Many of today’s enterprises are approaching AI the wrong way.
After an explosion of pilots and experimentation, organizations are discovering that scaling AI requires far more than deploying models or launching use cases. The real bottleneck is the enterprise itself — legacy operating models, fragmented engineering practices, and systems never designed for AI-driven ways of working.
In this article, Alok Mirchandani, PwC US Scaled Engineering Services Leader, explains why the next phase of AI transformation will likely be defined not by experimentation, but by industrialization — where platform thinking, engineering velocity, and measurable business outcomes separate leaders from laggards.
Q: “Scaled engineering” is a term that gets used frequently, but not always clearly. How do you define it, and what does it look like in practice?
A: Scaled engineering is often misunderstood as adding more people to solve a problem. In reality, scale has very little to do with headcount. It’s about designing architectures, platforms, and operating models that help increase velocity, adaptability, and repeatability across the enterprise.
The focus shifts from effort to outcomes — how quickly you can move from idea to business value, and how consistently you can repeat that process at scale.
In practice, that means building composable, API-driven platforms; aligning cloud, data, and AI strategies; and engineering for continuous evolution rather than one-time deployment. In the AI era especially, the organizations that can win will be those that industrialize innovation — turning experimentation into scalable, measurable business outcomes.
What makes this work in practice is that PwC’s engineering capability was built on top of an advisory foundation. This means we bring business transformation context, sector expertise, and risk management into engineering execution, not just sprint velocity. That combination is what separates scaled engineering from a traditional staff augmentation or outsourcing model.
Q: Many organizations struggle to move beyond pilots when it comes to AI and digital initiatives. What typically breaks down when they try to scale?
A: Many organizations don’t struggle with AI experimentation — they struggle with AI industrialization.
What typically breaks down is that companies focus on isolated use cases instead of reimagining end-to-end business processes and operating models around AI. A pilot may succeed technically, but if it’s disconnected from the broader enterprise system, it won’t scale.
Another common issue is treating AI as a series of projects rather than a continuous transformation capability. That creates fragmented architectures, duplicated efforts, and unclear ownership.
The organizations scaling successfully are the ones thinking beyond pilots — building platform-based engineering models, aligning business and technology outcomes, and creating repeatable systems that turn experimentation into enterprise-wide impact.
Q: What must be in place, from a technical and organizational perspective, before scaling is even possible?
A: Before organizations can scale AI, they should stop thinking in terms of isolated applications and start thinking in terms of systems.
Technically, that starts with a platform-first architecture — modular, API-driven, composable, and designed for continuous evolution. Just as important is a strong data foundation with modern platforms, governance, and pipelines that can support AI at enterprise scale.
Organizationally, the operating model has to evolve as well. AI cannot scale in siloed teams or fragmented decision structures. Organizations should have tighter alignment between business, engineering, data, and risk teams, along with the ability to move quickly from experimentation to operational execution.
Ultimately, scaling becomes possible when architecture, data, and operating models are designed together, not as separate transformation efforts.
Q: How does scaled engineering change traditional delivery models, particularly in environments that rely on managed services or outsourcing?
A: Traditional delivery models were built for predictability, scale of labor, and long transformation cycles. But AI is compressing timelines and raising expectations around speed, adaptability, and measurable outcomes.
Scaled engineering shifts the model from large, linear programs to continuous, iterative value delivery. The focus becomes how quickly organizations can move from idea to production and scale outcomes — not how many resources are deployed.
It also changes the role of managed services and outsourcing. Clients are no longer looking for labor-heavy models; they want engineering ecosystems that combine platform capabilities, automation, AI, and targeted expertise to accelerate business outcomes. The value is no longer in capacity alone; it’s in velocity, flexibility, and the ability to industrialize innovation at scale.
Q: Where are you seeing the most meaningful impact from scaled engineering today, and what are organizations still getting wrong?
A: We are seeing the greatest impact where organizations are using scaled engineering to help unlock value from existing cloud, data, and AI investments — not just deploy new technology.
Many enterprises have already modernized infrastructure, but now the focus is shifting to engineering productivity, platform efficiency, AI-enabled development, and faster time to value. AI, in particular, is starting to drive measurable gains across the software development lifecycle.
Where many organizations still struggle is treating cloud, data, and AI as separate transformation agendas. In reality, they are deeply interconnected. Scaling AI without modern engineering platforms or strong data foundations creates fragmentation and limits impact.
The organizations moving fastest are the ones approaching this as a unified engineering and operating model transformation — not a collection of disconnected initiatives.
Q: PwC often points to the gap between strategy and execution as the biggest barrier to value. How does scaled engineering help close that gap?
A: The gap between strategy and execution exists because many of today’s organizations are still operating with disconnected planning, engineering, and business models. Strategy moves in one direction, execution in another.
Scaled engineering closes that gap by combining speed, industrialization, and outcome alignment into a single operating model. It allows organizations to move quickly from idea to production, prove value faster, and scale successful outcomes in a repeatable way.
Just as importantly, it brings together engineering, domain, data, and advisory capabilities into integrated teams rather than siloed functions. That alignment is imperative in the AI era, where business value depends not just on building technology, but on operationalizing it at enterprise scale.
Q: What decisions or actions should leaders be taking now to prepare for scaling AI effectively?
A: Leaders should stop viewing AI as a collection of experiments and start treating it as an enterprise capability that should be engineered for scale.
That starts with aligning around outcomes, not activity. The goal is not to launch more pilots — it’s to create repeatable business impact. To do that, organizations should invest early in the foundations that enable scale: platform-based architectures, modern data ecosystems, and operating models built for speed, iteration, and cross-functional execution.
Most importantly, leaders should act with urgency. AI is compressing competitive advantage cycles dramatically. The organizations that win won’t necessarily be the ones experimenting the most — they are often the ones that industrialize and operationalize AI the fastest.
The gap between strategy and production is where many AI programs stall — and where the real competitive advantage is won. Click here to explore how PwC’s Scaled Engineering Services helps enterprises build, scale, and execute at the speed AI demands.