You've seen the clips. Salt Bae walks out, sunglasses on, sprinkling salt over a $400 steak with that dramatic elbow flick.

It looks cool. It feels like luxury. But at the end of the day… it's still just salt.

That's exactly how most enterprises are approaching AI right now.

Chatbot for HR? Sprinkle.
Copilot for Finance? Sprinkle.
Generative widget for Sales? Sprinkle.

Everything gets a little AI garnish. But nobody's actually following a recipe.

In meetings across the enterprise, the pitch is always the same:

"We'll add a chatbot here… a recommender there… maybe a GenAI pilot for the marketing team…"

It feels modern. It demos well. But the kitchen's a mess:

This is Salt Bae Syndrome: mistaking a sprinkle for a system.

And the data confirms it's not just a metaphor it's an epidemic.

MIT's Project NANDA (2025) analyzed 300 AI deployments, conducted 150 executive interviews, and surveyed 350 employees across industries. Their finding?

Approximately 95 percent of enterprise AI pilots fail to deliver measurable profit and loss impact — though this finding has sparked debate about methodology and definitions of "failure."

This isn't primarily a technology failure. It's a leadership gap.

Lead researcher Aditya Challapally calls this a "learning gap" between individuals who can use AI tools and enterprises that can operationalize them. While the study's methodology has been scrutinized, the core insight resonates: most AI initiatives stall not due to model quality, but organizational integration challenges.

The pattern repeats everywhere:

Executives approve spend, engineers build agents, yet the efforts rarely connect into a single operational roadmap.

What's missing isn't another pilot. It's a portfolio.

The problem isn't the individual pilots. It's the lack of a portfolio operating model.

When every team adds "just one more topping," you end up with the AI equivalent of a pizza so overloaded it collapses:

And suddenly your "AI transformation" is a mess nobody can support:

The result is AI sprawl without transformation lots of activity, no compounding value.

According to BCG (2024), only 26 percent of enterprises are AI "leaders" — companies that have moved beyond proofs of concept to generate real value.

These top performers demonstrate three key behaviors:

- Focus 2× more investment on building capabilities versus peers
- Scale 2× more use cases across the organization
- Generate 62% of AI value in core business processes — including operations (23%), sales and marketing (20%), and R&D (13%) — rather than limiting AI to support functions

And here's the kicker from MIT's research: firms that purchase specialized AI tools succeed approximately 67 percent of the time, while internal-only builds succeed roughly 33 percent.

Translation: The recipe matters more than the chef's ego.

Think of your AI portfolio like a restaurant menu you need a strategy, not just random toppings:

You don't serve dessert first. You don't put anchovies on the tiramisu. And you don't launch GenAI before your data governance is ready.

An AI Portfolio is your enterprise's capital-allocation model for intelligence systems. It doesn't just track projects it balances them.

Not every high-value idea is feasible today. Plot each initiative on a Value × Complexity matrix:

This exercise routinely eliminates 30–50 percent of unaligned initiatives and shifts resources toward measurable ROI.

Is this part of your recipe or just a sprinkle?

A strong AI portfolio builds muscle before mastery.

Start with foundational "operational" agents they harden pipelines, governance, and authentication. Then layer analytical and generative capabilities that reuse those foundations.

Think of it as your mise en place: you don't start cooking until your ingredients are prepped.

The message from BCG is clear: layer capabilities; don't chase hype. Launching GenAI before data governance is like pouring rocket fuel on wet wood.

Every great recipe has a base salt, oil, heat. For AI, it's governance.

Governance isn't bureaucracy; it's velocity with brakes that work.

Before you "season" your enterprise with AI, define:

- Which OKR the project supports
- What data risks (PII, bias, drift) it touches
- Who owns outcomes, uptime, and audit trail

Follow NIST's AI Risk Management Framework:

Governance done right accelerates trust and simplifies audits — it doesn't slow them down.

Organizations that break the Salt Bae habit and build portfolios instead of pilots:

In other words, the meal actually gets served on time, to paying customers, with five-star reviews.

A financial-services leader has 10 AI projects in flight.

Without a portfolio: fragmented spend, no visibility.
With one: clarity and control.

Funding decisions become obvious: Quick wins first, platform builders next, experiments later.

Clarity compounds confidence.

Next time someone says, "We'll just add AI here…" pause and ask:

"Is this part of our recipe — or just a sprinkle?"

Then, in your next AI governance meeting:

That single exercise creates your first AI Portfolio artifact and restores strategic control.

You've just stopped building a kitchen of chaos and started following a recipe.

AI isn't a project to finish it's an operating system to manage.

You wouldn't rebuild your ERP every quarter; you govern it as a portfolio asset. The same discipline now applies to intelligent systems.

Organizations that scale responsibly will:

Those that don't will spend more and learn less.

Because Salt Bae is fun on Instagram. But nobody wants to eat your MILLION $ AI mistake.

"The Agent Portfolio Map: How to Build Your AI Portfolio in Under an Hour."

We'll cover:

👉 AI Portfolio Canvas (v1.0) — 1 Page Excel template to map initiatives and ownership.
(Free download for newsletter subscribers.)

If you're done sprinkling AI on everything and ready to build an actual recipe for enterprise value, join The Foundry Floor our weekly transmission for operators who build systems that last.