For mid-market companies, the conversation around artificial intelligence often begins in the wrong place. Leaders debate models, vendors, and infrastructure, assuming the breakthrough will come from deploying the right technology stack. But across industries, from manufacturing and logistics to private equity-backed portfolio companies, the fastest unlock for scalable AI adoption rarely starts with AI itself.
It starts with modernization.
The challenge is not simply upgrading systems; it is identifying which specific modernization steps, data integration, cloud evolution, workflow digitization, or system decoupling, create immediate operational clarity and allow AI to move from isolated pilots to embedded capability.
Strategy Before Systems
Cesar DOnofrio, CEO and co-founder of Making Sense
But before defining any modernization roadmap, the most important step is strategic clarity, says Cesar DOnofrio, CEO and co-founder of Making Sense, who has worked for decades with the digital transformation of US mid-market organizations and recognizes that legacy modernization is now a recurring structural constraint barrier to AI driving real economic impact in this segment.
“Modernization should not start with technology. It should start with a clear business objective. Are we trying to improve margins? Accelerate growth? Prepare for exit? Reduce operational friction? Without that alignment, modernization can easily turn into a large transformation effort without measurable impact,” he explains.
Once the objective is defined, the priorities become clearer, he reiterates. The first structural move, he says, is usually data integration.
“If data is fragmented or poorly governed, AI outputs won’t be trusted, and adoption will stall,” he explains.
The second is architectural flexibility, often through cloud evolution.
“Not because cloud is trendy, but because scalability and integration are prerequisites for AI to operate effectively,” says D’Onofrio.
And third, workflow digitization.
“AI creates value when it’s embedded in decision points. If processes remain manual or opaque, insights never translate into action. In short, define the value first. Then modernize the layers that directly enable that value,” he adds.
This sequence reflects a shift in thinking. Modernization is no longer an IT upgrade. It is a structural preparation for intelligence to operate inside the business.
Why Data Integration Delivers the Fastest Unlock
Among modernization steps, data integration consistently emerges as the highest-impact starting point. Without unified, reliable data, even the most sophisticated AI models cannot generate actionable outcomes.
Srinivas Devarakonda, Principal Data Scientist at Nisum, says the fastest unlock is almost always strategic data integration paired with workflow digitization, but specifically through the lens of observability.
“Mid-market companies often rush toward cloud migration as a silver bullet, but moving a disorganized mess to the cloud just gives you a more expensive, remote mess,” he says. “The real speed to value comes from consolidating historical data into a single, clean framework that reflects actual operational constraints.”
In practice, this means integrating operational realities into the data layer itself. For example, in forecasting, he says, the fastest unlock isn’t a better model; it is the integration of real-world variables like outbound fulfillment discipline and fraud controls into the data stream.
“When you digitize the workflow so that every step has a measurable financial consequence, you create the high fidelity environment AI needs to thrive,” he explains. “I tell my teams that we should solve design problems with design, not AI,” he adds.
By modernizing the system to distribute work intentionally between human decision makers and automated execution, the organization can create a plug-and-play architecture. This allows them to swap in AI agents for specific tasks, like routing or disposition, without rebuilding the entire stack.
“This structural clarity is what enables a mid-market firm to scale in a controlled, sustainable manner,” he says.
The implication is clear: AI scalability depends less on algorithmic sophistication and more on structural readiness.
Workflow Digitization: Turning Insight Into Action
Even when data exists, AI cannot create value if workflows remain trapped in manual processes, spreadsheets, or email chains. Workflow digitization transforms AI from an analytical layer into an operational driver.
Matt Roberts, Founder of Happy Operators, an AI consultancy, says it is imperative to find your light bearers.
“Every company has three or four people who are naturally curious, process-minded, and frustrated by inefficiency. Give them controlled access, put them in a hackathon environment isolated from production systems, and let them work on real problems,” he advises.
This approach accelerates adoption organically from within the organization. As Roberts says, this does two things. It surfaces use cases the leadership would never have thought of, and it builds internal proof points with actual ROI attached, which is what gets the rest of the organization moving.
“The tooling now exists for a non-developer to build meaningful solutions. The companies winning at AI adoption aren’t the ones with the biggest budgets. They’re the ones who stopped treating access as a risk and started treating it as a multiplier,” he says.
This democratization of capability represents a structural shift. AI adoption is no longer constrained solely by technical teams. It is increasingly driven by operational teams empowered with access to clean, usable systems.
The reliability of AI systems ultimately depends on the quality and accessibility of the data beneath them. Asparuh Koev, Founder of Transmetrics, warns that many organizations underestimate this risk.
“The biggest risk we see isn’t some dramatic system crash. It is actually an error of confidence. These new AI agents deliver their outputs with such polished assurance that it’s easy to forget they are only as good as the data underneath them. If your business data is still siloed or sitting in someone’s inbox then you aren’t actually automating intelligence. You are just scaling your potential for error,” he says.
Cleaning Workflows Often Delivers Faster ROI Than Cloud Migration
Cloud migration remains an important modernization step, but experts caution against treating it as a universal solution.
Larry Adams, Executive Chairman at AI software development firm Chromatics.AI, says for mid-market companies, the biggest gains usually come from cleaning up messy workflows.
“Anywhere approvals live in email threads or spreadsheets is friction. Structure those processes. Make the data usable. When information is consistent and accessible, teams move faster, and performance follows,” he advises.
Moving to the cloud is helpful. But simply relocating old problems does not fix them, he says. “What matters is whether your systems actually work together. If they do not, you will always be working harder than necessary.”
“Technology does not create advantage on its own. Discipline does. Companies that are willing to simplify, connect, and modernize their core operations will pull ahead. The rest will keep wondering why the promise never turns into results,” he adds.
This highlights a critical distinction. Cloud migration enables scale, but workflow clarity enables value.
Decoupling Data Unlocks AI Without Waiting for Full Replacement
For many mid-market companies, replacing core systems entirely is too costly, risky, or time-consuming. Instead, decoupling data from legacy infrastructure provides a faster path forward.
Jonathan Selby, Tech and Media Practice Lead at Founder Shield, says the fastest unlock isn’t a total system replacement; it’s data integration and cloud migration.
“You don’t necessarily need to rip out your core systems on day one, but you must ‘decouple’ your data from them,” he says.
Moving data into a centralized, cloud-based warehouse allows companies to create a “sandbox” where AI can actually play, he says.
“By decoupling the data, you eliminate the ‘all-or-nothing’ risk of system replacement. It lets AI prove its value immediately without waiting for a decade-long digital transformation,” he adds.
This approach allows companies to layer intelligence onto existing infrastructure without disrupting business continuity.
Modernization Is Becoming the Real AI Strategy
Across industries, we can see a pattern emerging. The fastest way to aim for scalable AI adoption is not simply to replace the full system, but to modernize foundational layers in a targeted manner. This means bringing your data together, digitizing how work actually gets done, and making your systems flexible enough to adapt and scale.
These steps transform AI from an experimental tool into an operational capability.
For mid-market companies operating under tight growth timelines, modernization is no longer optional. It is the prerequisite for intelligence to function at scale. The companies that move fastest aren’t the ones using the fanciest AI models. They’re the ones fixing their foundations so AI can actually work.
In the end, AI does not fail because of models. It fails because the systems are not ready to support it.
Article Co-Authored by Navanwita Bora Sachdev
