AHEAD’s new podcast, Ready, Set, Runtime, opens with a question AHEAD Field CTO and host Michael “MK” Kolbrener has been asking clients and colleagues for the better part of a year.
What do we actually mean when we say “AI-accelerated development”?
It’s a phrase that gets used constantly across the industry, and in our experience, rarely gets defined the same way twice. For the inaugural episode, MK sat down with Josh Perkins, VP of Emerging Technologies, and Craig Martin, VP of Advanced Engineering, to pin it down. The conversation offers a direct look at how AHEAD approaches this work with enterprise clients.
AI SDLC: Software is the entry point, not the destination
The instinct is to treat the AI Software Development Lifecycle (SDLC) as a faster version of the same thing: quicker code generation, shorter release cycles, more automated testing. That framing undersells what’s changing. The real shift is AI applied beyond code, with the same discipline across planning, development, testing, release, operations, security, and governance.
What our clients are building is closer to a digital workforce than a faster pipeline. That workforce is made up of agents that write user stories, respond to events, and build to specification, with humans shifting from doing the work to defining the outcomes it serves.
Software development is where this shows up first, largely because engineering teams adopt new tools faster than most functions and because code can be checked by machines in ways most work can’t. Software teams are the first to work out how to check what agents produce, when a person needs to step in, and how to measure results instead of effort. Every other function that puts agents to work will have to answer the same questions. Most will find it harder, because their work can’t be checked automatically. In underwriting, for example, you may not know whether a decision was right for years.
Discipline compounds, and so does its absence
One of the more direct points from the conversation is one we repeat often with clients. AI-accelerated development reveals whatever discipline already existed in an organization, then compounds it. Organizations without a standardized stack, mature DevOps practices, or clear architectural ownership need to address their maturity gaps more than ever. Those gaps get exposed faster and cost more once output is being generated at machine speed instead of human speed.
That’s a case for being honest about the starting point before you accelerate. The clients making the fastest, most durable progress are almost always the ones who’d already done the less visible transformation work before acceleration tools arrived. They’re ahead because agents build on what’s already there, and these teams were already well-prepared. it.
The right question has changed
For the last two or three years, most enterprise leadership conversations about AI have centered on a single question. Are you using it? But that question is already out of date. The one that matters is whether AI is driving an outcome the business cares about.
We’ve watched technology initiatives get justified by the technology itself, and they tend to struggle because no business owner is attached and there’s no clear way to know whether they worked. Engagements that produce real results start with a business problem, like expanding revenue, driving efficiency, or managing risk. AI should improve the solution, while the business problem remains the justification for the project. It’s a critical shift we encourage every client to make: treat AI as a lens for solving problems you already know you have.
The economics of technical debt have also changed. Retiring legacy systems used to mean hiring a team or bringing in an integrator for months, sometimes years, to rebuild something that already functioned. Most technology budgets go to keeping existing systems running, and legacy systems account for much of that spend. Replacing massive enterprise applications is time-consuming, expensive, and challenging to ensure the new platform has the same feature set as the platform its replacing. Agentic workforces change that math, and for many of our clients, that’s where the most immediate value is sitting today.
What we’re watching next
Two threads from this conversation are shaping how we’re advising clients through the rest of the year.
The first is context. Enterprises are sitting on enormous volumes of data, and much of it isn’t organized in a way an agent can use well. Feeding a system everything available and hoping the right signal surfaces is slow and expensive. Giving agents only the knowledge a task requires, at the point they need it, is what cuts costs and makes them useful faster. The underlying work is the same knowledge management and data stewardship problem enterprises have been working on for two decades. The difference now is that there’s a deadline.
The second is security. Recent incidents involving agents from OpenAI and Google broke out of environments their owners believed were contained and reached systems they were never meant to touch. None of these involved an attacker turning AI against a company. They were agents doing things their owners didn’t intend. For our clients, that settles the order of operations. An agent that can’t be contained, monitored, and stopped doesn’t go into production, however useful it is. Security is the gate every other conversation waits behind.
Where the series goes from here
This first episode was intentionally about framing.
Before getting into tactics, we wanted to establish what AI SDLC means, because most of the industry conversation skips straight to tools without doing that work first. The next episode moves from framing to practice, with AHEAD partners walking through what practice looks like inside real client engagements, including the parts that didn’t go to plan the first time around.
We built Ready, Set, Runtime! to have these conversations in the open. If this first episode leaves you with a sharper question than the one you came in with, it did its job.
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