Mike Jerich, President and CEO at Flexera – Interview Series

Mike Jerich, President and CEO at Flexera, is an enterprise technology executive with more than 25 years of experience spanning software, cloud services, communications, and go-to-market leadership. He joined Flexera as President in May 2025 and was named President and CEO in July 2026 as part of a planned leadership succession, after helping advance the company’s platform strategy, partner ecosystem, and market position. Prior to Flexera, Jerich served as CEO of HungerRush and Chief Revenue Officer at ServiceMax and FinancialForce, while earlier leadership roles included Global Head of Sales & Marketing at IPC Systems, Chief Commercial Officer at IntelePeer, and Head of Indirect Channels at Level 3 Communications. His career has centered on scaling enterprise technology companies, building global sales organizations, developing channel and partnership strategies, and driving growth across SaaS, software, data, and cloud-based businesses.

Flexera is a technology management company focused on helping enterprises understand, govern, and optimize spending and risk across increasingly complex IT environments. Its Flexera One platform brings together IT asset management (ITAM), FinOps, SaaS management, cloud cost optimization, and AI cost management, giving organizations a unified view of technology assets, usage, costs, contracts, and risk across cloud, SaaS, software, hardware, and AI. The platform is underpinned by Flexera’s Technology Intelligence Platform and Technopedia, its proprietary technology data catalog covering millions of technology products. Flexera says it serves more than 50,000 customers worldwide and is increasingly positioning its platform as a system of intelligence for managing technology spend and risk as enterprises adopt cloud services and AI at greater scale.

You have spent much of your career scaling enterprise technology companies through sales, partnerships, and go-to-market leadership, and you served as Flexera’s president for more than a year before becoming CEO. How will that background shape your leadership priorities as Flexera enters its next stage of AI-driven growth?

The leadership roles I’ve held across enterprise technology companies have heavily influenced my current leadership style by reinforcing the importance of collaboration, accountability, and taking an action-oriented approach. That drive to act has never been more important than it is with the current AI landscape.

It’s easy to get caught up in how fast Enterprise AI is moving, so my role as CEO is to ensure our AI-driven growth is thoughtful, strategic, and proven. By prioritizing what customers need to do their own jobs better, we give our engineers room to innovate but also give absolute clarity. You’ve seen this in the news: it’s very easy to get disorderly with runaway AI spending and competing initiatives. As CEO, my job is to make sure we stay focused on helping our customers manage technology costs

Having worked closely with Flexera’s leadership, customers, and partners since May 2025, what opportunities or challenges did you identify that most influenced the company’s long-term AI strategy?

We’ve been talking about making sense of technology spend for years with hardware, software, SaaS, then cloud. AI is now part of that technology stack. It falls right into how we look at the world because, at the end of the day, a customer needs one unified view of the whole stack: a clear view of where they are spending, how to make sense of it, and most importantly, how to act on it. The “AI bill overspending” headlines you’re seeing in the news are something we’re hearing about every day. Organizations across all industries are actively trying to get their arms around AI spending and understand where those costs are coming from.

Working closely with Flexera’s customers and partners has made the opportunity clear: technology spend is becoming harder to see, manage and optimize as AI expands across teams and systems. That insight has shaped our strategy around giving leaders one trusted view of the entire technology stack.

Flexera is positioning Flexera One as a unified platform for managing technology across applications, AI agents, models, data platforms, cloud infrastructure, and compute. What does unified technology management look like in practice for a large enterprise?

In practice, unified technology management only works if you have one view across all spending areas. Without it, your IT asset management team sees software licenses, your cloud team sees FinOps views, and another team is likely scrambling to figure out AI token spend. So, if a CFO asks, “what are we spending on tech and is it under control?”, you could get three different answers from three different reports. In the simplest terms, it would be like driving a car and only seeing the speedometer without the fuel gauge or check engine light.

With Flexera One, companies get a unified view of their technology stack, so regardless of which leader wants the insight, there is a complete, visible foundation to use when making decisions across the entire lifecycle of an investment.

AI spending can be distributed across software subscriptions, token consumption, data platforms, graphics processing units, and cloud infrastructure. Where are organizations currently experiencing the greatest blind spots?

We saw this overwhelmingly in our own recent State of ITAM report where complete visibility across the IT estate fell to just 36%, down 7 percentage points year over year. The biggest blind spot is fragmented visibility. Within a single transaction, an agentic workflow can touch multiple technology assets, yet those costs are often tracked separately by different teams. When organizations can’t see the full picture, it’s difficult to understand the true cost and value of an AI initiative.

