Designing for Adoption: Lessons from a Digital Workplace Transformation

Digital transformation initiatives often start with the right intentions but stumble during execution. Tools get deployed, structures get created, and yet teams continue to struggle with the same problems: files are hard to find, information lives in silos, and users quietly revert to old habits.

This article reflects on a recent digital workplace transformation I supported, focused on improving how teams discover information, collaborate, and adopt shared standards—without relying on heavy centralization or rigid governance. While the work involved Microsoft 365, SharePoint, automation, and AI, the most important lessons had little to do with tools.

The Real Problem Wasn’t Technology

At the outset, the organization faced challenges that will sound familiar to many enterprise environments:

• Important files were scattered across team sites and personal storage

• Calendar visibility varied by team, making coordination harder than it needed to be

• Each site looked and behaved differently, increasing cognitive load for users

• Legacy content accumulated without clear ownership or structure

None of these issues were caused by a lack of platforms. The organization already had Microsoft 365 in place. The real problem was how information was structured, surfaced, and experienced by users .

This distinction mattered, because it shaped how we approached the work.

Design Principles That Guided the Work

Rather than starting with configuration checklists, we aligned early on a small set of design principles to guide decisions throughout the engagement:

• Meet teams where they are

• Visibility without forced centralization

• Standardization without rigidity

• Self-service over IT dependency

• Scalable by design

These principles became guardrails. Any solution that violated them—no matter how elegant—was reconsidered.

Reframing SharePoint as a System, Not a Collection of Sites

One of the early shifts was reframing SharePoint from “a place where teams store documents” to a system of record for how information flows across the organization.

This led to several key decisions:

• Establishing a hub-and-spoke model so teams retained autonomy while gaining shared visibility

• Designing a consistent information architecture (navigation, naming, metadata) across sites

• Treating the hub as an entry point, not a content owner

The goal was not centralization for its own sake, but predictable discoverability—a user experience where people could reasonably guess where something lived, even if they had never seen that site before.

Adoption Comes from Reducing Friction, Not Adding Rules

A recurring failure mode in digital transformations is assuming that documentation and training alone will drive adoption. In practice, people adopt systems when those systems save them time.

Several choices reinforced this:

• Cross-site document search surfaced relevant content without requiring users to know where it lived

• Calendar standardization improved visibility without forcing teams into a single calendar

• A consistent look and feel reduced the mental tax of switching contexts

Instead of asking users to “learn the new system,” the system was designed to work the way users already think.

Using Automation to Eliminate Hand-offs, Not Add Complexity

Automation played an important but quiet role.

Rather than automating for novelty, the focus was on information events—moments when updates needed to propagate across teams:

• A calendar update should appear where people already look

• A shared document should surface consistently across relevant sites

• Repeated manual steps should disappear entirely

By treating information changes as events, not tasks, automation helped reduce coordination overhead without introducing brittle workflows.

AI as an Adoption Accelerator, Not a Feature

An AI-based assistant was introduced later in the engagement, built on Microsoft Copilot capabilities. Importantly, it was not positioned as a replacement for navigation or structure.

Instead, it served as:

• A low-friction entry point for users unsure where to start

• A bridge between structured content and natural language questions

• A complement to documented standards and help content

The key insight was that AI works best when paired with clear underlying information architecture. Without that foundation, AI simply amplifies inconsistency.

Measuring Success Beyond Go-Live

Rather than declaring success at launch, readiness was assessed against outcomes that mattered:

• Improved findability across team sites

• Consistent structure and navigation patterns

• Reduced dependency on ad-hoc guidance

• A foundation that could scale without rework

A formal rollout readiness review helped ensure the solution was viable not just for one team, but as a primary entry point for broader adoption .

What I’d Carry Forward to Any Transformation

A few lessons from this work continue to hold across domains:

1. Adoption is a design problem, not a training problem

2. Standardization should create freedom, not friction

3. AI amplifies structure—it doesn’t replace it

4. Automation should remove decisions, not add steps

5. Good systems respect how people actually work

These ideas apply whether you’re building internal platforms, customer-facing products, or operational workflows.

Closing Thought

Digital transformation is often framed as a tooling exercise. In reality, it’s a systems design challenge—one that sits at the intersection of product thinking, engineering constraints, and human behavior.

When those elements align, tools fade into the background, and adoption follows naturally.