We recently invested in ChatGPT and Claude licenses for our Product team. On the surface, that’s a productivity decision. These tools help us write, analyze, prototype, and think more clearly. But that’s not the only reason. We did it as an investment in our people’s careers. The next generation of knowledge workers will be AI-native […]
We recently invested in ChatGPT and Claude licenses for our Product team.
On the surface, that’s a productivity decision. These tools help us write, analyze, prototype, and think more clearly. But that’s not the only reason.
We did it as an investment in our people’s careers.
The next generation of knowledge workers will be AI-native in the same way earlier cohorts were internet-native or social-media-native. They won’t “adopt” AI. They will assume it. It will be embedded in how they research, draft, analyze, and decide. How they think. Maybe even how they behave.
When we launched the program, I described it as the same kind of gap that existed between those of us who could program the VCR and our parents who stared at the blinking clock.
Then someone asked, “What’s a VCR?”
Face, meet Palm.
I shouldn’t have been surprised. We all have vocabulary and cultural references that don’t translate across generations. The wider the age difference, the more visible the gap. That was my point after all.
In the world of technology this happens faster. Vocabulary is retired faster. Skills become obsolete (on average) faster. Assumptions about what’s normal change faster.
I used to be an accomplished assembly language programmer. I could work at the register level, optimize memory usage, reason instruction by instruction. I even have a terrific story about debugging a device driver that people politely pretend to find interesting.
That was once a high-value capability.
I will never use it again. (Although I did learn durable problem-solving skills that still matter)
Not because it wasn’t important – but because the stack moved. Abstractions and patterns arose. The industry evolved, becoming simpler in some ways and more complex in others, at the same time.
And not just in the technology of software systems. The other day recently we realized something uncomfortable:
The same kind of generational gap is opening in enterprise software management.
The SaaS Gap: When the Mental Model Shifts
Cloud architectures and SaaS subscription models didn’t just change software delivery. They changed the perception of reality.
An entire generation of managers now assumes software:
If you start your career in that environment, those assumptions feel universal.
But installed and on-premise systems operate on different physics.
They require intentional upgrades. Lifecycle planning. Coordination across IT, operations, and finance. Awareness of support timelines and publisher roadmaps. Sometimes capital budgeting. The people side of Change Management.
Those disciplines used to be learned through experience. You lived through an end-of-support deadline. You managed a version migration. You navigated infrastructure refresh cycles.
Now many newer knowledge managers have never had that exposure.
That’s the SaaS gap.
Installed software is increasingly being managed with SaaS assumptions.
That creates quiet risk. It also creates a value gap.
Because part of what customers are paying for – risk mitigation, roadmap alignment, upgrade readiness, optionality, innovation – is not immediately visible. When everything works, those benefits don’t feel urgent.
Maintenance can look like overhead.
Upgrades can feel discretionary.
Advisory guidance can sound like selling.
This doesn’t make sense in their reality.
A Customer Conversation That Exposed the Gap
I recently had a conversation with a customer who wanted new functionality.
It was legitimate – had clear business value and broad market applicability. We agreed it aligned with where the product should go.
There was one issue.
They were on a very old, unsupported version.
Now let me be clear.
We are always going to act in our customers’ best interests. Even when clients are behind versions, we will look for workarounds. We will explore configuration paths. We will be pragmatic.
That’s partnership.
But we also have a responsibility.
We serve our customers best by investing new development – new functionality, architectural improvements, integrations – in current and forward versions of the product. That philosophy is expressed in our support policy (https://endpointas.com/support-lifecycle-policy/).
That’s not inflexibility. It’s stewardship.
Engineering investment should compound over time. Building meaningful new capability on outdated foundations widens the architectural gap, increases technical debt, and slows innovation for everyone.
And here’s the key point:
One of the core values of upgrading is closing the innovation gap.
Upgrading is not just about support eligibility. It’s not just about reducing risk.
It’s about staying eligible for new value.
When customers defer upgrades too long, they unintentionally widen the gap between where their system is and where innovation is happening.
Modern integrations. Automation enhancements. Analytics improvements. AI-enabled workflows.
Those live on the current architecture.
If you’re multiple versions behind, the conversation shifts from:
“How do we enable this?”
to
“How do we bridge the gap first?”
That gap is avoidable.
In SaaS, access to new functionality is assumed. It simply appears.
In installed environments, access to innovation requires alignment.
Upgrading isn’t a penalty.
It is the price of admission to the future of the platform. It’s how you prevent the gap from widening.
Closing the Translation Gap
This is the new reality for installed software vendors and resellers.
We don’t get to reset expectations. SaaS has reshaped financial psychology and operational assumptions.
So we have to lead differently.
First, we must accept that the burden of translation is ours.
If lifecycle physics aren’t intuitive, we have to translate them:
Second, lifecycle cannot be treated as an event. It must be operationalized – beyond the financial sense.
When lifecycle is embedded into operations, it feels strategic.
When it only surfaces at end-of-support, it feels disruptive.
Third, we must align with how clients now budget.
SaaS normalized cost of software as an operating expense. Installed software traditionally required episodic reinvestment. That’s not only a financial difference, it’s a labor difference as SaaS has enabled a “set it and forget it” mindset in budgets, while cost of upgrading invites more intense decision making.
That tension is real – and we can’t ignore it.
One lever we do control is commercial structure. License and maintenance models can evolve. We can operationalize more of the periodic expense associated with staying current. We can smooth the financial curve. We can design pricing structures that reduce the shock of large, infrequent upgrade investments.
But we also should be honest and clear to clients about the following.
Even if the financial model becomes smoother, the organizational change does not disappear.
Upgrades still require:
The people-side of change will always belong to the client organization.
We can reduce financial friction.
We can help plan modernization in multi-year roadmaps tied to budgeting cycles.
We can phase initiatives to reduce operational disruption.
But we cannot eliminate the need for periodic organizational alignment.
And we shouldn’t pretend that we can.
Modernization is not just a line item. It is an operating discipline.
The goal isn’t to make upgrades invisible.
The goal is to make them intentional, planned, and aligned with value creation – financially and organizationally.
Fourth, we must explicitly address the value problem.
If part of what the client is paying for is invisible, we must make it visible.
Risk reduction. Innovation. Roadmap access. Integration readiness. Strategic flexibility.
We have to make meaningful, business-valued investments in our products, especially those full-features, stable ones which may be near the end of the product lifecycle to gain that visibility.
If the relationship is purely transactional – invoice in exchange for entitlement – it will be challenged – this means deferrals and discounts.
If it is advisory – grounded in foresight, lifecycle planning, and forward alignment – it becomes seen as an investment having a return.
A current, supported system unlocks integration, automation, analytics, and AI initiatives.
A neglected system constrains them.
That’s the real difference.
Mind the Gap
AI is creating a generational gap in knowledge work.
SaaS has already created one in enterprise software management.
In both cases, assumptions change faster than experience transfers.
The answer isn’t to resist change.
It’s to recognize the gaps – and close them intentionally.
Installed software is not obsolete.
But the shared understanding of how it must be governed is fading.
The systems our clients depend on still obey software physics. They still require intentional modernization. They still carry risk if neglected.
Our job is to make the gaps visible.
Because the biggest risk isn’t being behind on a version.
It’s letting the gap grow so wide that you can’t cross it.
Next week: Keeping the plates spinning. Be here. Aloha.
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