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Busting open the AI Adoption Gridlock: 'We Only Win When You Win' Company Rewrites the Rules

Wondering if AI Adoption is slow for everyone? Keebo's CEO is flipping the script with a 30-minute to live model delivering results and ROI. Cut through the hype to reveal how to build trust, EP #68

AI adoption, especially at the enterprise level, is stuck in neutral.

Tons of promise, and challenges - how can we all align AI incentives with our customers, when we don’t know what the next 2 years will bring.

We're thinking about AI adoption all wrong, over promising and under performing the hype.

Yes it’s software, but it’s not software from 1989, and that’s still quietly how it’s being treated.

We’re wrapping AI, even if just in name, around any business to gather a look.

Check out Keebo

While enterprises scramble to implement AI and vendors plaster "AI-powered" on everything they sell, they're missing something fundamental:

The barrier to AI adoption isn't the technology - it's how we sell and implement it.

Think about this simple example I shared with my guest, Keebo CEO Barzan Mozafari:

When companies want to optimize their cloud infrastructure, which path do they take?

Do they:

A) Hire expensive consultants, spend months in implementation, and hope it works?

OR

B) Take 30 minutes to set up, see results in 24 hours, and only pay for proven savings?

This isn't just about efficiency. It's about a fundamental shift in how we approach AI adoption, and it's happening right now.

We're forcing AI adoption into a traditional enterprise software model - lengthy implementations, upfront costs, and unclear ROI.

It's like we're trying to sell a rocket ship using a used car salesman's playbook.

Then there are people like Barzan, delivering a solution that works in 30 minutes, saving money or you don’t pay.

Not many in AI can back that promise, nor do it that quickly.

And while we’re not going to clone Keebo, what we can learn is how to operate in AI, how to sell without seeming like the million other AI scripts trying to sell into an audience that is not adopting AI quickly.

CEO Barzan Mozafari (LinkedIn) is the co-founder of Keebo.ai  - a turn-key Data Learning platform for automating and accelerating enterprise analytics.

He also is a dual patent holder for his award winning research at the intersection of ML and database systems across the Univ. of Michigan, MIT, and UCLA.

What Keebo is all about

The data warehousing market will grow to $51B annually by 2028–over 10% CAGR. Keebo’s patented algorithms will be essential to optimizing it all.

  • Built on over 15 years of cutting-edge research at top universities

  • Data learning technology fully automates warehouse and query optimization

  • Our friendly robots happily do this tedious work in real-time 24/7 so you don’t have to

  • We’ve seen customers save as much as 70% and accelerate queries by 100x

Barzan is a sought after expert in the space who has spoken on panels like Insight Jam, he’s shares his research and expertise for the advancement and optimization of data teams everywhere.

·  The Crossroads of AI & Data Engineering

·  Why Automation ≠ Loss of Governance or Observability

·  Emerging Tech/Trends in Data Warehousing & AI

·  Building a Successful Data-Driven Company

·  Enterprise Concerns About AI Adoption + Overcoming Them

·  Cloud Data Warehousing & Cost Optimization

As Barzan told me,

"There aren't a lot of products out there where you can spend 30 minutes setting it up and then wake up the next day to see hundreds of thousands of dollars in net savings."

The real transformation isn't:

❌ Adding AI features to existing products

❌ Long consulting engagements

❌ Hoping for ROI months later

The real transformation is:

✅ Proving value immediately

✅ Aligning vendor and customer incentives

✅ Only paying for actual results

Why AI Adoption is Slow: The Hard Truth

"The rate of progress in AI and machine learning far surpasses the rate of adoption," Mozafari explains, with the kind of directness that comes from years in academia before entering the business world.

"It's disappointing."

As someone who's watched countless AI implementations fail, I've seen this disconnect firsthand.

Barzan brings a unique perspective, having made the leap from academia to entrepreneurship precisely to solve this problem.

"When you're in the academic bubble, you solve interesting problems, you're moving the state of the art forward," he shares.

