Council Post: Why AI Is Forcing Software Companies To Rethink How They're Built
Andrew Avanessian, CEO at Haiilo.
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I've spent my career scaling software businesses through periods of significant change—from shifts in technology to mergers and acquisitions to external events like the COVID-19 pandemic, which saw every organization rapidly adopting new ways of working.
All of these are different circumstances, but they each provide a consistent lesson: Reacting to the change is one part of the challenge, but accepting that you may have to alter your entire operating model is the real test.
Today, AI feels novel and unfamiliar because its capabilities are advancing at an extraordinary pace. But the underlying management challenge is one we've seen before: Something fundamental shifts, yet many organizations continue operating on assumptions from a previous era.
From my previous experiences, I’ve learned that successful leadership starts with recognizing that a change means you need to reevaluate the way your business is built.
When electricity was introduced into cotton mills during the Industrial Revolution, productivity didn't suddenly improve. The breakthroughs came when factory owners redesigned production lines around what the technology made possible.
As we approach a similar moment with AI, these are a few things I’ve been thinking about:
Stop optimizing yesterday's operating model.
It’s a fundamental error to treat AI as an efficiency program.
Many organizations congratulate themselves because they have seen teams save time, meetings become shorter, content produced faster and developers write more code.
The problem is that productivity gains alone rarely create lasting advantage. Accelerating an existing process often moves a bottleneck. A faster development team can expose slower decision-making, for example. Automated workflows can reveal unnecessary governance. Increased output can quickly collide with outdated approval processes.
The most common mistake I see across SaaS businesses is framing AI adoption around optimization. This sounds safe because it implies continuity, but it often keeps the old workflow and measurement systems in place.
This is where clarity of focus becomes critical. When everything is changing simultaneously, teams need to understand what truly matters. I've found that well‑defined objectives and key results (OKRs) create that radical focus, as they force you to articulate the important outcomes, strip away legacy assumptions and align teams around the priorities that genuinely accelerate the business.
At Haiilo, we've adopted a "zero day" mindset. Rather than asking how AI can improve existing processes, we ask: “If we were building this business today, with the technology now available to us, what would it look like?”
The answer is rarely a slightly more efficient version of what already exists. It often leads to different workflows, different responsibilities and different expectations around speed, ownership and accountability.
The real opportunity sits beyond simply deploying more AI, but in building "cleaner" systems around it.
Understand the changing economics of software.
For the last decade, software businesses could afford a degree of complexity. Growth covered inefficiency, new hires solved process problems, additional software solved workflow issues and teams expanded as demands increased.
That equation is becoming harder to justify. When activities that require days can be completed in hours, management teams can look more critically at where value is created.
At Haiilo, detailed RFP responses that once consumed days of specialist resources can now be produced in an hour. Likewise, our development cycles are shortening through agentic engineering. Cross-border collaboration between our German and English-speaking teams has also become easier.
With AI, activities that previously required significant coordination can be handled by smaller, highly leveraged teams.
I believe software companies will increasingly be judged on how effectively they convert capability into outcomes. This is one reason I believe the Rule of 40 will evolve. As AI increases productivity across the industry, investors will expect more. Businesses that once looked efficient may find themselves compared against organizations operating with materially higher leverage.
For leadership teams looking to deploy capital and resources, the question becomes increasingly simple: Does this investment strengthen our competitive position in a world where software development, content creation and knowledge work are becoming dramatically cheaper?
Competitive advantage is moving.
As AI moves further into the application layer, many traditional software advantages become less defensible.
Historically, strong user experience, workflow automation or access to specialist knowledge created meaningful differentiation. Increasingly, foundation models can replicate elements of that value themselves.
This means leaders need to think more carefully about their moat. Is it customer relationships? Proprietary data? Embedded workflows? Unique distribution advantages?
The uncomfortable reality is that some of the advantages you’ve relied on for years may not be as durable as they once appeared.
Beyond having the most sophisticated AI, software businesses will need to show that they offer something others don't, such as sitting closest to customer problems, possessing unique datasets and having earned enough trust to become embedded in day-to-day decision-making.
Be direct with communication.
Periods of change inevitably create uncertainty, and the instinct for many leaders is to soften the message to reassure people. In my experience, people are capable of dealing with difficult truths, and what creates anxiety is ambiguity.
As AI becomes embedded within organizations, leaders should be honest about the changes, evolving expectations and the new opportunities.
This applies both internally and externally. With your team, be clear about how roles will develop and how ways of working will transform. Then, spend as much time discussing the opportunities that come with it.
I've found that by showing your teams both what's happening and why it's important, they will have the clarity necessary to increase their impact, remove repetitive work and spend more time on activities that genuinely create value.
Have the right conversation.
Over the last year, conversations have become less about what AI can do and more about what organizations should look like when those capabilities become commonplace.
That's a discussion worth having. While AI may be developing at an extraordinary speed, the businesses that benefit most will be the ones prepared to rethink assumptions that may have been true for years, but no longer are.
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