Over many years of working through successive waves of enterprise technology and IT architecture, including fast-moving AI, I’ve heard the same question in different forms: “How can I build my business with AI while avoiding lock-in and tech debt?”
It came up again during a recent conversation about a major technology investment, when a CIO asked, “Should we really be making this commitment if the technology is changing constantly?” That resonated with me because it reflects a pattern I have seen across technology waves: the architecture must provide stability even as the capabilities around it continue to evolve.
For decades, the goal has been to build a technology foundation that serves the enterprise for five, seven or even 10 years. With the emergence of AI, some technologies become outdated before implementation is complete.
If shorter technology lifecycles are the challenge, adaptability is the answer. Instead of asking, “What will serve us for the next decade?” CIOs should ask, “How can we remain flexible as technology changes?” That requires being deliberate about what must remain durable and where the enterprise must be ready to adapt.
Think of it like a house. The foundation, walls and furnace are built to last, while paint, furniture and bathrooms change as needs and styles evolve. Enterprise architecture increasingly works the same way.
AI is separating the experience of work from the systems behind it
I recently heard about a company that now gives new employees a single onboarding tool: ChatGPT, connected to the enterprise systems and key data sources behind it. Instead of learning multiple applications, employees simply ask for the information or support they need, without knowing which underlying system provides it.
Of course, a single interface doesn’t mean blanket access to enterprise systems. This model requires security measures that ensure employees can access only the information and capabilities they’re authorized to use: carrying each employee’s identity and existing permissions through every request, limiting access to what’s necessary, tracking where information comes from, and logging retrievals and actions. Changes with significant business consequences should also require review.
This does change how we think about work and the technology that supports it. If agents are becoming the primary interface, the central question shifts from “Which application should employees use?” to “Can our data be accessed, understood and used securely by the interfaces and agents we adopt next?”
Adaptability means running at two speeds
Some technology capabilities should endure; others should evolve quickly. The mistake is treating everything as having the same lifecycle.
The durable layer includes ERP, HCM and other core systems, along with data, security and foundational architecture. These investments remain essential, but CIOs should design them around future business possibilities rather than solely around today’s processes.
The fast-moving layer comprises agents, interfaces, analytics, workflows, AI models and tools, experiments and business-specific solutions. The CIO’s role is to distinguish this layer from the durable core and ensure that rapidly evolving capabilities can be replaced without destabilizing the durable foundation.
CIOs need practical ways to change the underlying technology without disrupting the applications the business relies on. That starts with clear, consistent rules for how systems exchange data and interact with APIs, MCP connections and AI agents, keeping the implementation choices behind those boundaries. Before changing a model, agent or application, teams should confirm existing data and integrations will keep working, test security and expected behavior, and introduce changes in stages with a rollback plan if something goes wrong.
This two-speed model is a substantial departure from traditional enterprise IT management and from the way we did things when I was coming up in the tech world. It changes how CIOs plan investments, organize technology functions, structure vendor relationships and govern the connections between durable and fast-moving capabilities.
Invest thoughtfully for both durability and adaptability
In the pre-AI era, CIOs prioritized technologies with decade-long relevance. Today, they should avoid overspending on solutions prone to rapid obsolescence and invest in business outcomes and a durable foundation for intelligence.
Projects lasting more than six months warrant scrutiny: Will the solution remain relevant, or are we embedding processes that AI may soon handle; perhaps with a single prompt?
Durability extends beyond core systems to include the organization’s capacity to understand, evaluate and adopt new technology.
Today, most companies critically underinvest in skills. Knowing how to operate ChatGPT or Claude is not the same as working effectively with AI. OpenAI’s study found that AI power users outperform average users by sixfold. Upskilling gives CIOs leverage by helping employees redesign work and use AI effectively, while stronger leadership fluency facilitates new business models rather than merely supporting existing operations.
Most companies are also underinvesting in the data foundation. In West Monroe’s recent survey of 417 business leaders, AI governance, risk and compliance ranked as the lowest investment priority for the next 12 months. Traditionally, IT addressed data operations to ensure successful ERP implementation. Now, because data quality directly impacts AI value, investing in governance and accessibility is imperative.
CIOs need practices that preserve data meaning and reliability as platforms change. Those include assigning clear ownership, establishing authoritative sources and shared definitions, tracking data lineage and setting standards for quality and access. Definitions and rules should be maintained in portable documentation and data contracts, rather than embedding them in a single AI model or vendor platform, so they carry forward as technology evolves.
Break down IT silos to encourage adaptive architecture
Typically, CIOs organize operations around disciplines such as infrastructure, data, applications and security, with IT teams choosing an application and then defining processes and architecture around it. But as future architecture becomes less certain, CIOs must instead envision future business capabilities and design for change, bringing AI and data into architectural thinking alongside traditional cloud and application expertise.
Consider a reporting process that today moves from data analysis to Excel to PowerPoint to human review before a decision is made. An adaptive architecture focuses less on preserving each of those steps and more on the outcome: turning data into intelligence that informs a decision or action. Excel and PowerPoint may still have a role, but they no longer need to be hardwired into the process.
CIOs also need to break down silos. Teams often define architecture around a single suite, such as Salesforce or SAP, without considering alternatives. While working with an energy company recently, I saw how bringing analytics teams into the discussion shifted the focus from the ERP itself to how data should be accessed and used. Small, cross-functional teams can help create that broader view and design around business needs rather than individual platforms.
Build partnerships around future business outcomes
The most effective CIOs I work with are shifting their focus from simply controlling costs to driving value, actively partnering with the business, and reimagining how functions can evolve with AI, data and IT.
As vendor offerings expand across applications, analytics, agents and interfaces, CIOs need to balance integrated ecosystems with the flexibility to combine providers. Prioritizing open standards, multi-cloud support, data portability, interoperability and adaptable contracts can help organizations use the strongest elements of core systems while integrating agents and data across systems to adapt faster as needs evolve.
CIOs should build flexibility into vendor contracts, so changing providers doesn’t mean losing access to valuable data, context or governance rules. Contracts should provide for usable exports of data and metadata, documented interfaces that vendors keep current, advance notice of discontinued features, and support for testing portability and transitioning to another provider. They should also specify security and audit obligations throughout the relationship and during the transition.
The bottom line: buy with the future in mind. Don’t just ask if an application meets current requirements. Instead, envision what future business functions should look like, then derive technology needs from that vision. Shift the mindset from using HCM checklists to driving workforce optimization, from CRM checklists to revenue management, and from ERP checklists to enterprise data and intelligence.
Find the signal in the noise
As AI gets smarter and cheaper, the challenge will shift from affording it to connecting it to business outcomes. I believe the defining constraint in 2027 will be focus, not cost: CIOs must identify the few high-leverage priorities their organizations can absorb and execute well. That’s easier said than done.
There’s no established playbook for the AI era: we’re all experiencing it for the first time. CIOs face a flood of startups, models, architectural approaches, evolving standards, vendor claims and internal requests. The task becomes curation: deciding what deserves attention. Distinguishing signal from noise is becoming an essential leadership skill.
The new long game for CIOs
I believe CIOs are at a pivotal moment. They can remain excellent IT leaders or help the business rethink work through intelligence. In that sense, the role could evolve into what I call the chief intelligence officer: the leader who helps the enterprise turn fast-changing technology into durable business value.