From Vision to Value: How Technology Maturity Powers Executive Strategy

Every business wants technology to create more value. Right now, that conversation often starts with AI. Business leaders see what generative AI, automation, and machine learning can do and want to know how these tools can improve efficiency, reduce costs, and create new opportunities.

The problem is that a powerful new tool cannot compensate for unreliable infrastructure, weak cybersecurity, scattered data, or a business process that nobody performs the same way twice. Technology can accelerate a good process, but it can also accelerate a bad one.

In our latest episode of Shh... IT Happens, our host Veronica Sands joined me and John Ohlwiler of Sentry Technology Solutions for a conversation about technology maturity and why businesses need to build the right foundation before jumping into the latest technology. John walked us through his Technology Maturity Model, which provides a practical way for business leaders to understand where their organization stands and what needs to happen before technology can become a real driver of growth.

We also got into AI governance, shadow AI, intellectual property, and why IT needs a seat at the table when important business decisions are being made.

The shiny new tool is rarely the starting point

Veronica set up the discussion with a point I think most business leaders can relate to. There is no shortage of shiny new technology competing for our attention. The challenge is figuring out what is actually right for your business right now.

John's Technology Maturity Model provides a useful framework for doing that. Rather than looking at technology as a checklist of unrelated IT projects, he organizes it into four stages that build on one another: operational IT, cybersecurity and compliance, business integration, and business innovation.

You can try to skip ahead, but that is where businesses tend to fall into what I jokingly called the "ready, fire, aim" approach. The goal is not to slow innovation down. The goal is to give innovation a solid foundation.

Stage One: Get operational IT right

The first stage is operational IT. This is the technology required to keep your business functioning every day, including helpdesk support, employee onboarding and offboarding, connectivity, devices, Microsoft 365, patching, system availability, and the other routine responsibilities that keep employees productive.

These functions may not generate much excitement in an executive meeting, but every higher-level technology initiative depends on them. If employees regularly lose access to systems, accounts are poorly managed, devices are unreliable, or basic support is inconsistent, introducing more sophisticated technology can create additional complexity instead of meaningful improvement.

At this stage, what you are really looking for is predictability. Technology needs to work consistently enough that your employees can focus on their jobs and leadership can stop spending its time dealing with recurring IT problems.

Stage Two: Build security and compliance into the foundation

The second level is cybersecurity and compliance. We have moved far beyond the days when installing antivirus software and renewing it once a year qualified as a cybersecurity strategy.

Today's environment includes identity protection, multifactor authentication, endpoint security, backups, monitoring, vulnerability management, employee training, cybersecurity frameworks, cyber insurance requirements, and industry-specific compliance obligations. As businesses add cloud platforms, connect more systems, and introduce AI tools, this layer becomes even more important.

Every new connection creates another place where business information may be stored, processed, shared, or exposed. If you want technology to help your organization move faster, you also need confidence that the underlying environment is being appropriately secured.

AI may be learning more than you realize

Before we got into John's maturity model, I brought up an article that raised an interesting concern around AI sometimes referred to as "AI Sherlocking."

Businesses are becoming more aware that they need to think carefully about what information employees enter into public AI platforms. Enterprise agreements can provide additional protections around inputs and outputs, but there is another part of the equation worth considering: the interaction that happens between those two points.

Imagine one of your experienced employees asking an AI platform to analyze something. The first answer is wrong, so the employee tells it to exclude certain information, prioritize a particular variable, reorganize the analysis, or apply a specific decision-making method. They keep refining the output until the AI approaches the problem the way they would.

There is potentially valuable intellectual property inside that process.

Your company's IP is not limited to documents, databases, formulas, or source code. It can also exist in institutional knowledge, processes, judgment, and the methods your people use to turn information into decisions.

Veronica boiled that discussion down to a useful question for business owners: what would the impact be if a competitor got access to something like this?

That does not mean businesses should avoid AI. It means we need to understand the terms, privacy controls, governance, ownership, and data practices surrounding the tools our employees use. For organizations with particularly sensitive information or workflows, privately hosted AI may also deserve consideration.

Stage Three: Integration means getting everybody in the room

The third stage of John's model is business integration, and I think this is where the conversation becomes especially useful for executives.

Integration can mean connecting software platforms or using single sign-on so employees can securely access multiple systems. It also means getting HR, finance, operations, leadership, and IT talking to each other before important technology decisions are made.

John shared an example involving a large organization implementing Workday. The platform itself was capable, but important conversations among HR, IT, cybersecurity, and finance did not happen early enough. Identity structures were inconsistent, single sign-on was not properly addressed, financial processes created complications, and some capabilities could not be used as intended.