We’re also seeing a shift in how organizations think about AI economics. Token consumption can be a useful operational metric, but it doesn’t explain whether an AI investment is delivering business value. Leaders increasingly want to understand the cost of achieving an outcome, not simply the volume of AI consumed. That requires greater visibility, clearer accountability, and more consistent standards for measuring AI investments across the technology estate.

Flexera’s research found that only 31% of organizations have visibility into their AI software, while 59% reported an increase in wasted AI spending. Why are traditional technology management processes struggling to keep pace with enterprise AI adoption?

The findings in State of ITAM shouldn’t be surprising: AI is moving so fast that many enterprises don’t fully have their hands around it.

One of the biggest challenges we’re seeing is that ownership hasn’t kept pace with the speed of change. New investments often span multiple functions, with responsibility shared across IT, security, finance, and business teams. As a result, visibility and accountability can become fragmented.

Plus, AI doesn’t always flow top-down from functions like procurement the way traditional ERP systems did. Teams pick up a new tool because it makes them faster, sometimes outside centralized IT, creating shadow AI. Speed and experimentation are the point, and that’s not a bad instinct, but it means organizations are scaling AI workloads before they’ve built the muscle to manage what they cost.

The fact of the matter is that traditional technology management models were built for a more predictable environment. Today’s landscape moves much faster, creating new layers of cost, risk, and complexity that require a different approach. That’s why organizations need continuous visibility, governance, and optimization rather than periodic reviews. Helping customers make that shift and giving them a unified view of the entire tech stack is a key part of where we’re focused today.

How should organizations measure the return on their AI investments when improvements such as employee productivity, faster decision-making, and better customer experiences may be difficult to connect directly to spending?

Organizations should begin by defining the business outcome before they define the AI metric. While indicators like token consumption, adoption rates, and time savings can be useful operational insights, they don’t prove business value on their own.

The question I’d want an organization asking isn’t “how much AI are we using?” it’s “what changed because of it?”. Did a product development cycle get shorter? Did revenue go up, margins improve, or operational risk go down?

Not every experiment will produce an immediate financial return, but every initiative should have a defined objective and a point at which the organization decides whether to scale, revise, or stop it all together.

Flexera highlights challenges such as unused enterprise AI licenses alongside employees independently purchasing tools such as ChatGPT or Claude. How can companies address shadow AI and fragmented adoption without limiting useful experimentation?

The answer is not to restrict employee experimentation, but to make it visible and intentional. Organizations should first discover which AI tools are being used, by whom, and for what purpose. When analyzed, they are likely to find redundant purchases and personal employee payments when an approved subscription doesn’t meet their needs.

The organizations that will realize the greatest value from AI will be those that balance innovation with accountability. This looks like establishing clear governance, providing employees with guidance on responsible AI use, and creating a trusted view of AI activity across the board. When organizations can see how AI is being used, they can support experimentation, reduce unnecessary AI spend, and scale successful use cases with confidence.

AI agents can reason, retry tasks, call external services, and consume varying amounts of tokens and compute to complete a single workflow. How will agentic AI change the way companies forecast budgets, allocate costs, and establish financial guardrails?

This is where traditional budgeting just breaks down. The old model assumes a predictable unit cost like a seat, license, or subscription, and you multiply it out for the year. An agent doesn’t have a predictable unit cost like that, making it difficult to accurately forecast.

This calls for stronger financial guardrails and better visibility into how AI consumption translates into actual business outcomes.

The shift has to be toward measuring cost per completed task or per workflow, not cost per seat. That means attributing spend down to the agent and the workflow level instead of one big “AI” line item that nobody can explain. Guardrails for agentic AI can’t be an annual budget review; instead, they must be closer to real time. The key metric won’t be cost per token, it will be cost per successful outcome.

As organizations choose among proprietary models, open-source models, cloud AI platforms, and specialized vendors, how do you expect the economics of enterprise AI to evolve, and what role do you see Flexera playing in that market over the next several years?

Flexera has a front row seat to the evolution of the AI economy over the next several years. Our role is to give enterprises one trusted view across that entire ecosystem. We help customers understand what they are using, what it costs, where risk exists, and which AI investments are delivering value.

Thank you for the great interview, readers who wish to learn more should visit Flexera.