Over the weekend, the electrical system warning light came on in my Hyundai Kona EV. The owner’s manual was straightforward: “Take the vehicle to the dealer for evaluation.” Simple enough — or so I thought. What followed, however, was a lesson in how even the most intelligent technologies fall flat when they’re bolted onto poorly designed systems.
🚗 A Simple Problem, a Complex Journey
I started with what should have been the easiest option: scheduling an appointment online.
The dealership’s website, however, was a labyrinth. Before reaching the service section, I had to click through glossy promotions, banners, and testimonials — content built to impress the customer, not serve them. When I finally arrived at the scheduling page, I was met with dozens of service options, none of which clearly matched “electrical system warning.”
After some trial and error, I found a free-form text box where I could describe the issue manually. But when I clicked through to finalize the appointment, the first available slot was six weeks away.
So I did what many customers still do — I called.
☎️ The Rise and Fall of a Perfect AI Moment
The service line was busy, but a pleasant chatbot offered to help.
To my surprise, it was good — really good. It instantly recognized my car via caller ID, asked what was wrong, and correctly summarized:
“Got it. Your EV service light is on, and you need to have it checked out.”
Then, when I said I wasn’t sure how urgent the issue was, it replied:
“Got it. You don’t know how urgent the problem is. I’ll have a service advisor call you back.”
That exchange was nearly perfect — concise, contextually aware, and empathetic. It felt like technology doing exactly what it was designed for.
But the follow-up call broke the illusion. It wasn’t a technician or advisor; it was a receptionist who had no record of the chatbot interaction. She asked for my phone number again, had to look up the car, and made me repeat everything I’d already told the AI. It was frustratingly inefficient. After several back-and-forths, I finally secured an appointment for later in the week.
The chatbot had gathered all the right information — but the dealership’s workflow had nowhere to put it. The data lived and died inside a disconnected system.
🔥 A Gas Bill, a Broken Process, and No AI in Sight
Around the same time, I was battling another service issue — this time with my gas utility company.
After moving homes, my account information was mislinked. My online dashboard still displayed my old address, even after multiple phone calls and resets. Agents repeatedly verified my data, promised callbacks that never came, and apparently recorded nothing from prior interactions. The final straw was a letter addressed to “Resident” urging me to contact them because someone was using gas without an account on record — me.
Every call felt like starting over from zero.
Eventually, out of sheer exasperation, I wrote an email:
“I’m going to stop trying to solve this problem. The ball is in your court. In the meantime, I’ll assume you plan to provide gas services for free.”
That finally got a callback — and a resolution. But the experience exposed a fundamental truth: AI is only as smart as the systems it’s built upon.
🧠 AI Alone Isn’t the Problem — Integration Is
These two experiences illustrate the same structural failure from opposite sides.
The Hyundai dealer had a capable AI that wasn’t connected to the people or systems it needed to support.
The gas company had plenty of humans but no intelligent system to capture or share context.
In both cases, the workflow was broken. And this is the real challenge of today’s AI revolution.
AI can summarize, predict, and personalize — but unless it’s integrated into a company’s operational backbone, it can’t act effectively.
If a chatbot’s notes aren’t visible to the human who calls next, or if a “smart” system can’t trigger the right follow-up workflow, the result is the same: customer frustration.
🏗️ You Can’t Build a Skyscraper on a Crumbling Foundation
Enterprises today are rushing to “add AI” to their operations, but they’re treating it like a cosmetic layer — a digital facade on top of outdated systems. Without reengineering the core process, AI becomes just another widget in a cluttered workflow.
It’s not enough for a chatbot to sound intelligent.
It needs:
- Access to accurate customer records and historical data
- Integration with scheduling, billing, and support databases
- The ability to trigger follow-up actions automatically
- A feedback loop so humans and AIs learn from each other
Without that foundation, even the best models — GPTs, Claude, Gemini — are just polite middlemen repeating what we already know.
⚙️ The Future: AI as Workflow, Not Just Interface
Imagine a world where your car dealership AI logs your issue, checks your vehicle diagnostics remotely, identifies potential warranty coverage, and schedules the correct specialist — all before a human ever calls.
Or where your utility AI can reconcile mismatched records, update your account automatically, and send a transparent summary of the fix.
We’re not far from this future.
But to get there, companies need to treat AI as infrastructure, not interface. Integration — not intelligence — is the real differentiator.
🧩 Intelligence Is Becoming a Commodity — Integration Is the Moat
As large language models become ubiquitous and commoditized, the next competitive advantage won’t come from who has the smartest AI. It will come from who integrates it best.
Companies that deeply embed AI into their systems — connecting data, humans, and processes — will build a moat that’s incredibly difficult to cross. It’s not about adding a chatbot; it’s about redesigning the workflow around intelligent, adaptive systems.
This is the new hierarchy of advantage:
- Data Moat – Proprietary data still matters.
- Workflow Moat – How efficiently AI fits into operations.
- Human-AI Collaboration Moat – How seamlessly people and AI share tasks.
The future belongs to those who rebuild their foundations — not those who simply decorate them with AI.
🧭 Final Thought
AI will continue to evolve, but as these everyday frustrations show, the bottleneck isn’t intelligence — it’s integration.
Until businesses reimagine their systems to let humans and machines truly work together, even the most advanced AI will remain trapped in a broken process.
The next great innovation won’t come from making chatbots smarter.
It will come from making organizations smarter about how they use them.
#integration#aiworkflow#customerexperience #automation #oreilly #aifuture #cxinnovation #businesstransformation#digitalmoat