Do you know what you bought? Using AI and automation to understand IT assets in M&A.

The problem: Blind spots in IT diligence

Traditional IT diligence still relies heavily on interviews, manual data collection, outdated diagrams and incomplete system inventories. Acquired companies often lack accurate CMDBs (configuration management databases) or asset registers. Assets are scattered across departments, tracked in various tools or undocumented entirely. Even when documentation exists, it's usually outdated or inconsistent. 

How did we get here?

• Legacy systems and shadow IT complicate integration, often lacking formal records or introducing hidden risks. Even in well-managed environments, constant change makes documentation unreliable without automation.
• Mapping dependencies between systems is a perpetual challenge. Applications rely on databases, APIs, and cloud services in ways that aren't always visible, and manual efforts are error prone.
• Cultural and process gaps also play a role; documentation is rarely prioritized, and ownership is unclear, making it hard to maintain a complete and accurate infrastructure view.

The result? Buyers go into Day 1 with assumptions rather than certainty.

Consequences in the real world

Consider this real-world example: A global manufacturer acquired a mid-sized competitor and assumed its data center footprint was modern and easy to consolidate. Only after closing did they learn the target was running multiple aging data centers with limited documentation, complex dependencies, and significant technical debt. What should have been a 12-month migration dragged on for 18 months, burning millions of unplanned dollars and delaying synergy realization.

The consequences of blind spots like this can be significant:

• Unexpected costs: Undiscovered licenses, shadow IT, or duplicate applications inflate separation or integration budgets.
• Hidden risks: Legacy systems with unpatched vulnerabilities expose the acquiring company to compliance or security issues.
• Operational disruption: Overlooked dependencies between applications and infrastructure cause outages when systems are separated.
• Strategic delays: Without clarity, leaders struggle to make confident decisions about future-state architectures, delaying value creation.

In short, if you don't know what you bought, you risk buying more problems than value.

What knowing early gets you

Now flip the script. Imagine a diligence process where IT assets and dependencies are mapped with speed and precision. One global retailer leveraged automated discovery tools before acquiring a regional competitor. Within two weeks, they had a clear inventory of every server, application and dependency. When negotiations turned tense, they used this data to argue down the purchase price by tens of millions, citing the real costs of migrating the target's outdated systems.

Knowing early unlocks significant strategic advantages:

• Sharper negotiations: Accurate insight into IT costs and risks strengthens your hand at the bargaining table.
• Confidence in Day 1 readiness: You can design separation or integration roadmaps with fewer assumptions and greater accuracy.
• Faster synergy capture: Understanding overlapping systems enables you to accelerate consolidation and cost savings.
• Competitive differentiation: While many acquirers struggle post-close, your ability to execute quickly becomes a market advantage.

In other words, knowledge isn't just defensive; it puts you on offense. It allows acquirers to pivot faster, negotiate better, and realize deal value sooner.

Enter AI and automation

So how do you achieve this level of early clarity? Manual approaches won't get you there. This is where AI and automation come into play.

AI-driven discovery tools can crawl networks, applications, and data centers to automatically build real-time inventories of IT assets. Machine learning can detect anomalies, flag shadow IT, and categorize applications based on usage patterns. AI can even model dependencies between workloads and map critical business processes to the underlying technology stack.

Automation accelerates repetitive tasks: scanning endpoints, validating licenses, or reconciling discovered assets with existing records. Automated workflows reduce the time to gather data, improve accuracy, and free IT teams to focus on higher-value analysis.

Implementing these solutions requires a comprehensive tooling strategy to overcome discovery limitations posed by firewalls and other security or segmentation measures. A combination of agentless and agent-based methods can provide a complete picture of the infrastructure. Then, you'll need a framework, something like WWT's Asset 360, to pull together fragmented asset data while rationalizing data from OEM systems, telemetry platforms and financial sources.

When combined, AI and automation tools transform IT diligence from a static exercise into a dynamic, data-driven process. Instead of waiting weeks for incomplete spreadsheets, deal teams can access dashboards that confidently show what assets exist, how they interact, and what risks they pose.

What if you don't know?

Let's push the bounds of conventional thinking. What happens if you don't know - if IT diligence fails to uncover critical gaps? Here are a few scenarios: 

• Security exposure: A financial services firm discovered post-close that the target had hundreds of unsupported servers running sensitive workloads. Immediate remediation costs ran into millions of dollars, erasing the first year of projected synergies.
• Integration stalls: A healthcare acquirer assumed a patient scheduling system could be swapped out easily. They later discovered it depended on a niche vendor contract that wasn't disclosed. Integration stalled for nearly a year.
• Cost explosion: In a software acquisition, redundant ERP systems required parallel licensing until rationalization was complete. The unplanned expense delayed expected returns by two years.

These aren't minor inconveniences. They can shift the economics of a deal entirely.

Risk mitigation through clarity

The antidote is early, AI-powered visibility. By leveraging automated discovery and analysis before Day 1, acquirers can:

• Identify hidden costs: Flag redundant licenses, maintenance contracts, or shadow IT early to adjust budgets.
• Mitigate compliance risks: Detect unpatched systems, unsupported platforms, or regulatory gaps before they impact operations.
• Model separation scenarios: Understand application dependencies to avoid business disruption during carve-outs.
• Plan TSA exit strategies: Design transition service agreements with realistic timelines and cost structures.
• Accelerate integration: Quickly identify overlap and redundancy, setting the stage for rapid synergy capture.

For example, one acquirer used automated mapping to discover that 40% of the target's applications were already cloud compatible. With that knowledge, they accelerated cloud migration, cut infrastructure costs by 25%, and hit their synergy targets six months ahead of schedule.

Looking ahead: Turning diligence into a competitive weapon

In the future, the most successful acquirers will treat IT diligence not as a checkbox, but as a differentiator. AI and automation won't just prevent mistakes; they'll create an advantage. Imagine deal teams that can:

• Run side-by-side comparisons of IT footprints during target evaluation.
• Model integration timelines with real data before closing.
• Predict the total cost of ownership for IT environments across multiple acquisition scenarios.

In this vision, technology diligence becomes a source of strategic insight, not just risk management. Acquirers that invest in these capabilities today will find themselves moving faster and with greater confidence than competitors stuck in manual, assumption-driven approaches.

Final thoughts

"Do you know what you bought?" is more than a rhetorical question. In M&A, it's a litmus test for whether deal value is at risk or ready to be unlocked. AI and automation are changing the game, giving acquirers unprecedented clarity into IT assets and dependencies. The winners will be those who don't just react to IT surprises post-close, but who seize early knowledge as a strategic weapon.

Keep learning!