Small firms face big threats. AI changes everything.
The recent AlixPartners study confirms what many of us in the software industry have suspected: mid-market software companies are caught in a dangerous vice grip. Over 100 mid-market software firms find themselves in what the consulting firm calls a “pressure cooker” of AI model development, with both growth and customer retention slowing significantly as AI capabilities accelerate.
This isn’t merely a competitive challenge. It’s an existential one.
As tech giants pour billions into proprietary AI tools, the development costs far exceed those of legacy software, squeezing profit margins for smaller SaaS firms. The traditional playbook that worked for decades is rapidly becoming obsolete. But there’s a critical perspective missing from this conversation.
The report recommends building AI agents as core products, switching to outcome-based pricing, and considering mergers or acquisitions. These are logical steps, but they don’t address the fundamental power imbalance at play. Having spent years helping small and mid-sized companies leverage AI to compete with corporate giants, I see both greater danger and greater opportunity than most analysts recognize.
The Real Nature of the Squeeze
The “big squeeze” isn’t just about resources or development capabilities. It’s about a fundamental shift in how software creates value. Legacy software automates processes. AI transforms them.
Mid-market companies face a triple threat: increasing development costs, compressed timelines, and customer expectations reset by AI-native experiences. When OpenAI can release a new capability and reach millions of users overnight, the traditional 12-18 month product development cycle becomes a competitive liability.
But here’s what the AlixPartners study overlooks: this moment of disruption creates unprecedented opportunities for agile, focused mid-market software companies willing to embrace a new approach.
Multi-Agent Systems: The Mid-Market Advantage
The report correctly identifies AI agents as critical components for future success. But it fails to recognize the transformative potential of multi-agent systems (MAS) architecture for mid-market companies.
While large tech companies build monolithic AI systems requiring massive resources, smaller companies can develop specialized, interconnected AI agents focused on specific domains. This approach allows for faster iteration, more targeted solutions, and often superior results for specific use cases.
In our work developing AI recruitment software, we’ve seen how a multi-agent approach allows smaller companies to outperform much larger competitors in specialized domains. Each agent focuses on specific tasks within the recruitment lifecycle, creating an ecosystem that’s greater than the sum of its parts.
This specialized approach gives mid-market companies a fighting chance against tech giants who must build for broad, generalized use cases.
Beyond Building Agents: Creating Hybrid Workforces
The report’s recommendation to build AI agents as core products doesn’t go far enough. The real opportunity lies in creating what we call a “Hybrid AI Workforce” that combines human intelligence with artificial intelligence.
This isn’t about replacing humans with AI. It’s about creating systems where humans and AI work together, each handling the tasks they do best. For mid-market software companies, this approach offers several advantages:
First, it creates immediate value by enhancing existing human capabilities rather than requiring a complete system overhaul. Second, it generates continuous feedback loops that improve the AI over time. Third, it addresses the human factors that often derail pure AI implementations.
The companies that will thrive aren’t just building AI features. They’re fundamentally reimagining how humans and machines work together.
Outcome-Based Models Require New Metrics
The AlixPartners recommendation to switch to outcome-based pricing models is sound but incomplete. To succeed with outcome-based pricing, companies need new metrics that capture the full value their AI creates.
Traditional SaaS metrics like user counts, storage usage, or transaction volumes fail to capture the transformative impact of well-implemented AI. Companies need to develop metrics around time saved, decision quality improvement, risk reduction, and opportunity creation.
These metrics must be concrete, measurable, and directly tied to customer business outcomes. Without them, outcome-based pricing becomes a race to the bottom rather than a value-capturing opportunity.
The Path Forward
For mid-market software companies caught in the AI squeeze, survival requires more than incremental adaptation. It demands a fundamental rethinking of product strategy, development approach, and business models.
The winners will be companies that embrace domain-specific multi-agent architectures, create true human-AI partnerships, develop new value metrics, and focus relentlessly on solving specific high-value problems better than anyone else.
The big tech companies have advantages in data, compute resources, and engineering talent. But they’re often constrained by the need to serve broad markets and maintain compatibility with existing products.
Mid-market companies can win by being more focused, more nimble, and more willing to reimagine how software creates value in an AI-first world.
The AI divide is real, and it’s growing. But with the right approach, mid-market software companies don’t just have to survive the squeeze. They can thrive in it.