(Part I of the Series)

For over 20 years, I have focused on designing the best interfaces to drive the highest possible adoption rates. In my earliest years, I was invited to lunch with three individuals: one from IBM, another from Oracle (Siebel CRM), and the CIO of General Motors. Midway through lunch, the CIO asked me, "What is the most important part of software?"

I thought for a moment and replied, "To me, it’s the users and their ability to rapidly and effectively use it." A week later, I was invited to join a global team tasked with deploying a worldwide CRM solution.

That moment solidified a core principle that has guided me ever since: whenever a front-end or interface is within my scope, I make it a priority to understand the users and the applications they rely on daily. From car dashboards to mobile devices, versions of Office, intranets, and frequently used websites, I made it my mission to align interfaces with user needs and behaviors.

Through numerous failures and experiments, I learned that users tend to fall into distinct clusters: some gravitate towards workflow-intuitive UIs or UX, while others prefer more sequential, task-oriented designs. This realization drove me to explore and adapt systems like Great Plains, Salesforce, SAP, and NetSuite, all with the goal of improving adoption and usability for the intended audience.

The cost of these platforms, in terms of time and training, is so significant that a company’s ROI is directly tied to its users’ ability to seamlessly perform their jobs within the new software. The easier it is to use, the less frustration it causes, accelerating ROI.

Fast forward to 2011, when we began integrating surrogates (AI agents) into software systems. We saw a world of opportunity—a chance to exercise creative control over what users “had to see” versus what they “needed to see” to accomplish their tasks, whether routines, processes, or workflows.

We used the term "surrogates" because we envisioned AI as an apprentice in a Mentor-to-Apprentice relationship (or Human-in-the-Middle, SME-in-the-Middle). These AI helpers worked quietly in the background, learning and gradually transitioning to recommending. This shift enabled us to transform the UI from “input-driven” forms and step-by-step actions into “output-driven” systems where users simply approved or rejected outcomes.

This adaptation elevated the workflow from a tedious, time-consuming process to a more managerial and decision-focused state. Input forms were revealed only when a rejection occurred, allowing the user to make corrections, which were then processed and integrated into the training of the AI models. This approach let us remove unnecessary UI elements, reducing psychological strain on staff and simplifying their interactions into a binary “Go” or “No-Go” decision-making process.

This decision-based UI not only empowered workers to handle more complex inputs—inputs that would eventually be automated as well—but also enhanced productivity. Workers shifted from being a cost to becoming a capacity metric, and HR onboarding times shrank from weeks to mere hours.

That was the journey from 2003 to 2012. But what about today?

But today, the question has evolved: why have an interface at all?

Today, the question has shifted from improving interfaces to questioning their very necessity. Why have an interface at all? Generative AI is redefining user interactions, creating systems that don’t require users to learn, adapt, or even notice their presence. This shift isn’t about eliminating interfaces entirely but about making them invisible—seamlessly integrated into workflows and environments.

This marks the next chapter in the evolution of adaptive systems that not only respond but anticipate. Imagine tools that learn your preferences so well that they eliminate friction entirely. These systems don’t just hyper-personalize; they adapt uniquely to individual users and roles, crafting experiences that feel intuitive and effortless.

In exploring this evolution, we uncover the science of hyper-personalization and its transformative impact—not only on user experience but also on organizational efficiency. From reducing the Total Cost of Ownership (TCO) to driving an impressive Return on Investment (ROI), these advancements trace back to the core principle I learned early in my career: understanding the user’s world and designing technology to work for them, not the other way around.

Yet, hyper-personalization raises new questions. What happens when AI becomes a surrogate decision-maker? These "invisible helpers," as I call them, have advanced far beyond the apprentice-mentor dynamic of earlier systems. Today, they act as collaborators, filtering out unnecessary details and presenting only what’s essential. This shift alters not just how we work but also how we perceive our roles within workflows. Future discussions will delve into the ethical and psychological implications of delegating more to AI while maintaining a human-centered approach.

Looking ahead, the concept of interfaces may dissolve entirely. Voice commands, contextual awareness, and generative design are paving the way for "no interface" systems. In this vision of the future, technology becomes an extension of the user, adapting seamlessly to needs and fulfilling them before they even arise.

This series will explore these transformative shifts step by step. Each chapter will build upon the last, examining how adaptive interfaces are reshaping industries, redefining work, and challenging our understanding of user experience. We’ll explore how AI’s role in design impacts adoption, productivity, and the metrics of success.

This is just the beginning. The future of interfaces isn’t about simplicity—it’s about systems that truly understand. As we step into this new era, I invite you to join me in exploring how generative AI and hyper-personalization will shape what’s next—not just for technology, but for how we live, work, and connect.

How are the companies affected

The impact of this transformation will be profound across all functional groups within organizations. As tasks shift from manual input to decision-making, the criteria for evaluating talent and success will undergo a fundamental change. The classic CV or resume, which once emphasized proficiency with specific applications or tools, will instead focus on a candidate’s ability to make sound decisions within critical workflows.

This evolution places a renewed emphasis on the human capacity for strategic thinking, judgment, and prioritization. It’s no longer about how well someone can navigate software but about the quality and impact of their decisions. In this environment, success will be measured by how effectively individuals can train AI systems to understand organizational priorities, balance competing demands, and deliver outcomes that align with business goals.

Crucially, this shift will redefine the skills organizations prioritize in their workforce. Decision-making will become the centerpiece of professional development, with a focus on teaching individuals how to assess trade-offs, navigate ambiguity, and make high-stakes calls that influence the trajectory of the business. The ability to train AI systems to mirror these decision-making processes will also become a sought-after skill, as it directly impacts the performance and adaptability of these systems over time.

For businesses, this represents an opportunity to transform their approach to talent development. Training programs will evolve to emphasize not just technical skills but also cognitive and emotional intelligence. Employees will need to learn how to think critically under pressure, evaluate competing priorities, and make ethical decisions that account for both immediate needs and long-term objectives.

This transformation will also reshape organizational dynamics. Functional groups will need to collaborate more closely to ensure their AI systems are aligned with shared goals and priorities. Decision-making will become a more transparent and collective process, supported by AI tools that provide data-driven insights while still leaving room for human intuition and judgment.

Ultimately, this shift will redefine what it means to add value in the workplace. It’s no longer about how quickly or efficiently a task can be completed but about the quality of the decisions that guide those tasks. Organizations that embrace this shift will be better equipped to adapt to changing market conditions, harness the full potential of their workforce, and maintain a competitive edge in an increasingly automated world.

This is the future of work—a world where the focus moves away from rote tasks and towards decision-making, strategy, and human ingenuity. And it starts with rethinking how we train, evaluate, and empower the people who will lead this transformation.

/Mark