Custom AI App Development Trends 2027: When PE Portfolios Should Build, Buy, or Blend

Key Takeaways

• Build vs. buy is now a per-workflow call. Gartner and JLL both land there. One company can buy, blend and build at the same time, and most should.
• Buying AI gets easier first. Gartner expects AI to be sold mostly by incumbent software vendors through 2026, so a lot of "buy" decisions will arrive as upgrades you didn't ask for.
• Blend is the default for custom work. Rent the model, own the context, the workflow logic and the evals. That is where most custom AI apps will sit in 2027.
• Buying an "agent" carries new risk. Gartner estimates only about 130 of the thousands of agentic AI vendors are real, and expects over 40% of agentic projects to be cancelled by the end of 2027.
• Portfolios should decide once. A fund that sets the build, buy or blend rules at the portfolio level stops paying for the same decision in every company.

Two of the most useful pieces of AI strategy writing this year reach the same answer and still give it different names.

Gartner's guidance on deploying AI frames the choice as "build, buy or blend," and says the most effective AI will be a mix of existing apps with added AI features, new AI software and enterprise-crafted AI. JLL's July 2026 point of view on AI-assisted development calls it "buy, boost, build," and argues the build-versus-buy decision has become a workflow-by-workflow spectrum rather than a single switch.

Blend. Boost. Same idea. You don't pick a lane for the whole company anymore. You pick one for each workflow.

That sounds obvious when you write it down. In practice, most portfolio companies still make this call once, at the top, and then live with it. The CEO decides "we're a buy shop" or the new CTO decides "we build." Then every AI request gets forced through that answer, whether it fits or not.

And the money is moving fast. Gartner forecasts worldwide AI spending of $2.52 trillion in 2026, up 44% in a year, and about $3.34 trillion in 2027. A lot of that will land in your portfolio companies whether or not anyone has a sourcing strategy.

So here is how I'd think about custom AI app development going into 2027: which way the buy, blend and build lines are moving, and what a PE portfolio should do about it.

Build vs. Buy Was Always a Per-Workflow Question

The old build-versus-buy debate assumed software was expensive to build and cheap to rent. Under that assumption, a company-wide default made sense. You bought almost everything and built the core product.

AI broke the assumption on both sides. Building got cheaper, because a small team with AI-assisted development can stand up a working app much faster than before. Buying got messier, because every vendor in your stack now ships AI features, and some of those features overlap with each other.

When both sides change at once, a single policy stops working. The same company might reasonably buy AI inside its ERP, blend a custom quoting assistant on top of a foundation model, and build one proprietary pricing engine from scratch. Those are three different answers, and all three can be right.

Decide workflow by workflow

Pick the ten workflows where AI could matter most in a company. For each one, write down three things: how unique the work is to this company, how sensitive the data is, and who would own the result for the next three years. Those three answers will sort most workflows faster than any strategy offsite.

Buying Gets Easier Before Building Does

The first trend for 2027 is the quietest one. A lot of AI will simply show up in software you already pay for.

Gartner calls embedded AI the largest and fastest-growing area of AI capabilities, delivered as upgrades and add-ons to ERPs, CRMs and case management tools. Its 2026 spending forecast goes further: because AI sits in the Trough of Disillusionment throughout 2026, Gartner's John-David Lovelock says it will most often be sold to enterprises by their incumbent software provider rather than bought as a new moonshot.

That is a big deal for portfolio companies. It means the default "buy" decision is often made for you, at renewal, as a price increase with an AI label on it.

Some of those features will be great. Some will be a chatbot bolted onto a menu. Either way, you're paying.

Gartner also flags the other side of this. When individual departments bring in their own best-of-breed AI tools (it calls this "bring your own AI"), the cumulative effect can create overlap, unnecessary costs and technical debt. Marketing buys a writing tool. Legal buys a contract tool. Sales gets an AI add-on in the CRM. Three tools, three data copies, nobody looking at the whole picture.

Audit the AI you already pay for

Before anyone pitches a custom build, list every AI feature the company already has access to: embedded features in core systems, standalone AI tools by department, and the AI seats people expense on their own. You will usually find capability that nobody turned on and overlap nobody noticed. That list is the baseline every build or blend decision gets measured against.

Blend Is Where Most Custom AI Apps Will Live

This is the trend I care most about, and it is the one most likely to shape custom AI app development in 2027.

Gartner's definition is useful here. It describes "blended" AI as organizations blending in APIs from foundation models with custom front-ends, integrations and the customization needed to make those models work for the business. Built AI, in its original sense of training models from scratch, has mostly given way to that approach.

I think that is the right default for almost every custom AI app a portfolio company should commission. You rent the hard, expensive, fast-moving layer: the model. You own the parts that make the app yours: the data and context it draws on, the workflow logic, the interface your team uses, and the evaluations that prove it works.

