AI everywhere, except at scale

Ask almost any operator where they are with AI and the answer sounds impressive. Hundreds of potential use cases. Dozens of pilots in play. Multiple foundation models, thousands of agents, several data platforms, and a meaningful share of infrastructure already running in the cloud. Then ask how many of those use cases are in production at scale, with a measurable business result attached, and the room goes quiet.

The usual explanations arrive quickly. The models aren't ready. The data is a mess. We can't hire the talent. Microsoft's view is that it is none of those things. As Kevin Shatzkamer, the Corporate Vice President leading the Telco and Media Engineering Studio at Microsoft Frontier Company, put it on a recent episode the Telco in 20 Podcast, the bottleneck is no longer the AI model. It isn't cloud, it isn't data, and at this point it probably isn't even engineering capacity. The bottleneck is transformation capacity: how fast an organization can move from idea to production to measurable outcome to scaled capability.

“The distance between what AI can do and what enterprise can actually operationalize has just become too large.”

Kevin Shatzkamer, Corporate Vice President, Microsoft Frontier Company

AI doesn't break in the demo. It breaks at the seams.

There is no shortage of good telco AI demos, and that is precisely the problem. AI rarely breaks down in the demo. It breaks down at the seams of the operational environment, where a model that performs beautifully in isolation meets business context it doesn't have, permissions it wasn't granted, observability nobody built, and an escalation path that was never designed to carry it.

Telcos carry a particular version of this problem. They have extraordinary technical depth. They also have fragmented data, decades of accumulated systems, regulatory obligation, and workflows that run cross-domain across network, care, field service, security, and commercial operations. An agent that automates one step of that chain still has to earn the trust of the five organizations that own the rest of it.

What has changed is how operators describe the problem to themselves. For the first time, they are looking at this and saying it is structural capability they need to build, not a set of tools to procure. The industry is converging on an AI-native operating model thesis, and Kevin is openly optimistic that telecoms capture this one. This is the single biggest transformation the industry will ever go through.

Three phases, and why the third one is hard

Kevin frames the industry's arc in three phases. The encouraging part of his read is that a lot of the foundational capability for the third phase already exists. The hard part is what the second phase quietly left behind.

Phase two consumed the last decade. Operators digitized the business because they recognized it as an imperative, and they built a genuine platform in the process. What it did not do was simplify the telco operating model. What it produced instead was a digital services conglomerate running on top of the network. Every new capability arrived as another platform, another vendor, another API, another integration point, another data silo, another product, another lifecycle journey for the customer. The cumulative result is technical debt at industry scale.

Operators need to do two hard things simultaneously: burn down that technical debt while capturing the AI-native opportunity. Kevin's shorthand for it is the tightest summary of the decade ahead I have heard.

“What we used to think about in the cloud world as migrate and modernize has become migrate, modernize, agentify.”

Kevin Shatzkamer, Corporate Vice President, Microsoft Frontier Company

In practice, phase three looks like the network becoming intelligent, customer service becoming intelligent, and products becoming intelligent. Instead of bundles of services, products become software plus AI plus connectivity plus data, sold with outcomes attached. It also looks like the employee becoming augmented, which is not a matter of handing everyone a copilot. It is redesigning work itself around human and agent teams. The proof points are already growing across business functions, from AI customer care and AI coding to autonomous procurement and zero-touch network operations.

What embedded engineers actually do

Microsoft Frontier Company is not a product. It is engineers embedded directly inside customer environments, building and running AI alongside the people who own the work. The operating thesis is that forward deployed engineering is largely the right model, and that the real value sits in co-design, co-deployment, and continuous improvement of AI systems at scale. Every system is outcome-driven by design, because at this point a system that cannot deliver a measurable business result is not worth deploying.

Kevin describes what he asks of his own team in a single line.

“Fall in love with our customer's problems and not our products.”

Kevin Shatzkamer, Corporate Vice President, Microsoft Frontier Company

In practice, the engagement runs in four beats.

1. Start with an outcome, not a model. What business or operational result do you actually want to change? What is the baseline? Who owns it? Which constraints are non-negotiable? Those questions sound simple and are not, and most organizations discover they cannot yet answer them.
2. Observe the work as it actually runs, not as it is documented. Where are people compensating for missing context? Where do approvals stall? Where do exceptions accumulate? Where does risk enter the system?
3. Identify the smallest production workflow that proves the new operating model, then run it for real. A prototype is very good at telling you where something can work. It does not tell you where it fails, and it does not tell you whether an organization can trust it.
4. Close the learning loops, for quality, exceptions, adoption, business outcomes, and human-in-the-loop. The objective was never a clever agent. It is a repeatable agentic harness that lets the customer build, govern, observe, and improve many agents over time.

The part most likely to feel foreign inside a telco is the cadence. Transformation in this industry has meant heavyweight programs that last years and are tracked by milestone. The discipline Frontier is trying to instill is capability-driven rather than milestone-driven, and it snaps to what engineering cadence actually looks like today: two-week sprints. That forces capability transfer and change management to become continuous as well, rather than a training event bolted on at the end. Fewer meetings about AI, and more AI in production.

A partnership with a deliberate boundary

The obvious question follows quickly. Do Microsoft's engineers train telco teams to eventually take over the work, or does Microsoft simply become the operator's AI department? Kevin calls that a false choice, and he answers both halves of it. If Microsoft becomes a customer's permanent AI department, we have not built a durable operating model for the industry, and industry-level transformation is the actual goal. At the same time, pretending that every enterprise should independently recreate a hyperscaler, or stumble through problems that already have solutions, is not productive either.

This is explicitly not about taking over telco operations. The industry ran that experiment, and most operators have since re-insourced. The model is build-with from the beginning: shared architecture, shared decisions, shared governance, and deliberate transfer of knowledge. Teams embed for a period of time, and when they leave, they leave a structural capability behind. The interlock between the two sides is unusually clean.

“My team is not successful when we've delivered. We're successful when the outcome that we intended to create for the telco is realized.”

Kevin Shatzkamer, Corporate Vice President, Microsoft Frontier Company

The question to ask your team this week

The operators who capture this cycle will not be the ones with the most pilots or the biggest AI budget. They will be the ones that rewire how change gets done: sprints measured in days rather than two-year programs, shipping in weeks rather than quarters.

So, there is one diagnostic worth running. Ask your team what your cycle time is from idea to production. If the answer comes back in months or quarters, the constraint is not the model, the cloud, or the data. It is transformation capacity, and unlike the other three, it is entirely within your control to change.

Kevin closed the conversation with the advice he is giving his own kids as they head into an AI-shaped workforce, and it holds up just as well against operating models as it does against careers. Do not try to predict which jobs AI will replace, because the work inside just about every job is going to change. Build judgment instead. Learn how to frame a problem, ask a better question, think about system dynamics, test an answer, and take accountability for outcomes. Before the internet there was value in holding and retaining knowledge. In this next world it is not just knowledge but task execution that sits at your fingertips, and the advantage belongs less to the person who has every answer than to the person who can frame the right problem.

That is as good a description of transformation capacity as any.