The cleanup became more painful than getting everyone involved from the beginning would have been.

That is one of the points I emphasized during our conversation. At Solve iT, we tend to have much greater success when clients involve us in these discussions early. Technology touches almost every pillar of a modern business. Finance understands financial requirements. HR understands employee processes. Operations understands workflows. IT understands systems, identity, security, and data.

You need those perspectives in the room before the implementation starts.

You cannot automate a process that does not really exist

Business integration also forces you to look at how work actually gets done.

John gave a great example. Put 10 or 20 employees in a room who supposedly perform the same job, and you may discover that all of them perform it differently. Sally has the best method for one part of the process. Jim has a better approach to another. Someone else is still sending a report every Friday because an SOP written three years ago says to send it, even though a new dashboard has made that report obsolete.

Before you automate that process, you need to define it.

That means determining how the work should be done, identifying who owns the process, deciding how success will be measured, reviewing your SOPs, and understanding the data involved. Otherwise, you can spend a lot of money automating inefficiency.

This is where technology maturity and business maturity start to overlap. AI cannot solve a process that the business itself does not understand.

Stage Four: Technology becomes a business advantage

The fourth stage is business innovation. This is where everyone wants to play right now because it includes AI, automation, machine learning, predictive analytics, and other technologies that can potentially produce significant returns.

John used Domino's as an example, and I like it because it predates the current AI frenzy.

Domino's connected its ordering systems, stores, employees, preparation process, delivery process, customer communications, and customer feedback into a digital experience. Customers gained visibility into their orders, while the company gained meaningful information about what was happening at nearly every step of the process.

That required the earlier maturity stages. The underlying technology had to work reliably. Payments and customer information had to be secured. Stores and systems had to be integrated. Processes had to be consistent enough to measure.

AI can now help smaller businesses accomplish things that previously required the resources of companies like Domino's or Target. That is a tremendous opportunity, but AI is still only one arrow in the quiver. Good leaders need to understand their bottlenecks, processes, desired outcomes, and data before deciding which technology should solve the problem.

Technology maturity gives executives and IT a common language

One reason I like John's model is that it helps bridge a communication gap I have seen throughout my career.

Technology professionals can fall into technobabble pretty quickly. We might understand why identity management, data classification, security controls, or system integration matters, but executives need to understand those issues in terms of business risk, productivity, cost, growth, and outcomes.

The maturity model creates a common language.

Instead of looking at technology as a pile of projects and products, leadership can look at where the business is today, where it wants to go, and what capabilities need to exist between those two points. That also makes it easier for IT professionals to explain why some foundational work needs to happen before the organization invests heavily in the next big initiative.

Shadow AI is already inside many businesses

We closed the episode with another AI issue that deserves more attention: shadow AI.

Employees are already using AI tools at work, and many organizations do not have a complete picture of what is being used. An employee might use a personal ChatGPT account, upload company information to an unapproved platform, or build an AI workflow that gradually becomes important to an entire department.

John shared an example that illustrates how messy this can become. An employee had created a workflow connected to a personal AI account, and other people in the department had become dependent on it. When that employee was preparing to leave the company, suddenly everyone had to figure out what belonged to the employee, what belonged to the business, and what would happen to the workflow the department was relying on.

That is a terrible time to start thinking about AI governance.

As Veronica put it, you have to shine a spotlight on what is happening. Businesses need visibility into the AI tools employees are using, what those tools are being used for, and where company information is going.

Enterprise licensing, acceptable-use policies, employee education, access controls, and documented ownership of AI-created business assets all need to become part of the conversation. AI adoption is already happening inside businesses, whether leadership has formally approved it or not.

Know where you are before deciding where to go

One theme kept surfacing throughout our latest episode: major technology problems often begin with seemingly small business decisions that quietly add up over time.

Someone buys an application without involving IT. An old process never gets updated. Two departments maintain different versions of the same data. Employees start using personal AI accounts because the company has not provided an approved alternative. Leadership decides it wants automation before anyone has defined what should actually be automated.

Individually, those decisions may not look particularly dangerous. Together, they tell you a lot about the organization's technology maturity.

Operational IT creates stability. Cybersecurity and compliance help manage risk. Business integration connects your people, processes, systems, and data. Once those pieces are working together, innovation has a much better chance of creating measurable business value.

AI may be the shiny new tool everyone wants to discuss, but your results will depend heavily on the foundation underneath it.

At Solve iT, we help business leaders understand that foundation, identify security and operational gaps, and develop a practical technology strategy around where the business is going. If you are considering AI, automation, new software, or another