From scattered pilots to strategic systems: why clear authority matters more than speed when AI Agents make everything possible.
Recently, a product team at a Series B company showed me three versions of the same feature. They’d been sitting in Figma for two weeks because nobody could decide which one to build.
The team was divided on which direction to go, with product leaning towards one design and engineering advocating for another. Leadership said “use your judgment” which meant nobody wanted to own the call. Meanwhile, the actual launch date slipped by a month.
This tension between product, engineering and leadership isn’t new. I’ve seen it at startups with 10 people and Fortune 500s with thousands.
Recently, a product team at a Series B company showed me three versions of the same feature. They’d been sitting in Figma for two weeks because nobody could decide which one to build.
The team was divided on which direction to go, with product leaning towards one design and engineering advocating for another. Leadership said “use your judgment” which meant nobody wanted to own the call. Meanwhile, the actual launch date slipped by a month.
This tension between product, engineering and leadership isn’t new. I’ve seen it at startups with 10 people and Fortune 500s with thousands.
Recently, a product team at a Series B company showed me three versions of the same feature. They’d been sitting in Figma for two weeks because nobody could decide which one to build.
The team was divided on which direction to go, with product leaning towards one design and engineering advocating for another. Leadership said “use your judgment” which meant nobody wanted to own the call. Meanwhile, the actual launch date slipped by a month.
This tension between product, engineering and leadership isn’t new. I’ve seen it at startups with 10 people and Fortune 500s with thousands.
What’s different now is that AI has made this dynamic significantly more expensive and exceedingly more apparent. When you can generate three versions of anything in an afternoon with AI, the bottleneck isn’t production anymore. It’s decision authority. And most organizations haven’t figured out who actually has it.
Two Roles Are Emerging Whether You’ve Assigned Them or Not
Here’s what I’ve learned across dozens of engagements: there are fundamentally two roles operating in every organization right now, regardless of what anyone’s title says.
Understanding which role you’re in, and which role your team members are in, determines whether AI or Agents make you faster or just creates more things to argue about.
The first I call “Deciders.” These are the people who define intent. They set constraints. They make the irreversible calls about priorities, acceptable trade-offs, and what should and should not be used.
A decider wouldn’t say “use your judgment.” Instead, they say things like “we’re optimizing for shipping speed this quarter” or “customer data privacy is non-negotiable, even if it limits what the product can do.”
The second role is “Interpreters.” These are the people who turn vagueness into work. When constraints aren’t set, they guess what leadership wants. They make judgment calls about priorities that should have been decided upstream. And they absorb the risk of getting it wrong.
Interpretation is often done by some of your most capable people. But it’s expensive because the interpreter is carrying decision-making risk without decision-making authority.
What Happens When Decisions Stay Murky
I recently advised a company that had an incredible team of engineers who could ship features in mere days, but unfortunately were stuck spending weeks in refinement cycles. Product leadership kept saying “make the product less buggy and more seamless” without defining what less buggy and more seamless actually meant for their Agents.
This left that engineering team trapped in interpretation mode, because the decision about acceptable quality thresholds had never been made. They’d build something, show it, get vague feedback from the business or customers, rebuild it, show it again. While these cycles were executed quickly, the product never actually moved forward and was stuck in a perpetual “never done” Agent demo posture.
What’s different now is that AI has made this dynamic significantly more expensive and exceedingly more apparent. When you can generate three versions of anything in an afternoon with AI, the bottleneck isn’t production anymore. It’s decision authority. And most organizations haven’t figured out who actually has it.
Where Interpretation Hides in Your Organization
If you’re reading this and realizing your organization is too heavy on interpretation, here’s where to look. It shows up in predictable patterns:
The approval loop that won’t close. Work gets delivered, reviewed, revised, and re-reviewed. Not because there’s anything wrong with it, but because the original ask was ambiguous enough that “right” remains a moving target.
The over-explained decision. Someone writes three paragraphs justifying a straightforward choice. In this case they’re often compensating for missing constraints. They’re doing interpretive labor, building a case for why their guess aligns with unstated priorities, because the bounds weren’t set upfront.
The AI or Agent output that triggers debate instead of action. A team uses ChatGPT to generate campaign copy or Claude to draft a technical spec. Instead of picking one and moving forward, everyone weighs in on which version feels better. Nobody has clear authority to decide, so the output becomes another thing to interpret collectively.
These are clear signs that decision-making authority hasn’t been made explicit. And in an AI-accelerated environment, that ambiguity gets expensive fast.
What Deciding Actually Looks Like
So what does it mean to operate as a decider in an AI-driven organization?
It means setting constraints before work starts. You define what “done” looks like, what trade-offs are acceptable, and where the boundaries are. You’re not micromanaging execution, but you’re providing the frame that lets people move with confidence.
I like to call this the “AI Bookends” framework for decision-making for every workstream that is AI enabled or when you are building the product / engineering systems to ship AI or Agents themselves.
It also means being explicit about priorities. If everything is important, nothing is. AI can optimize for speed, quality, cost, or user experience, but it can’t determine which one matters most in a given context (or worse yet, it decides arbitrarily and the entire output is anchored on what it believes is the highest priority–”governance” anyone?) That’s your job as a decider.
The most effective deciders I’ve worked with don’t wait for questions to surface. They front-load the constraints. Before a project starts, they establish what’s non-negotiable. “This ships by the end of Q1, even if features get cut.” Or “We’re prioritizing technical foundation over user-facing polish this sprint.”
When those boundaries are clear upfront, teams don’t need to waste time and resources interpreting intent or second-guessing priorities. They can execute and use AI tools to explore options that actually fit within the parameters you’ve set.
When AI Exposes What Was Already Broken
In an AI-first organization, somebody has to own the call about what matters, what’s negotiable, and where the line is. If that authority isn’t explicit, then AI won’t actually make your teams any faster. They will just continue to spin their wheels until the problem becomes impossible to ignore.
The organizations that are already scaling AI and Agents effectively aren’t the ones with better tools or bigger budgets. They’re the ones where decision authority was already clear. AI just gives them leverage because their people can explore options confidently instead of guessing which direction leadership actually wants.
If your organization doesn’t work that way, AI or any number of Agents certainly won’t fix it. Rather it will expose it now more than ever.
So the question isn’t whether you’re ready for AI. It’s whether your organization will continue being stuck interpreting or move into a mode where decisions are actually made. AI just exposes and surfaces the lack of decision-making sophistication in your organization; get ahead of it before it results in a standstill or, worse yet, avoidable politics.
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Nick Talwar is a CTO, ex-Microsoft, and a hands-on AI engineer who supports executives in navigating AI adoption. He shares insights on AI-first strategies to drive bottom-line impact.
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