Here is my reasoning. The model layer improves every few months, and you want those upgrades for free. The context layer (your documents, your customer history, your pricing rules, your process) is the only part a competitor can't copy. Over time, that is where the value in an AI app sits.

The spending data points the same direction. In Gartner's forecast, AI services spending outpaces AI software spending in every year from 2025 to 2027, reaching about $761 billion against $636 billion for AI software in 2027. Gartner doesn't break down what's inside that services number, so I won't pretend it's all custom builds. But it tells me companies expect to pay for help fitting AI to their business, on top of the software itself.

Own the layer that holds your context

When you scope a blended app, write down which parts you own and which you rent. The model, hosting and vector database can be swapped. The data pipeline, the prompts and workflow logic, the access rules and the eval set should belong to the company, documented well enough that a new team could pick them up. If a vendor or partner holds that layer and you can't take it with you, you haven't blended. You've bought, with extra steps.

Agent Washing Makes Buying Riskier Than It Looks

The buy side has its own 2027 problem, and it is a labeling problem.

Gartner has a name for it: "agent washing," the rebranding of existing AI assistants, RPA and chatbots as agents without real agentic capability. In the same release, Gartner estimates only about 130 of the thousands of agentic AI vendors are real, and predicts over 40% of agentic AI projects will be cancelled by the end of 2027 due to escalating costs, unclear business value or inadequate risk controls.

Read that date again. End of 2027. A lot of the agent pilots portfolio companies signed this year are going to hit their review point next year. Some will get cancelled because the product was never really an agent. Others will get cancelled because nobody could say what it was worth.

Neither of those is a reason to stop buying. It is a reason to buy like a skeptic.

Ask vendors to show the loop

When a vendor says "agent," ask them to walk through one real task on your data: what the agent decides, what tools it calls, where a person approves, and what happens when it gets something wrong. A real agent has an answer to each of those. A rebranded chatbot tends to change the subject.

Build Only Where Your Data Is the Product

Then there is full build. I'm bullish on building, and I still think most companies should do less of it than they want to.

JLL's framing here is sensible. It reserves AI-assisted bespoke build for the few workflows that create durable, proprietary advantage and depend on data no vendor can replicate, and says every such build should carry a named owner, separate development and production environments, approval gates, security scanning of generated code, an audit trail and a cost model that includes maintenance.

That list is long on purpose. Building software is cheaper than it used to be. Owning software is not. Somebody has to patch it, monitor it, fix it when an upstream system changes, and explain it to an auditor.

My test for a real build candidate is simple. If the app gets better every time the company does more business, because it learns from data only this company has, it's a build candidate. A proprietary underwriting model fed by years of claims history fits that. A meeting summarizer does not. Plenty of people have built a decent one, and some of them sell it.

Fund the maintenance before the build

Before approving a full custom build, ask for the three-year cost, not the launch cost. Include the people who will own it, the monitoring, the model and hosting bills, and the time to update it as the business changes. If that number still makes sense, build. If it only works when you leave maintenance out, it was a blend or a buy all along.

Portfolios Should Make This Call Once, Not Twelve Times

This is where PE has a real advantage over a standalone company, and most funds don't use it yet.

A single company has to work out build, buy or blend on its own. A portfolio has twelve or twenty companies working it out at the same time, usually with the same vendors, the same workflows and the same mistakes. Each one runs its own evaluation, signs its own contracts and builds its own version of the plumbing.

That is wasted work. The decisions that repeat across companies should be made once, at the fund level, and then applied.

Here's how I'd split it:

Build the shared layer at the fund level

The piece worth funding centrally is the blended foundation: model access, logging, access controls, evaluation and a standard way to connect company data. Once that exists, each new custom AI app at a portfolio company starts from a governed base instead of a blank page. That is the same idea behind the AI platform work we describe on our Custom AI Application Development page: shared services for models, retrieval, prompts and evaluation, so teams stop rebuilding the same plumbing for each use case.

The Line Will Keep Moving, So Write It in Pencil

I'm confident about the direction. Buy where the work is generic. Blend where the workflow is yours and the model can be rented. Build only where your own data makes the product better every month.

What I'm less sure about is where the lines sit a year from now. Vendors will keep pulling capability into their platforms, which pushes some blends back into buys. Models will keep getting cheaper and more capable, which makes some builds easier to justify. And a lot of 2026's agent pilots will get cancelled or rebuilt in 2027, which will reset expectations for everyone. JLL makes the same point in its own way: attach a review date to every decision instead of treating it as permanent.

So I'd start small and specific. Pick one portfolio company. List its ten highest-value AI workflows. Mark each one buy, blend or build, with a reason and an owner. That one page will tell you more about your AI strategy than any trend report, this one included.

If you want help working through that list, or you're ready to build the blended layer the rest of the portfolio can reuse, that is what our Custom AI Application Development and Forward Deployed AI Engineering teams do. Book a Discovery Call.

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