AI Automation: A Mid-Market Guide to Strategic Value Creation
- Mart Lumeste
- Mar 26
- 4 min read
AI and automation have become the loudest conversations in the boardroom. Yet, a distinct disconnect remains between the volume of the conversation and the reality of the results. Industry surveys consistently show that while the adoption of AI tools is rising, tangible Return on Investment (ROI) is frequently absent. We see pilot programs that fizzle out, innovation labs that fail to impact the P&L, and roadmaps designed to impress rather than deliver.
At Intium, we have not only advised on AI and automation transformations but also invested heavily in streamlining our internal processes and creating value-added services. Through this experience, we have seen that the gap between hype and results is closed by a clear AI automation value creation strategy, strong leadership buy-in, and disciplined, step-by-step execution with continuous iteration. For investors aiming to protect exit multiples and operational leaders focused on efficiency, the approach to AI and automation must move beyond experimentation or skepticism to generate tangible business value.
Are your AI automation initiatives currently stuck in the pilot phase without a clear path to tangible ROI?
Here is how mid-market investors and operators should frame their AI and automation strategies and practices, regardless of whether the company is a tech company or a tech-enabled company in a more traditional sector, since delivering more customer value and delivering your products or services faster, with higher quality, and at a lower cost base, is applicable to all.
The Three Strategic Buckets of AI Automation in Value Creation
To move beyond the buzzwords, funds and portfolio companies must categorize their initiatives into three distinct buckets. If an initiative doesn't firmly fit one of these, it is likely a distraction.
1. Productivity and Internal Efficiency (The Bottom Line): This is the low-hanging fruit of value creation. The objective here is purely financial: improve the EBITDA margin by cutting costs, improving quality or delivering faster. Companies are at different maturity stages, so the tools and solutions that improve the bottom line can vary, but we often see that most real gains come from the unglamorous work of connecting systems, not necessarily from deploying a Large Language Model (LLM).
2. Product Innovation (The Top Line): To drive real top-line growth, the product roadmap needs to shift away from AI and automation initiatives created mainly to impress the board or investors, and instead focus on solving genuine customer problems and delivering tangible value. It’s essential to distinguish between superficial AI washing and features that truly enhance the user experience. Not only do these inflated AI efforts fail to improve revenue, but they can also hurt the bottom line, as the high COGS associated with AI can erode margins.
3. The Defensive Strategy (The Exit Story): What is the value creation story for the next investor? In a market where competitors are rapidly advancing with AI and automation, standing still only increases the risk of disruption. A future investor, whether in a tech or tech-enabled business, will think twice before paying a premium for a company running on a legacy tech stack that demands substantial capital just to catch up and integrate modern AI or automation capabilities. A thoughtful AI approach isn’t about chasing every bleeding-edge trend; it’s about making strategic investments to stay in step with the market and preserve a defensible exit multiple.
Execution: Bridging the Mid-Market "Pilot to Production" Gap
At the strategy and senior leadership level, it is critical to recognize that experimenting with AI and automation is low-cost relative to the risk of disruption. With tools widely available and the cost of intelligence declining, running AI pilots represents a relatively low-risk investment that can uncover significant value.
Success begins with meeting the company where it is. This means conducting thorough due diligence before acquiring an asset or performing an assessment if the goal is to transform an existing portfolio company. Evaluating the target’s data quality, process maturity, and the team’s readiness to adopt new ways of working lays the foundation for a realistic, executable value creation plan.
AI and automation should not be approached as one-off initiatives, but as long-term capabilities to develop and scale. The foundation for sustained success is to start small by defining a Minimum Viable Product (MVP) with clear objectives and applying a disciplined Build-Measure-Learn loop. This method allows teams to tackle a single task end-to-end, measure outcomes against KPIs, iterate, and test with real customers before scaling across the organization. By embedding this iterative approach, companies drive continuous improvement while avoiding the risks and costs of significant, upfront investments.
AI and automation are not just a technology challenge but rather a change management challenge. Success depends on active senior leadership buy-in, clear direction, and consistent follow-through to ensure employees embrace new ways of working, as AI and automation shift where human capital is deployed. Senior leaders also play a critical role in ensuring that technology initiatives deliver measurable business outcomes. CFOs and operational leaders are often best positioned to challenge technical teams, making sure pilots have a clear path to commercial impact before scaling.
Is your mid-market portfolio truly prepared for its next exit, or is a legacy tech stack putting your future valuation and exit multiples at risk?
