There was a time when enterprise systems had clear borders. Data stayed on-site. Devices rarely left the office. Control was centralized.
Then came mobile phones and the cloud. Employees started working from anywhere. Critical tools moved outside the firewall. IT teams had to adapt fast.
It was a new way of working. One that required trust, flexibility, and a willingness to rethink what control really meant.
Today, with AI entering the enterprise, those same questions are back on the table. How do you scale innovation without losing grip on governance?
They’re the same questions Mark Vange is addressing through Autom8ly, helping businesses adopt AI in a way that’s accountable, secure, and built for the long game.
BlackBerry, Control, and the Cost of Playing It Safe
At its peak, BlackBerry was the blueprint for enterprise-ready tech.
Encrypted messaging, ironclad servers, physical keyboards built for business users, everything about it was tailored to corporate needs. It gave IT leaders control, compliance, and confidence.
But it also made a critical misstep: It never let go.
As iPhones and Androids gained ground, BlackBerry held fast to its original playbook. It clung to its proprietary OS, ignored shifting user expectations, and downplayed the rising importance of app ecosystems and seamless user experiences.
Meanwhile, workers brought their personal devices to the office, and companies eventually followed them. ‘Bring Your Own Device’ wasn’t a policy decision. It was a surrender to reality.
The message for AI is clear: Tools that win trust today can lose relevance tomorrow if they don’t evolve with the people who use them.
Here are five takeaways AI leaders would do well to remember:
- Security earns trust, but usability keeps it. BlackBerry offered unmatched encryption. But when the user experience fell behind, even government agencies moved on.
- The ecosystem matters. BlackBerry struggled because it couldn’t build a thriving developer community. AI solutions that ignore integration, extensibility, or collaboration with existing tools will face the same uphill battle.
- User-led adoption beats top-down mandates. Just like workers brought their iPhones to work, teams today are experimenting with ChatGPT and other AI tools on their own. If the enterprise can’t keep up, it loses both control and insight.
- Clinging to control can backfire. BlackBerry’s insistence on a walled-garden model ultimately isolated it. AI models, too, must be governed, but flexibility and transparency are key.
- Transformation isn’t just technical, it’s cultural. BlackBerry lost the plot when it treated the smartphone as a tool, not a personal experience. With AI, organizations must design for how people work.
When the Cloud Broke the Perimeter
At first, no one trusted the cloud.
For decades, enterprise IT had been built on control, racks of servers humming behind locked doors, data guarded like treasure.
But outside those data centers, a new model was taking shape. Startups with no legacy infrastructure, just an idea and an internet connection, began scaling at a pace that made Fortune 500s nervous.
Netflix rebuilt movie watching streaming on top of AWS. Stripe simplified payments without touching a bank branch. Airbnb and Uber skipped physical assets entirely, building billion-dollar businesses on rented code and compute.
While enterprises debated risk, these companies redefined agility.
We’re seeing that same hesitation play out with AI.
Executives worry about data privacy and lack of explainability. They should. But if that concern turns into paralysis, they risk missing the same curve their predecessors did. The one that separates early movers from everyone else.
Here are some lessons to keep in mind as you scale AI with the caution and care it deserves:
- Trust is built, not assumed. Security is a baseline. Like the early cloud giants, AI initiatives need transparency, controls, and real validation baked in from day one.
- Infrastructure is your runway. If your systems can’t scale with AI’s speed and complexity, you’ll stall out fast. Treat compute and data readiness like core business strategy.
- Adoption lives or dies by experience. Even the most secure platform can fail if users resist. AI success depends on training, clear communication, and designing around real-world workflows.
- Autonomy must be earned. Confidence metrics matter. Don’t hand the reins to AI until it’s proven. Start with oversight, measure impact, and scale autonomy like you would with any new team member.
Why Cooperative AI Is the Answer
The mobile and cloud eras showed us that technology alone doesn’t drive transformation; how we integrate it does. Security, scalability, usability, and trust aren’t just IT concerns; they are leadership challenges.
Cooperative AI meets that challenge head-on.
It brings the power of AI into the fold without blowing up what already works. It supports the people who know the processes best. And it builds trust, the only way that lasts: in stages, through proof, with real feedback loops.
That’s the approach companies like Autom8ly are taking under Mark Vange’s leadership: AI that earns its role. Not a black box. Not a top-down mandate. But a system designed to adapt, align, and grow with the enterprise it serves.
The future of AI shouldn’t feel like a leap of faith. It should feel like a step forward, with your team, your data, and your values intact.
