Before you automate, modernize

David Zey Gerry Pitt

August 10, 2026

Artificial intelligence (AI) has materially moved from buzzword to boardroom priority in the last two quarters.

Business owners and executive teams are asking the same question: How can AI help us improve productivity, reduce costs and gain a competitive advantage? 

The excitement is justified.

AI is already helping organizations automate routine work, improve customer service, analyze data faster and support better decision-making.

But many organizations are learning that implementing AI is far more complex than simply purchasing a software platform or turning on a chatbot, despite the common expectation that it should be that easy.

The biggest obstacle isn’t AI itself – it’s the foundation beneath it. 

Many organizations are trying to build AI capabilities on decades-old technology, inconsistent data, manual business processes and workforce skill gaps.

These underlying issues have existed for years, but AI has brought them into focus. 

The businesses seeing the greatest success with AI aren’t necessarily those spending the most money.

They’re the ones that invested first in becoming ready and using AI to assist in that preparation. 

Legacy isn’t just old technology 

When executives hear the term legacy technology, they often picture aging servers, outdated computers or software that hasn’t been upgraded in years. 

Those are certainly part of the equation, but legacy extends much further than hardware. 

Legacy can include disconnected business applications that don’t communicate with one another.

It can be spreadsheets that employees manually update every week because systems can’t exchange information automatically.

It can be paper approval processes, outdated ERP customizations, undocumented procedures or critical knowledge that exists only in the minds of long-time employees. 

Many of these systems still work.

In fact, they’ve often served the business well for years. 

The question is whether your systems are a sail or an anchor to your corporate ship. 

Legacy isn’t defined by age.

It’s defined by how much it limits your organization’s ability to adapt, innovate and grow. 

AI doesn’t create problems, it reveals them 

Artificial intelligence depends on quality information and repeatable processes.

If those don’t exist, AI simply magnifies existing weaknesses in the status quo. 

If customer data is incomplete or inconsistent, AI-generated insights will be unreliable. 

If sales, operations and finance each maintain separate versions of the truth, AI cannot produce authoritative analysis. 

If employees rely on manual workarounds because systems aren’t integrated, AI has little opportunity to automate those manual workflows. 

If business processes and definitions vary from one department to another, AI cannot consistently deliver accurate results. 

In other words, AI doesn’t create organizational problems – it exposes them at face value. 

That’s why successful AI initiatives almost always begin with foundational improvements to support the AI transformation. 

Organizations need trusted data, integrated systems, documented business processes and strong governance before AI can consistently deliver measurable business value.

Though that may sound reminiscent of Industry 4.0 or another large-scale modernization initiative, there’s an important difference: AI can help accelerate many of those foundational improvements.

This is really the age-old challenge of working “on” the business versus working “in” the business.

AI can help organizations strategically work “on” their business by analyzing processes, documenting workflows, identifying inefficiencies and recommending improvements.

By strengthening these foundations, organizations are better positioned for AI to work “in” the business, where it can automate tasks, augment decision-making and optimize day-to-day operations.

In other words, AI is not only the beneficiary of operational maturity.

It can also be a catalyst for achieving it.

But ultimately, artificial intelligence is only as smart as the business processes, data and people that enable it. 

Modernization finally has a business case 

For many years, CIOs struggled to justify modernization projects. 

Replacing aging infrastructure, cleaning up data or simplifying business processes often sounded like expensive maintenance rather than strategic investment.

Executive leadership naturally asked, “What’s the return?”

The answer wasn’t always easy to quantify. 

Today, the conversation has changed. 

Modernization is no longer simply about replacing old technology.

It’s about enabling new business capabilities. 

Organizations with modern systems are better positioned to deploy AI assistants that improve employee productivity.

They can automate repetitive administrative work, analyze business trends faster, respond to customers more quickly and make better-informed decisions. 

Technology modernization is no longer simply an investment in maintaining existing systems – it’s an investment in future growth.

By improving AI readiness through modern platforms, integrated data and scalable infrastructure, organizations position themselves to rapidly adopt emerging AI capabilities that didn’t exist just a few months ago and will continue to evolve at an unprecedented pace.

Industry analysts have noted that leading CIOs are reframing modernization initiatives as AI enablement projects.

Instead of asking executives to fund infrastructure upgrades, they’re demonstrating how modern technology creates the foundation required for AI, automation and digital transformation. 

That shift in perspective resonates because the return on investment is becoming increasingly visible. 

Modernization requires more than technology 

Preparing your organization for AI requires attention in four key areas:

Technology 

Modern infrastructure remains essential. 

That doesn’t necessarily mean replacing every server or migrating every application to the cloud. 

It does mean evaluating whether your core systems are secure, supported, integrated and capable of supporting future business needs. 

Technology should remove friction, not create it. 

Data 

Data has become one of every organization’s most valuable assets. 

Unfortunately, many businesses struggle with duplicate records, inconsistent information, missing documentation and conflicting reports. 

Before or while implementing AI, organizations should focus on improving data quality, establishing ownership and creating governance around how information is collected and maintained. 

Good decisions begin with trustworthy data. 

Business processes 

Many organizations have accumulated years of workarounds and exceptions. 

Employees often perform duplicate data entry, manually route approvals or create spreadsheets because “that’s how we’ve always done it.”

These inefficiencies become barriers to automation. 

Standardizing workflows, documenting procedures and eliminating unnecessary steps not only improve operational efficiency today but also create meaningful knowledge and context for AI to effectively automate the work of tomorrow. 

People and skills 

Technology alone doesn’t transform organizations.

People do. 

As AI becomes more integrated into everyday business operations, employees will need new skills to work alongside it effectively. 

That includes understanding how to evaluate AI-generated information, protect sensitive data, ask better questions and identify appropriate business use cases. 

Organizations that invest in employee development will realize greater value from their technology investments than those relying solely on software. 

Where business leaders should begin 

For many small and mid-sized businesses, the prospect of modernization can feel overwhelming. 

The good news is it doesn’t require replacing everything at once. 

Start by asking a few simple questions: 

• Where are employees spending the most time on repetitive administrative work? 
• Which business processes require duplicate data entry? 
• What reports take days instead of minutes to produce? 
• Where is critical knowledge concentrated in only one or two individuals? 
• Which business data do leaders trust the least? 

The answers often reveal opportunities to improve both operational efficiency and AI readiness. 

Rather than launching an “AI project,” focus first on solving a meaningful business problem. 

Improving customer service, streamlining invoice processing, enhancing production scheduling or modernizing document management can all produce immediate benefits while establishing the groundwork for AI initiatives. 

Small, measurable successes create momentum and help build organizational confidence and future capability. 

Build the foundation before the house 

Many business leaders want to take advantage of artificial intelligence – that’s understandable. 

AI has enormous potential to improve productivity, accelerate decision-making and create competitive advantage. 

But AI isn’t a shortcut around outdated systems, inconsistent data or inefficient processes.

It’s an amplifier. 

If your business has built a strong operational foundation, AI can help accelerate growth and innovation.

If that foundation is weak, AI will simply expose the cracks more quickly. 

The organizations that thrive over the next decade won’t necessarily be those with the most sophisticated AI tools.

They’ll be the ones with modern technology, trusted data, efficient business processes and employees prepared to embrace change. 

Technology modernization is no longer just an IT initiative.

It’s a strategic business investment that enables the next generation of growth. 

The question is no longer whether your business should invest in AI. 

The real question is whether your business is ready for AI.