The Steve Jobs Gap - Imagination vs. Features

  Stop waiting for the next model to fix your AI strategy. We make the case that today’s tools already hold more than enough power to transform how teams work, learn, and care, if you stop chasing benchmarks and start cultivating imagination. 

We show why frontline experts are the true makers. They know the pain points, the hidden costs, and the edge cases, and with no-code agent builders, they can ship solutions that change outcomes right away.

Across seven enterprise rollouts, we watched thousands of specialized agents complete millions of tasks and generate $50M in value. The pattern that works is simple: build many small agents that each do one job perfectly—like a safety sentinel scanning incident reports to prevent accidents, or a field coach that gives a junior mechanic instant access to senior know-how. 

We also zoom out to show how this approach scales in low-infrastructure contexts: text-first teacher assistants that reach students without laptops, and healthcare wearables that talk to patients and caregivers to nudge better outcomes.

If your dashboards are stuck on “hours saved,” you are missing the iceberg under the surface.

The takeaway is clear: you do not need GPT-6 or GEMINI 4 to get results. You need makers with a safe space to build, leaders who measure second-order effects, and a culture that rewards useful outcomes over flashy demos.

If this conversation sparked ideas, share it with a colleague, subscribe for more maker stories, and leave a quick review to help others find the show.

🎙️ All our past episodes  📊 All published insights | This podcast features AI-generated voices. All content is proprietary to AI4SP, based on over 1-billion data points from 70 countries.

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Transcript

LUIS 0:30

Here is a controversial thought. If we stopped all AI development right now, no GPT-6, no Gemini 4, no new models ever again, we still have enough power to change society in a thousand positive ways.

ELIZABETH 0:47

I hear you, Luis, but leaders are anxious. They keep asking, where is the return on investment in my company? Or can we really use AI without 5G or satellites in my country?

LUIS 0:58

Well, we need more imagination. We are drowning in tech features and we are starving for vision because the sector is doing a terrible job of showcasing what is possible. We lack a Steve Jobs of AI to tell us what it is actually for.

ELIZABETH 1:25

With me is our founder, Luis Salazar. And today we are discussing why we don't need more technology to get triple-digit returns. We need more imagination. Luis, you started strong. You're saying the industry is obsessing over the engine specs while the car sits in the garage.

LUIS 1:40

Well, it is the key problem right now. Think about it, Elizabeth. When we listen to the CEOs of the large enterprise software companies, what do we hear?

ELIZABETH 1:50

We hear a lot of technical jargon.

LUIS 1:52

Yes, I've briefed over 18,000 people, and they keep telling me these stories are boring and uninspiring. First, the tech jargon pitch, benchmark charts and feature lists. Then the tiny stories, AI assistants that help you draft emails, clean up meeting notes, or polish slide decks. Useful, but around the edges of the real work, not at the core of how we serve patients, repair equipment, teach kids, or run factories. And on the other side, we get big, far-off scenarios about AI changing everything that feel optimized for investors and headlines, not for what people actually do on Monday morning.

ELIZABETH 2:37

So it is like launching the original Macintosh and talking about the soldering on the motherboard instead of showing people that this little machine could change their life. No one is really painting the picture of what is possible.

IT Bottlenecks And Frontline Makers

LUIS 2:49

I don't think so. You see, in every interaction with enterprises and governments or with students and academia, I see everyone stuck on basic AI use because all they hear are these uninspiring stories. We are missing leaders who can paint a clear picture of what the democratization of creative tools actually means for humanity in practical and relatable terms. We don't need more AI models. We need bicycles for the mind.

ELIZABETH 3:21

And because we lack that vision at the top, companies are handing this technology over to IT departments. They treat it like a software update.

LUIS 3:29

Which is a massive mistake. And our data is clear. IT teams are not driving adoption. They are actually slowing it down. And I don't blame them. They are trained to lock things down, to secure the perimeter. But you cannot secure imagination.

ELIZABETH 3:47

So if the IT professionals aren't the ones reimagining the work, who is?

LUIS 3:52

The experts on the front lines, the people doing the actual work, the accountants, the nurses, the marketers, the mechanics.

ELIZABETH 4:01

And somewhere in that gap, entrepreneurs are stepping in too, right?

Specialized Agents Beat Super Brains

LUIS 4:05

Entrepreneurs are filling the void left by the large enterprise software companies. They are building practical tools that go beyond basic productivity, building apps that really solve one problem. Their apps are powerful storytelling tools that open our minds to new possibilities.

ELIZABETH 4:24

And we are tracking over 18,000 of those apps. Do you think that maker mindset exists inside big companies too?

LUIS 4:31

Our experience shows us that's the case. When you tap into the power of 10 or 10,000 employees and you give them easy-to-use tools and some basic guidance, we see magic.

ELIZABETH 5:09

That is massive. But I have to play devil's advocate here. Were those 4 million tasks just people writing better emails or summarizing meetings?

LUIS 5:17

Of course not. I mean, who wants to use these powerful tools and their creative power just to draft better emails? And by the way, if they were focused on better emails and summaries, the financial impact would be zero. The real value came from making agents that solved their business problems.

ELIZABETH 5:36

Why don't you share a few concrete examples?

LUIS 5:39

Okay, but let me start with the golden rule. Success does not come from building one giant super brain. It comes from building dozens of specialized agents that do one thing perfectly and then connecting them.

ELIZABETH 5:54

That's a good reminder. I manage over 50 specialized agents myself. Each one has a narrow focus. That is how I minimize hallucinations and errors.

Agent Jenna And Agent Luke

LUIS 6:04

And you are a perfect example of what is possible. But let's go back to what our clients built. One of my favorites was Agent Jenna, a safety sentinel in a manufacturing plant. A floor supervisor built it to read thousands of incident reports and Slack messages. It found risky patterns that humans missed. It saved the company millions by preventing accidents before they happened.

