In the world of enterprise technology, there is often palpable tension between the “Core” and the “Edge.”

The Core is the domain of the CIO, focused on stability, consolidation, and the massive machinery of ERP migrations. It’s a world of order. But out at the Edge, where business units actually meet their customers, the reality is messy, rapid, and organic.

At Method, we operate as scouts on this Edge. We don’t just build infrastructure; we craft the digital products and interfaces that human beings interact with every day.

From this vantage point, we are witnessing a dangerous gap widening in the digital transformation narrative. While enterprises are heavily invested in stabilizing their technological foundations, a wave of artificial intelligence is crashing against the shoreline, threatening to stress-test every operational process currently in place.

The danger isn’t that the technology won’t work. The danger is that we are deploying powerful engines into vehicles that our people can’t drive.

Here are two critical lessons from the field that suggest we need to fundamentally rethink our investment strategy: moving beyond just TechOps and DataOps to invest heavily in the Human Experience.

1. The “Standard” Trap: Why Bad UX is Sabotaging Your AI Strategy

For the last decade, standardization has defined the playbook for Digital Transformation. The logic is sound: consolidate fractured systems into a “standard” enterprise application to drive efficiency and data visibility. Yet time and again, we see these expensive implementations fail to deliver the promised value.

Too often, these standard applications ignore the user’s specific “job to be done,” their tasks at hand and desired outcomes. They prioritize backend architecture over frontend usability.

The 6% Reality

Recently, while working with a major industrial customer to modernize their global quality management system, we uncovered a startling metric. The backend infrastructure was flawless: the data pipes were connected, and the SAP integration was robust. Yet, during user testing, we found that only 6% of users could complete a standard task without error.

The interface was so friction-heavy that users simply abandoned it. They retreated to the tool they actually understood: Microsoft Excel. This isn’t an anomaly; it’s a symptom of treating User Experience (UX) as decoration rather than infrastructure.

The Hidden Cost: Starving the AI

When employees bypass your official enterprise app to work in offline spreadsheets, your operational data stays trapped on their desktops. It becomes “Hidden Data” or “Dark Data.”

This has catastrophic downstream effects on your AI strategy. AI models feed on data. If your operational reality is locked in thousands of siloed spreadsheets because your enterprise application is unusable, your AI models will starve. You can’t use a Large Language Model (LLM) on data you can’t see.

The Fix: The Economics of Design Systems

To solve this, we must stop viewing design as a “nice-to-have” and start viewing it as a scalable economic engine. The solution lies in robust Design Systems. A Design System isn’t just a style guide or a library of buttons; it’s a mechanism to enforce consistency, reliability, and trust at scale.

Many leaders hesitate here, assuming that building a custom User Layer is too expensive compared to buying “Standard” software. This view ignores the Crossover Point. While a Design System requires an upfront investment to build the foundational “kit of parts,” a powerful shift occurs once that foundation is laid. Teams stop building from scratch and start assembling.

The cost to deliver each subsequent feature plummets, and the delivery speed accelerates. This creates a permanent, compounding advantage. Instead of buying rigid standard software that forces your users into Shadow IT, you build a flexible, governed ecosystem that users actually adopt, ensuring that your data is captured correctly at the source.

2. Agentic AI: You’re No Longer Building Tools; You’re Designing Colleagues

The second shift we’re navigating is the transition from Generative AI (chatbots that talk) to Agentic AI (digital workers that do).

We are moving from a paradigm in which humans are “doers” — inputting data into forms — to one in which humans are “orchestrators” of AI agents. This shift is profound because it means IT is no longer just managing software assets and is no longer solely the provider of user tools. You’re inadvertently stepping into the realm of HR, the realm of Talent.

You’re designing the digital workforce.

Designing for “Systemic Patterns of Behavior”

If an AI agent is negotiating a procurement contract or routing a supply chain request, it’s acting as a proxy for your company. In this context, your brand is no longer just a logo; it is, as we define it, a systemic pattern of behavior. The “emotion” of the brand must translate into feelings of trust and reliability in the agents we deploy.

We can’t leave the behavior of these agents to chance. We must apply a Design Systems approach to Agentic AI. Just as we document visual standards for pixels to ensure visual consistency, we must document behavioral standards for agents to ensure ethical consistency.

This introduces the need for an “Agentic Runtime,” a governance layer that manages the trade-offs between performance, cost, and carbon footprint. For example, should an agent prioritize speed (high compute cost) or sustainability (lower carbon impact) for a non-urgent task?

These are design decisions, not just engineering configurations. This requires a human-centric discipline that incorporates ethics, sustainability, and brand values into the very code that runs the business.

The Convergence: Rethinking “Dual IT” for the AI Era

Ultimately, these points converge on a single, uncomfortable truth: your digital core’s success depends on the adoption at the edge.

We often use a simple equation to visualize this: Cx = Px * Ex. Customer Experience = Product Experience times Employee Experience. Customer experience is directly correlated with business value.

If your Employee Experience (Ex) is zero (because the tools are unusable or the agents are untrustworthy), your value realization is zero, no matter how powerful the technology (Px) is.

To survive this shift, Corporate IT must evolve. For years, the industry has debated the concept of “Dual IT” (or two-speed IT), the idea of splitting the organization into a stable, slow-moving “Core” and a fast, agile “Edge.”

But in the age of Agentic AI, the traditional definition of the “Edge” is insufficient. It’s not enough to simply be “agile.” The Edge must now be Human-Centric.

We need to extend the definition of Dual IT. Yes, the Core must remain the guardian of security, stability, and data standards. But the Edge can’t just be about “speed to market.” It must be about “quality of interaction.” It must focus on the Human Experience dimension, governing not just how software functions, but how it behaves.

This principle applies equally to humans and machines. Whether we’re designing an interface for an employee or a set of behavioral rules for an AI agent, the goal is the same: to create a “colleague” that is intuitive, trustworthy, and aligned with corporate values.

This change requires a portfolio rebalancing. In addition to your investments in TechOps and DataOps, you must now invest in Human Experience capabilities. We must equip our teams to conduct user research, map service blueprints, and design intuitive interfaces that capture clean data. Those are today’s unavoidable tasks. They’re also the foundation of the task we’ll all approach next month: designing agents we can trust.

The technology is ready. Are we designing a future our people can actually inhabit?