Editorial
Research Report
The ROI of Employee Experience
Organizations today face a choice: remain AI-enabled, bolting on yet another chatbot or analytics dashboard, or become truly AI native, embedding intelligence into every workflow, decision and role. 2025 taught us that although AI adoption is widespread, impact and value remain limited.
McKinsey’s research shows that workflow redesign is the single strongest predictor of scaled value. AI-enabled organizations simply layer technology on old processes, while AI-native ones reimagine work around humans and intelligence.
As Jennifer George, chief creative officer of the Rube Goldberg Institute reminds us: “All you need is a pile of junk and a great imagination, and you can literally build an award-winning machine. It is the great equalizer.”
Her words capture the essence of creativity as a mindset, turning constraints into possibilities and unexpected connections.
In organizations, small, intentional design choices in workflows, defaults and interactions can, like a Rube Goldberg device, set off cascading effects that shape how people experience work and how value is created. With that spirit, AI-native organizations configure operating models where empathy, collaboration and interpretive skills drive impact and unlock value.
Experience Intelligence, the third and final pillar of digital fluency, focuses on designing and optimizing human-centered experiences that convert data and AI use into differentiated value. Creativity is how organizations differentiate at both the human and business levels. When practiced intentionally, it shapes experiences that serve employees and elevate business results.
Designing With Intelligence
Experience intelligence activates when organizations move beyond efficiency and embed AI into the flow of work to amplify human judgment, creativity and collaboration. True intelligent design asks: Does the workflow build confidence? Does it support collaboration across silos? Does it turn raw data into decisions?
Adoption and confidence in an AI-native organization is built early by striking a balance between intelligent defaults and human override.
Intelligent defaults, such as automated summaries, predictive alerts and contextual recommendations, reduce cognitive load and simplify decisions. Human override ensures judgment, ethics and creativity remain in control. This balance transforms AI use into meaningful and efficient interactions without eroding trust.
Consider these practical scenarios:
- Judgement: An employee postpones an AI suggested mindfulness break to match their schedule.
- Creativity: A designer reworks AI generated ad layouts to infuse brand personality.
- Ethics: A recruiter adjusts an AI generated shortlist to advance inclusion goals.
- Collaboration: A project manager modifies automated task assignments to balance workloads.
AI-native organizations not only change technology, they change roles and how people work, think, collaborate and grow. The question shifts from “How do I use this tool?” to “How do I design meaningful outcomes with this intelligence?” Employees learn continuously in the flow of work, through experimentation and adaptation.
Optimizing the End-to-End Experience
A combination of intelligent design and continuous optimization will guide behavioral change and help sustain impact. Optimizing experience means improving the end-to-end moments where people interact with work (tools, data, processes and each other) to meet human needs while achieving efficiency and business outcomes.
AI-native organizations create value through the quality of experiences that tools create. That quality shows up in real workflows and is measured by employees’ trust in insights (confidence), their ability to see the next best step (clarity), and their capacity to work fluidly across teams (connection).
Imagine an enterprise legal team reviewing contracts. AI surfaces risk clauses and suggests standard language as an intelligent default. Attorneys apply human override to account for jurisdictional nuances or client-specific terms. The result is faster turnaround, reduced compliance risk and improved collaboration across legal, procurement and compliance teams.
- Confidence: AI highlights risk clauses and provides rationale with links to regulatory sources and surfaces emerging regulations or pending legislation likely to impact the clause in the next six–12 months, so attorneys trust why a clause is flagged and can anticipate changes.
- Clarity: Intelligent defaults suggest standard language for common clauses and surface the next best action (e.g., “Replace with approved template” or “Escalate for compliance review”) and benchmark the proposal against industry practice or competitor norms, providing strategic context for negotiation.
- Connection: The system auto-notifies compliance and procurement teams when high-risk terms are detected, enabling real-time collaboration on mitigation steps and can automatically open a cross-functional chat or schedule a short decision huddle with the right stakeholders, reducing delays and miscommunication.
To measure experience quality, not just efficiency, track regulatory readiness rate; false positive flag rates (confidence); time to clarity; first pass acceptance of AI suggestions (clarity); and escalation resolution time; cross-functional participation rates (connection).
Sustaining Experience With a Productivity Coach
Intelligent design and optimization alone won’t sustain impact, that requires continuous adaptation and human creativity. Work is transformed by people, not tools.
This is where a Productivity Coach helps. Organizations, including Microsoft, through its MCAPS coaching ecosystem, are turning to AI-enabled human productivity coaches to guide behavioral change, reinforce technology adoption and ensure transformation is sustainable and realized across all levels of the enterprise. Productivity coaches bridge the gap between intelligent tools and meaningful moments in the end-to-end workflow. Coaches are responsible for:
- Embedding intelligent defaults and human override into daily work at all levels
- Building digital fluency and interpretive skills so employees trust and act on AI insights
- Optimizing experiences for confidence, clarity, and connection
This series began by asking why digital fluency matters, moved to how it drives business advantage, and now ends with activation: embedding intelligence into human-centered, adaptive workflows.
Becoming AI-native is ultimately a leadership choice. It requires the courage to redesign workflows, invest in digital fluency and empower people with interpretive skills and productivity coaches. The future of work is intelligently human. Lead the change.
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Editor's Note: Catch up on more thoughts on building a human-centered, AI native workplace:
- People-Centric Leadership and AI: Can We Have Both? — AI can support people-first leadership, if we use it with care.
- Humans& Bets $480M That AI Can Be Human-Centric — Humans& debuted at unicorn status with no product. Its goal is human-centric AI that resists enterprise's default to efficiency.
- The Return of Human-Centered Design — Human-centered design has been around for over 50 years, but with companies now trying to get the most of their AI investments, HCD is more relevant than ever.
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