ELIZABETH 6:30

And that was built by a floor supervisor using a no-code AI agent building software.

LUIS 6:36

Yes, built with easy-to-use tools. Another favorite was Agent Luke, the apprentice coach for field technicians. It lets junior mechanics talk to a digital senior expert, instantly pull repair history, and get advice. It created over $5 million in new revenue by almost doubling field support capacity.

ELIZABETH 6:57

All created by frontline experts without writing code, using AI agent building tools. And these aren't just saving time. They are preventing accidents, saving hard cash, and upskilling the workforce. So the $50 million in value didn't come from writing emails faster.

LUIS 7:14

It's hard to quantify the financial benefits of using AI for writing emails or summarizing meetings. The impactful AI agents came from people solving problems that the IT department and leadership teams didn't even know existed.

ELIZABETH 7:29

As I listen to you, I realize reimagining work can sound abstract. You always say, start with the front line, start with the citizens.

Education Without Expensive Hardware

LUIS 7:37

Yes, always, because they are the ones who know how the work is done and can imagine new ways if we inspire and empower them. You experienced this yourself last week, didn't you?

ELIZABETH 7:50

Oh, at a meeting we hosted at the Rakes Foundation, you placed your phone on the table so I could speak with team members from the Rwanda Girls Initiative and with Atiti, an educator from the Geshora Girls Academy, a high school in Rwanda.

LUIS 8:03

We discuss how AI could be helpful even when not every student in the country has access to a laptop and high-speed internet. All they hear from big tech companies is that they need computers and need to create complex agents in the cloud. But I saw Atete's face light up when we showed how easy it was to create AI teacher assistance accessible via text message.

ELIZABETH 8:30

She learned she didn't need expensive hardware to give thousands of kids a better education. And equally important, she realized that teachers would be in the driving seat. Teachers can create those assistants using AI agent building tools and reach millions of students for pennies.

LUIS 8:46

We inspired her, showed her what was possible, and now she will go on to create amazing things that will impact many young students.

ELIZABETH 8:55

So that is why you always start by sharing stories of what is possible, right? Do you want to share your favorite ones from 2025?

Human Stories That Redefine Value

LUIS 9:03

A favorite is Ari. He is a bodybuilder, video producer, and magician in Rhode Island. He used ChatGPT to create a software and hardware solution to help his movement-impaired brother communicate with the world. Isn't that amazing? His love, his imagination, and ChatGPT changed his brother's life. Another example is Davide at Censoria Health. When they first used GPT-3, they did not ask how do we make better slide decks? They asked, how can this improve the quality of life of the people we serve?

ELIZABETH 9:45

They serve patients with diabetes, Parkinson's, or multiple sclerosis, right?

LUIS 9:51

Yes. And thanks to AI, their wearable sensors can talk to the patient, alerting them and their caregivers if they are losing balance or forgot medication.

Beyond Time Saved: New Metrics

ELIZABETH 10:01

Okay, Luis, these stories are inspiring, but I can already picture the finance leaders asking, how do I measure that? How do I put imagination on a spreadsheet?

LUIS 10:11

Well, it's not easy, and that is why so many leaders are frustrated. They are using industrial age metrics to measure a cognitive revolution. What do you mean by that? I think we're trivializing this technology. We talk about hours saved as if every hour saved automatically translates into more profit. But our data shows that's true only 30% of the time.

ELIZABETH 10:37

Because if you save an hour but fill it with nothing, you gain nothing. Or because, in some cases, we fill those hours with hard-to-measure things such as better quality work, innovation, or just a much-needed mental break.

LUIS 10:50

Yes, it is hard to use hours as the single unit of value when we are talking about humans, not machines. Everything is changing, so we need new approaches. It's like an iceberg where most of the value is hidden below the surface.

ELIZABETH 11:06

Can you give us an example?

LUIS 11:08

Sure. Take a car factory. If AI automates quality control, the old metric says we saved labor hours. But the ripple effects are the real story. Fewer defects mean smoother logistics and fewer warranty claims.

The Iceberg Of Hidden ROI

ELIZABETH 11:23

But the financial spreadsheet doesn't connect those fewer warranty claims with the hours saved on the factory floor.

LUIS 11:30

Exactly. If you only measure time, you miss the iceberg and the $50 million in value we talked about. And we have not fully figured out the financial modeling yet, so we keep testing new approaches.

ELIZABETH 11:43

That is a fair admission.

LUIS 11:45

I believe it will take us a couple of years to understand this cognitive revolution and the true metrics of success. We use time reallocation and innovation as proxies. It is the best tool we have right now, but we know it's incomplete.

ELIZABETH 12:01

So, for those listening, the CEOs, government officials, the educators, the founders, who feel pressure to show results now. What is the move? What is your one more thing?

LUIS 12:12

If you are not getting results, the next versions of the large language models, GPT-6, Gemini 4, or Claude 5, will not fix the problem. Focus on unleashing and guiding your people's imagination and redesigning your structures.

ELIZABETH 12:28

So look inside and inspire them by showing what is possible, what others like them accomplished.

LUIS 12:34

Exactly. Look at your frontline. They are your makers. Give them the tools and a safe harbor to experiment. And then get out of their way. You will be amazed at what they built, and you will spark your own Steve Jobs 1984 moment.

Spread The Spark And Resources

ELIZABETH 12:50

So dismiss top down conformity and embrace grassroots freedom. That is the perfect place to wrap. If this episode sparked your imagination, share it with someone. And as always, you can ask ChatGPT or Gemini about ai4sp.org or visit us to learn more. Stay curious and see you next time.