Performance Insights: a collaboration between Matt Unger, Founder, Mangrove Performance Group and Lynda Cotter, Co-Founder, Market Performance Group
For many companies, the AI journey has started in roughly the same place. Licenses get purchased. Training gets scheduled. Leadership announces that the organization is embracing AI. Then everyone waits for productivity to improve.
That is a reasonable place to start. It is not an AI strategy.
The real challenge is closing the gap between “we have AI” and “AI is changing how we operate.” Most organizations are still somewhere in between.
Adding AI to an existing workflow is not the same as redesigning that workflow around what AI can now do. The first can create incremental productivity. The second can change cost, speed, decision quality, organizational capacity, and ultimately enterprise value.
Access Is Not Adoption
Access means employees can use AI. Adoption means they consistently use it to change how they work. Transformation means those changes produce measurable business value.
Most companies have achieved access. Far fewer have achieved broad adoption.
We have seen this before with ERP, CRM, and business intelligence. Technology creates value when it changes how work gets done, not when it sits alongside existing processes as another optional tool.
AI raises the stakes because it is evolving far faster than previous technology cycles.
AI Is Bigger Than the Chatbot
ChatGPT, Claude, and similar platforms have demonstrated the power of generative AI for research, analysis, writing, and problem solving. But AI has already moved well beyond the chatbot.
Specialized platforms are emerging across R&D, regulatory, finance, procurement, supply chain, legal, and manufacturing. More importantly, AI is moving from answering questions to performing work.
An AI assistant can tell you how to analyze supplier performance. An AI-enabled workflow can continuously evaluate suppliers, identify concentration risk, analyze purchase-price variance, and surface sourcing opportunities.
One makes an employee more productive. The other changes how the function operates.
Start With the Business, Not the Technology.
One of the easiest mistakes is starting with the tool. A platform gets licensed, employees receive training, and six months later a handful of early adopters are using it while the organization largely operates as it did before.
The company purchased AI. It did not implement AI.
The sequence should be:
Process first. People second. Technology third.
Start by identifying where people spend significant time collecting and reconciling information, where decisions are delayed, where expensive talent performs repetitive analytical work, and where critical institutional knowledge resides with a few individuals.
Those are AI opportunities.
In beauty and personal care, consumer health, OTC, supplements, and life sciences, the opportunities extend across the enterprise: commercial intelligence, procurement and spend analytics, formulation and R&D, regulatory intelligence, forecasting, planning, manufacturing, and financial analysis.
Find the business friction first. Then determine where AI can remove it.
Adoption Is a Leadership Issue
AI adoption rarely fails because the technology does not work. It fails because organizations add AI without changing workflows, expectations, accountability, or incentives.
Employees are effectively asked to adopt something new while continuing to perform their jobs exactly as before. Successful implementation requires leadership sponsorship, redesigned workflows, clear expectations, appropriate governance, and measurement of whether behavior and performance are actually changing.
Training matters. Access matters. Neither is transformation.
Leadership should know where AI is being used, where workflows have materially changed, and what measurable value is being created.
For PE-backed companies, the question becomes even more important. AI should increasingly be viewed as another meaningful value-creation lever, with opportunities translated into productivity, growth, EBITDA, cash, and ultimately enterprise value.
The Differentiation Is in the Application
Most companies will ultimately have access to many of the same AI models and technologies. Access itself will not create sustainable competitive advantage.
The differentiation will come from application: how well a company understands its workflows, organizes its institutional knowledge, embeds AI into repeatable processes, and develops its people to work differently.
The leadership question is no longer:
“Are we investing in AI?”
Most companies can answer yes.
The better questions are:
Where is AI changing how our company operates? How broadly has that change been adopted? And what measurable value is it creating?
If the answers are unclear, the organization does not have an AI technology problem. It has an AI strategy gap. Mangrove Performance Group works with leadership teams to close the gap between AI access and AI-enabled performance. Please reach out at contact@mangroveperformancegroup.com.
Matt Unger, Founder, Mangrove Performance Group
Lynda Cotter, Co-Founder, Market Performance Group