"But you're not getting the adoption.

You try to approach companies saying you have a really cool idea, here's an important problem, here's a solution - but you can't really get something out into the world."

The 4 Key Blocks to AI Adoption

Barzan breaks down the barriers into four critical categories that every enterprise faces:

1. Implementation Effort:

"AI is a completely new beast to most enterprises.

They've been doing their business the exact same way for decades, and suddenly they're hearing about GenAI, ChatGPT, LLMs..."

2. Maintenance Cost:

"There's the CapEx part of the initial investment - hiring, headcount, training, changing processes.

But then there's the OpEx aspect - what's the maintenance cost, the total cost of ownership?"

3. Security and Privacy:

"CIOs have to think about how they know customer data isn't going to end up somewhere in the LLM helping another customer.

There are security, privacy, fairness, ethics laws, compliance requirements..."

4. ROI Uncertainty:

"After all these investments in implementation, maintenance, and security - what's the ROI?

When will I have something to show for all this cost?"

The Bias Against AI ROI

Keebo claims to save customers 30-60% on their cloud warehousing costs - numbers that sound almost too good to be true in an industry filled with hype.

"That's actually one of our biggest obstacles early on," Mozafari admits.

"The thing sounds - and actually we have a case study on this on our website - it sounds too good to be true.

The customer says it sounds too good to be true. A week later after the free trial, they were like, 'What do I sign?'"

"It's actually troubling," Barzan notes,

"when you come from academia where we have PhD students working on peer-reviewed publications and patents - stuff that actually works.

But when you're in a lineup with 20 other vendors who are pitching AI, you have to convince people yours is real."

This gets to the heart of today's AI credibility crisis. At a recent Snowflake Summit, Barzan noticed something telling:

"I don't think we came across a booth that didn't have the word AI on it somewhere.

Statistically speaking, it's pretty unlikely every single vendor is using AI."

Overhyped Marketing: When AI Becomes Meaningless

"Shameless marketing is muddying the waters for those of us actually trying to move the field forward," Barzan says, and he's seen both sides of the divide.

"There's a company doing visualization - where's the AI?

Well, you can visualize it and then you say 'AI.'"

This "AI washing" creates a paradox: the more vendors claim to use AI, the harder it becomes for companies doing real AI work to stand out.

"When you're in a lineup with 20 other vendors who are pitching AI, you have to tell people

'No, this one is actually AI.'"

AI Overpitch vs. Academic Discipline

"Coming from an engineering background, certain problems are really easy to solve," Barzan reflects.

"But there's many ways you can do amazing things - you don't need the latest LLM to deliver value to a customer.

Sometimes a linear regression does a pretty good job, and we should be okay with that."

This academic pragmatism stands in sharp contrast to the market's AI hysteria.

"As a community, if we want to move forward, more of us should act responsibly and just say it for what it is.

Why do you care if I'm using AI or not?

What matters is: Does it solve your problem?"

How Does Keebo Get Them to Adopt AI?

Here's where Keebo's approach gets interesting. Instead of fighting the adoption barriers, they designed around them from day one:

"Whatever solution we developed, the entire onboarding implementation effort should be almost zero," Barzan explains.

"The entire onboarding of Keebo should take no more than 30 minutes of one engineer's time.

Because regardless of how busy your team is, 30 minutes is the time it takes to grab a cup of coffee."

They tackled each barrier systematically:

- Implementation:

"I used to joke in the early days that if we take more than 30 minutes of your time, we'll send you an iPad for free. We never had to give away an iPad."

- Maintenance:

"We designed the system to be not another child that needs constant attention."

- Security:

"We don't even need to see customer data. We train our AI purely on telemetry data."

Aligning Incentives with the Customer

The breakthrough aspect of Keebo's approach isn't technological - it's philosophical.

 "When a vendor approaches a customer, we should have slightly more confidence in our ability to deliver than you do. That's a reasonable expectation," Barzan explains.

