McKinsey State of AI 2026: Build vs Buy Just Flipped
McKinsey State of AI 2026 shows 32% of companies skipped a software purchase because agentic coding tools let them build it instead. Here is how to pick your side.
We've spent the last 11 months shipping voice agent deployments for coaches, consultants, fintech, real estate, and a handful of edge cases. Ninety-six in production. Here's what we've learned about what actually works in 2026.
1. The model isn't the bottleneck anymore
GPT-4o-realtime, Claude 3.5 Sonnet voice, and the open-source equivalents are good enough for 92% of production scenarios. Telephony latency, audio processing pipelines, and prompt routing are now the failure modes not LLM quality.
If your agent feels janky, audit your audio path before you audit your prompts. Eight times out of ten, that's where the friction lives.
"The agents that work feel like infrastructure. The agents that fail feel like party tricks."
2. Voice ≠ chatbot with audio
Every team that tries to port their chatbot prompt to voice fails the same way: too verbose, too formal, too explainer-y. Voice is improv. You need shorter turns, callback handles, and graceful interruption.
3. The handoff is the product
The best voice agent in the world is useless if the post-call sync is broken. Notes go to CRM. CRM triggers sequence. Sequence books follow-up. Calendar invites human. That is the system. The voice piece is one component.
If you want to see a live example, our AI calling system is running in production for loan servicing and collections you can see the real numbers on the case studies page.
The McKinsey State of AI 2026 survey put a number on something operators have felt all year: the build versus buy decision just flipped. According to the report, 32 percent of organizations have skipped buying at least one software product or feature in the past year because they could build it internally with agentic coding tools. That is not a rounding error. It is nearly one in three companies deciding that a subscription they would have paid for in 2024 is now cheaper to assemble in house.
At the same time, the share of large enterprises scaling AI agents in one or more functions climbed from 27 percent to 40 percent, while smaller firms stayed flat near 22 percent. The gap between companies that can build and companies that still only buy is widening fast. Here is what the McKinsey State of AI 2026 data actually means for your roadmap, and how to decide which side of the line each project belongs on.
Why Build vs Buy Flipped in 2026
For a decade, the math favored buying. Building software meant hiring engineers, waiting quarters, and maintaining the result forever. A SaaS subscription was faster, predictable, and someone else's problem to keep running. Agentic coding tools broke that trade in three places.
- Time to first version collapsed. A working internal tool that used to take a two-person team six weeks now takes one operator a few days. When the build is that cheap, the annual cost of a mid-tier SaaS seat starts to look like a bad deal.
- Maintenance stopped being a wall. The same agentic tools that write the first version also patch bugs, add fields, and refactor when requirements change. The long tail of ownership that killed most internal builds is now a prompt, not a hiring plan.
- Integration is the real product. Most SaaS value in operations is glue: moving data between your CRM, your billing system, and your inbox. That glue is exactly what agents and workflow tools do well, and it is exactly what generic SaaS does badly because it does not know your stack.
The result is that the category of software worth buying shrank. Deep, defensible platforms still win on buy. Thin workflow wrappers lost their moat.
What the McKinsey State of AI 2026 Numbers Tell Operators
Three signals matter for planning.
The build capability is now a competitive line. Large enterprises jumped to 40 percent scaling agents while small firms stalled at 22 percent. If your competitors can stand up an internal tool in a week and you still file a procurement ticket, they will out-iterate you on process, not just cost. This is the same dynamic that separated companies with real data teams from companies without one a decade ago.
Buying is not dead, it is narrower. The 32 percent figure is about skipped purchases of features that were easy to replicate, not core systems. Nobody is rebuilding their general ledger or their data warehouse with a coding agent. The purchases getting cut are point solutions: a lightweight approval tool, a reporting dashboard, a notification router, a small internal portal.
The risk moved from vendor lock-in to build sprawl. When every team can build, you get twenty half-maintained internal tools with no owner, no monitoring, and no documentation. The failure mode of the buy era was paying for shelfware. The failure mode of the build era is a pile of undocumented automations that break when one API key rotates.
A Build vs Buy Decision Framework for 2026
Run every candidate project through four questions before you decide.
- Is this core to how we make money, or is it plumbing? Plumbing is a strong build candidate. Core systems of record are a strong buy. If the tool would show up in a customer contract or an audit, lean buy.
- How often will the requirements change? High-change workflows favor build, because you control the iteration speed. Stable, standardized processes favor buy, because a vendor amortizes compliance and edge cases across thousands of customers.
- Who owns it after launch? If you cannot name one person accountable for the tool's uptime, cost, and roadmap, do not build it. Buy it and let the vendor be the owner.
- What is the real total cost? Compare three years of subscription plus integration work against the build cost plus monitoring, hosting, and the owner's time. Include the cost of the thing breaking at 2 a.m. Build usually wins on small tools and loses on anything with heavy compliance.
The pattern that holds up: buy the systems of record, build the workflows that connect them. Your CRM, your accounting platform, your support desk, and your data warehouse stay bought. The agents and automations that move work between them are where an internal build pays off, because that layer is specific to your business and changes every quarter.
How to Build Without Creating Sprawl
The companies getting value from the McKinsey State of AI 2026 shift are not the ones building the most tools. They are the ones building on a controlled foundation. That means a single workflow platform instead of ten disconnected scripts, an agent layer with logging and cost tracking on every run, a named owner per system, and a human approval step on anything that touches money or customers. Build sprawl is not a reason to avoid building. It is a reason to build on rails.
This is the model Nexica uses on client work. We have delivered 100+ systems and handled $48.9M in accounts through agents and workflow automation built on 14-day builds, with monitoring and ownership baked in from day one instead of bolted on after something breaks. The teams that win the build era treat an internal tool like a production system, not a weekend script.
The Takeaway
The McKinsey State of AI 2026 report confirms the shift is real: agentic coding tools made building cheaper than buying for a large and growing class of software, and the enterprises that can build are pulling ahead of the ones that only buy. The move is not to build everything. It is to buy your systems of record, build the workflow layer that connects them, and put every internal build on a foundation with logging, ownership, and a human check. Pick each project deliberately and you capture the upside without the sprawl.
See how we build production systems in our case studies, or explore AI agents and workflow automation.
If you want this built for your business, book a 20-minute call with Nexica AI. We build production-grade AI systems in 14 days.