Your AI strategy is only as strong as your business foundation

09/23/2026

by Taylor Seville and Evan Placke

We recently had the opportunity to attend MaximoWorld, and while we expected to bring back a wealth of knowledge about IBM Maximo and Enterprise Asset Management (EAM), our greatest takeaway extended beyond any single tool. Instead, we came away with a clear understanding that AI is only as valuable as the business foundation it rests upon.

With conversations and breakout session topics ranging from intelligent asset management to automation, data, and emerging AI capabilities, one message stood out. The organizations that are best positioned to benefit from AI aren’t necessarily the ones adopting the newest tools. They’re the ones building the right foundation for those tools to succeed.

Across industries, organizations are exploring how AI can improve productivity, automate work, enhance decision-making, and create new sources of value. Yet, many are still working through fragmented data, legacy processes, disconnected systems, and organizational complexity.

That raises an important question: Are organizations trying to solve an AI adoption problem when they actually have a business transformation problem?

AI doesn’t fix a broken business foundation

It’s tempting to view AI as a shortcut. If a process is manual, automating it can seem like a quick win. If employees can’t find information, give them an AI assistant. If there is too much data, use AI to analyze it.

However, even the best AI implementations won’t automatically solve underlying problems. Consider an asset-intensive organization managing thousands of pieces of equipment across multiple facilities. Critical information may exist across EAM, ERP, engineering applications, GIS, IoT platforms, spreadsheets, and other systems.

The organization may have plenty of data, yet employees still struggle to find the right information they need to make an informed decision. Adding AI won’t solve that problem. AI needs context.

If data is incomplete or inconsistent, AI has less reliable information to work with and train from over time. If a process is poorly designed, automating it may simply make an inefficient process faster. If employees don’t trust AI-generated recommendations, even accurate insights may never translate into meaningful action.

So while these technologies may be new, the underlying transformation principles are not and may have been lingering for some time.

Data is the starting point

One of the most important considerations for AI readiness is data. AI-ready data is more than simply having a lot of information. Data needs to be trusted, accessible, connected, governed, and relevant to the business decision being made.

This is particularly important in asset management, where information can span across some of the most critical systems within any organization. Important tribal knowledge may also exist primarily in the minds of experienced employees. When those sources remain disconnected, organizations struggle to create a complete picture of their assets and operations.

Connecting that information creates an opportunity to move from simply storing data to using that data to make better-informed decisions. This is where AI becomes significantly more powerful.

Don’t automate what should be redesigned

Data isn’t the only foundation that matters. Before introducing AI or automation into a process, organizations should first evaluate whether the process is designed the right way to begin with.

Technology transformations often uncover years of accumulated complexity, encompassing manual approvals, duplicate data entry, spreadsheets, inconsistent processes, and legacy configurations that no longer reflect how the business operates.

A transformation can reproduce that complexity in a new system, or you can use the opportunity to rethink your processes.

A better approach is: Simplify > Standardize > Automate

Simplify: Understand the desired business outcome and eliminate unnecessary complexity.
Standardize: Establish a consistent process and establish clear ownership.
Automate: Determine where technology, automation, and AI can create additional value.

AI should accelerate good processes rather than attempt to compensate for bad ones.

People are part of the equation

AI readiness is also a people challenge. As experienced employees retire and organizations face increasingly complex workforces, capturing and sharing institutional knowledge becomes even more important.

AI can help make that knowledge more accessible. Instead of searching multiple systems or tracking down an experienced technician, an employee could use an AI-enabled assistant to surface relevant asset history, documentation, previous repairs, or other information needed at the point of work.

The goal isn’t necessarily to replace that expertise but to make it accessible to others.

That requires more than technology. Employees need to understand how AI will support and enhance their work, when to trust its recommendations, and where human accountability remains essential.

From AI experimentation to business value

Organizations shouldn’t start with the question, “Where can we use AI?” They should instead begin with the business problem.

Identify the opportunity: Where are you losing time, money, productivity, reliability, or customer value?
Assess readiness: Do you have the data, process maturity, technology, and organizational support ready to go in order to address it?
Start with a focused use case: Choose a problem where the potential value is meaningful and measurable.
Pilot and measure: Test whether the technology actually improves the business outcome, and not simply whether the technology works.
Scale what works: Use successful pilots to build broader capabilities across the organization.

This approach allows organizations to pursue AI opportunities while simultaneously strengthening the business foundation.

The conversations at MaximoWorld reinforced a broader lesson that applies well beyond asset management. Technology transformation is most successful when organizations address the business foundation before focusing on the technology itself.

Whether the initiative involves EAM, ERP, supply chain, manufacturing, data, analytics, or AI, the questions that come before the technology remain critical:

What business outcome are we trying to achieve?
How should the organization operate differently?
Do we have the right data to support those changes?
What technology can enable the future state?
And how do we prepare people to adopt it?

At Sendero, we approach transformation holistically through connecting strategy, people, processes, data, and technology to help organizations achieve measurable business outcomes. Is your organization ready to use AI to accelerate transformation? Reach out to one of our experts today.