This led to their performance-based model:

"Instead of requiring you to commit to a set payment, we're going to start optimizing and just take a percentage of whatever we save. If we save you $2 million, you take two, we take one. That comes with an inherent, intrinsic, guaranteed ROI."

The contrast with traditional approaches is stark:

"One of the biggest ways people tried to reduce their infrastructure costs before Keebo was hiring consulting firms.

But the incentives weren't aligned because they get billed by the hour.

For the consulting agency to make more money, they have to make just enough progress not to get fired, to prolong that process as far as possible."

"Most of our customers start seeing ROI and savings within 24 hours," Barzan notes.

"We're not successful until the customer is successful.

The more we save them, the more money we make."

This alignment of incentives creates a virtuous cycle.

Without long implementations or upfront costs, companies can try the solution with minimal risk.

When they see immediate results, trust builds quickly.

"If you can figure out, in whatever business you're doing, where you can align your incentives with your end users' incentives -

that's where things happen really, really quickly."

In an industry drowning in hype and complexity, Keebo's approach is refreshingly straightforward:

prove value fast, align incentives completely, and let results speak for themselves.

It's a model that could transform not just how we adopt AI, but how we think about enterprise software entirely.

As Barzan says,

 "When the dust settles, when the community matures a little more, people will get more sophisticated about what AI does, what it can do, and what it should do.

Then those who do it well will thrive - because at the end of the day, if it doesn't work, it doesn't matter if you used AI or not.

You're going to move on."

Breaking the AI Adoption Gridlock:

When Performance Beats Promises

We've been thinking about AI adoption entirely wrong.

While the industry obsesses over the latest AI models and capabilities, the barrier isn't technical - it's structural.

Keebo's approach is unique not because of their AI (though their patent-pending query optimization is impressive), but because they've fundamentally reimagined the business model of enterprise AI adoption.

Instead of the traditional "pay upfront and hope it works" model, they've created a performance-based system that aligns perfectly with customer success.

Think about it:

30 minutes to set up,

24 hours to see results, and

you only pay for actual savings.

Almost absurdly simple, yet solves the four major barriers to AI adoption that Barzan identified:

- Implementation complexity? Gone.

- Maintenance headaches? Eliminated.

- Security concerns? Minimized by using only telemetry data.

- ROI uncertainty? Solved through performance-based pricing.

AI Startups, listen: We're so caught up in building bigger models and adding more features that we've forgotten the fundamental truth:

customers don't care about AI - they care about results.

The current enterprise AI model is broken.

We're forcing customers through months-long implementations, expensive consulting engagements, and complex integrations - all while plastering "AI-powered" on everything.

As Barzan noted at the Snowflake Summit, every vendor claimed to use AI, which is "statistically unlikely."

Here's the challenge to AI startups:

Stop thinking about how to add AI to your business model and start thinking about how to align your incentives with customer success.

Keebo's approach - taking a percentage of customer savings - might not work for every business, but the principle behind it is universal:

prove value first, get paid later.

The real problem with AI adoption isn't the technology - it's trust.

And you don't build trust through marketing claims or complex implementations.

You build it by putting your money where your mouth is and succeeding only when your customers succeed.

As Barzan says,

"When the dust settles... those who actually do it well will thrive - because at the end of the day, if it doesn't work, it doesn't matter if you used AI or not."

That's not just good advice for AI adoption - it's a blueprint for enterprise software to follow.

Check out Keebo

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The AI Optimist
The AI Optimist
Moving beyond AI hype, The AI Optimist explores how we can use AI to our advantage, how not to be left behind, and what's essential for business and education going forward.
Each week for one year I’m exploring the possibilities of AI, against the drawbacks. Diving into regulations and the top 10 questions posed by AI Pessimists, I’m not here to prove I’m right. The purpose here is to engage in discussions with both sides, hear out what we fear and what we hope for, and help design AI models that benefit us all.
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