faster. The better opportunity is not to create a second version of the current organization. It is to redesign the work around outcomes. That means asking harder questions:

Those questions move the conversation from "How do we give everyone an agent?" to "How should this business operate now that AI is part of the operating system?" That is the real transformation. Why the Internal Forward-Deployed Engineer Matters The strongest part of Tim's argument is the case for the internal forward-deployed engineer. Most companies do not need a ceremonial Chief AI Officer as their first AI hire. They need someone who can sit close to the work, understand the business problem, build practical systems, and stay with the implementation long enough for adoption to happen. That last part is critical. AI strategy breaks down when it remains abstract. Leaders can approve a roadmap, buy tools, run workshops, and still fail to change the way work actually gets done. The gap is rarely imagination. It is execution. An internal forward-deployed engineer helps close that gap. Not because they are simply "technical," but because they combine technical capability with product judgment and organizational empathy. The best version of this role can:

That is a rare combination. It is also why the role is so valuable. The future of AI implementation will not be won by teams that only understand models, prompts, or software architecture. It will be won by teams that understand work. The companies that benefit most from AI will be the ones that can connect technical capability to operational reality. The Writer Is Not a Nice-to-Have Tim's second point may be even more important than it first appears: companies need more writers. I agree, with one clarification. The need is not for writers in the narrow sense of people who produce words. The need is for people who can turn strategic ambiguity into language clear enough for others to act on. That skill is becoming more valuable, not less. AI has made language cheap. It can generate emails, proposals, social posts, summaries, scripts, and strategy documents in seconds. But the abundance of language has not created an abundance of clarity. In many cases, it has done the opposite. Organizations are now surrounded by more content, more drafts, more messages, and more noise. That creates a premium on people who can think clearly, frame precisely, and communicate with judgment. A strong strategic writer does more than polish copy. They help an organization answer:

This is not communications as decoration. It is communications as operating infrastructure. In the AI era, language becomes one of the main ways strategy turns into action. If people cannot understand the strategy, they cannot execute it. If customers cannot understand the value, they will not buy it. If employees cannot understand why their work is changing, they will resist the change or quietly work around it. That is why Tim's case for writers matters. AI does not eliminate the need for human articulation. It raises the bar. Builders and Sensemakers The companies that win with AI will not be the ones that sprinkle agents across the org chart and call it transformation. They will be the ones that pair builders with sensemakers. Builders make the systems real. They understand the data, the workflow, the tooling, the integration points, the security concerns, and the user experience. They can move from idea to prototype to deployed capability. Sensemakers make the work coherent. They clarify the purpose, frame the strategy, name the customer value, align the organization, and help leaders communicate change in a way people can understand and trust. Both roles matter because AI transformation has two failure modes. The first is technical theater: impressive demos that never become reliable, adopted systems. The second is strategic fog: big language about transformation that never becomes a concrete change in how work gets done. Builders without sensemakers can create tools no one understands or adopts. Sensemakers without builders can create compelling narratives that never become operational reality. The advantage comes from putting them together. What Leaders Should Do Next For leaders trying to make this practical, I would not start by rewriting the org chart. I would start with a small number of important workflows and study them closely. Pick the places where AI could create meaningful leverage:

Then ask what the workflow would look like if it were designed today, with AI available from the beginning. Who needs to make decisions? What data do they need? Where does judgment matter? Where is the work repetitive? Where does quality break down? Where do customers or employees feel friction? From there, the need for new roles becomes more obvious. You may need an internal forward-deployed engineer. You may need a strategic writer. You may need both. But the starting point should be the work, not the title. A Better AI Conversation What I appreciate about Tim's article is that it moves the AI conversation away from tools and toward organizational capability. That is where the conversation needs to go. The question is no longer whether AI can help people work faster. It can. The more important question is whether leaders can redesign work thoughtfully enough to capture the value. That requires technical execution. It requires strategic clarity. It requires trust. It requires language. And it requires leaders to resist the temptation to treat AI as a layer on top of the existing business. AI should not just accelerate the way the company already works. It should invite the company to examine whether the work is designed well in the first place. That, to me, is the real opportunity inside Tim's argument. Hire the builder. Hire the sensemaker. But more importantly, create the conditions for them to help the business become more intelligent, more focused, and more human in how it operates.

• What decisions should move faster?
• Where is expertise trapped?
• Which workflows depend too heavily on manual coordination?
• What information does the team repeatedly recreate?
• Where does the customer experience suffer because internal systems do not talk to each other?
• What work requires human judgment, and what work simply requires structured execution?
• Translate a messy workflow into a buildable system
• Identify where AI should and should not be used
• Integrate tools into the company's real data and processes
• Prototype quickly without losing sight of governance and reliability
• Earn trust from the people whose daily work is changing
• Measure whether the system improved the business outcome
• What do we actually believe?
• What are we trying to become?
• Why should customers care?
• What tradeoffs are we willing to make?
• How do we explain this change to employees, customers, partners, and the market?
• What language will create alignment rather than confusion?
• Sales follow-up and proposal creation
• Customer onboarding
• Internal knowledge management
• Recruiting and candidate communication
• Financial reporting and forecasting
• Field service coordination
• Client delivery
• Marketing content operations
• Executive decision support