“AI costs too much”: what if that’s the wrong question?

First issue. Here’s what Indirect, Augmented is, and why it’s landing in your inbox now.

Indirect procurement is being transformed. Not in the future: now. On foundations you’ve already built (P2P, master data, contract governance), that AI is finally accelerating by absorbing the transactional load. The ecosystem moves fast: every month, a new model, a new tool, one more acquisition. This newsletter is the map to navigate that change.

The hero of this map is the augmented buyer: tooled when it helps, method when the tool isn’t enough, human judgment when method hits its limits. The contract fits in one line: one issue a month, 8 to 10 minutes to read, a month of news distilled, by a buyer obsessed with procurement and convinced by AI. No evangelism, no SaaS pitch. I explore with you where AI makes sense, where it doesn’t, and how to try to make the most of this fast-moving technology.

Well augmented, the indirect buyer becomes the strategic partner to the CFO everyone has been waiting three decades for. Badly augmented, he becomes a robot that approves purchase orders faster.

My read on procurement AI in three states: assisted, augmented, autonomous →

On the menu this month: why “AI costs too much” is the wrong question, a framework to decide (Build / Buy / Defer), three acquisitions reshaping the market, a workflow to try Monday, and two players to watch.

“AI costs too much”: the line that gives away you never priced the value.

One question runs through this whole issue: what does AI really cost, and compared to what. Here’s my short version. Since Microsoft cut its Claude Code licenses and Uber burned through its 2026 AI budget in four months, the debate has shifted to: “AI costs too much.” That’s the wrong question.

The cost of AI is one of the few things we can actually price today. The price per million tokens runs from $0.10 to $25 depending on the model (public vendor pricing, 2026), and it drops every quarter. That cost is known, bounded, and falling. What almost no one prices is the other side of the equation: the value. Which workflow, how much time freed, reinvested in what.

The approach of these giants was bound to fail. Instead of finding the right use cases and governing their agents (costs included), they handed unlimited credit cards to their teams, in the form of tokens. Uber even ranked its teams by usage volume. Then everyone is surprised the bill explodes.

Behind these companies’ “AI problem” sits a governance problem, and even a procurement one:

• Oversized models → challenge the need.
• Resources poorly used → assess TCO and ROI.
• Budgets exploding → governance and cost-monitoring.
• Contracts left unmanaged → negotiation.

You already make this trade-off every day, without naming it. An €80 purchase request routed through 4 approvers costs more to control than to execute: under €500, processing alone commonly runs €30 to €80 (Ardent Partners / APQC, P2P benchmarks). An €800k order through the same workflow is the opposite. The right level of process calibrates to risk and value, not to gross amount. Exactly the reasoning to apply to AI.

Value first, cost second. An organization that starts from cost buys the cheapest license and buries it in a PoC that gathers dust.

Identify the workflow that frees the most judgment time, price that time, then build with cost in mind. In that order. And that’s where indirect procurement is the right pilot. Not IT, not an AI committee: procurement already does this work. Run a should-cost on the AI itself: that’s exactly the job of an AI procurement audit. Manage your agents like you would manage a new hire: a scope, rights, a review. Governance enables augmentation rather than holding it back.

Well augmented, the indirect buyer sets the frame that makes value measurable. Badly augmented, he signs three SaaS and calls it a strategy.

The cost of AI was never the issue. The issue is the value you’re augmenting, and whether you priced it before opening the wallet.

Build / Buy / Defer.

Three acquisitions mark the month (detailed below, in the signals): each one a bet on the “Buy” branch of a framework that belongs on every indirect buyer’s desk in 2026. McKinsey sorts every agentic-AI decision into four branches. Read through a procurement lens:

You’ll recognize these branches in this month’s news. The three acquisitions are the Buy strategy seen from the vendor side: they buy the capability rather than build it. Lio, the a16z-funded vertical (below), is the Build strategy. And the same decision faces you, at your scale: for each AI use case, build it, buy it, or wait? The branch everyone forgets is Defer. BCG surveyed 32 retail CPOs: 72% are exploring AI, none could name a concrete use case. (BCG, Indirect Spend in Retail, January 2025.) On a technology whose token cost drops about 80% in 18 months (public vendor pricing, 2025-2026), waiting 6 to 12 months is often the highest-ROI scenario. 80% of indirect procurement use cases (spend categorization, invoice processing, RFQ generation) aren’t differentiating: the default answer is Buy or Defer, never Build.

Before your next AI committee, demand the “do nothing for 12 months” scenario, with the opportunity cost quantified. In 30 to 50% of cases, that’s the one that wins.

The month’s pieces on augmentation.

This month, one obsession on the blog: the cost of AI. Three angles to put it in its place.

The month of three acquisitions.

One month, three acquirers with opposite profiles, one target: own the autonomous procurement stack. What each deal means, and what the three say together.

An augmented buyer in action.

We start with the easiest use case: data enrichment. We’ll move further down the procurement process over the coming issues. What if you restarted that project you’d shelved for lack of data? If it lives in PDFs, extracting it has never been easier.

Start with the data. It’s the easiest use case, and the one that clears the way for all the others. Next month: sort a longlist of 8 suppliers into a ranked shortlist of 3.

Need a hand getting started or improving your first results? Write to me, I reply to every message.

The player to watch: Lio.

I’m launching this rubric, and fate lands on the company that shares my last name. I promise it isn’t staged. And no, it isn’t mine: we just share the name and the obsession with indirect procurement.

Lio (ex-askLio) is a multi-agent platform for indirect procurement: supplier research, negotiation, approvals and delivery tracking run in parallel. A $30M Series A led by a16z in March 2026, ~$33M raised in total, clients Munich Re, Brose, Novozymes. (Lio / a16z, 2026.)

What’s in it for you: concretely, its agents take over tasks your teams do by hand today. Finding and qualifying suppliers, preparing and running a negotiation, routing approvals, tracking deliveries. All of it on top of your ERP rather than inside it, so no new IT project. The CFO question: on which repetitive procurement tasks would a layer like this save you the most time?

The person to watch: Jason Busch.

Founder and CEO of Spend Matters since 2004, the world’s reference for procuretech analysis, and founder of Azul Partners. Co-founder of Public Spend Forum, ex-FreeMarkets, 1,000+ published analyses. (Spend Matters / Azul Partners, 2026.)

Why I follow him: a visionary, an AI enthusiast, and some of the sharpest analysis on the market. He doesn’t just read the deals (like our three acquisitions this month), he draws out the implications for the whole chain: buyer education, SaaS-vendor economics, procuretech consolidation. The read that gains altitude when the noise rises.

His LinkedIn profile →