Don’t make your workforce wait for AI

An open letter to CIOs.

Recently, I was in what was supposed to be a 30-minute kickoff meeting for a company-wide Microsoft Copilot rollout.

The pilot had been a success. One hundred employees participated. Ninety-four percent were actively using it. More than half were using it multiple times per day. We saw measurable time savings, reduced meeting fatigue, clearer communication, and a visible shift in how people approached their work. Several described it as “my little assistant.” Others said it reduced decision fatigue and increased their confidence in high-stakes communication.

Then the CIO joined.

The meeting stretched to 50 minutes. For 40 of those minutes, he pontificated about his strategy.

He had just returned from a Gartner conference energized by what he had learned. His focus was clear. The real opportunity was not broad personal AI enablement. It was building targeted AI agents deeply connected to enterprise systems such as SAP, DataBricks, and Azure. His team was building a Gemini interface connected to a DataBricks core data architecture. That was his vision of the future.

He questioned whether broad personal AI assistants generated enough ROI to justify investment. He shared a frustrating experience with Copilot. He had crafted a thoughtful prompt and the output did not meet his expectations. He also believed a broad adoption campaign was unnecessary. Employees were already excited. They would figure it out.

Here is the hard part for me: I agree with almost all of that strategy.

Building powerful, data-connected agents is smart. Preparing infrastructure is responsible. Rethinking workflows is necessary. If you can redesign how work happens at a systems level, you should.

But while that vision matures, no one can say how long that will take. And in the meantime, most of the workforce is waiting.

In 2026, asking your workforce to wait for AI is a mistake.

The case for the parallel path

If you are a CIO, you are likely thinking a lot about architecture, governance, risk, identity, compliance, and scale. That work matters.

But something else is happening at the same time. Your employees are already living in an AI-augmented world. They use ChatGPT to draft ideas. They experiment with Gemini to summarize complex information. They refine writing in Claude before sending important messages. They are integrating AI into how they think because the tools are available and they need them to stay competitive.

Employees are consumers too. And consumers are evolving quickly. Their expectations around speed, clarity, synthesis, and decision support have changed. Their cognitive rhythm — the pace at which they process information and generate output — has accelerated.

When that capability exists outside the enterprise but not inside it, work begins to feel artificially constrained. Not because infrastructure is weak. Not because leadership lacks vision. But because the employee’s lived experience has already moved forward.

If you focus exclusively on system-level AI while delaying personal AI enablement, you create asymmetry. Enterprise intelligence advances. Human capability stalls.

The strongest strategy is not sequential. It is parallel.

If you can and want to build the targeted agents. Then you should. But at the same time, enable your people with governed AI assistants that elevate how they think, write, prepare, and perform. Not because it is trendy. Because it aligns your organization with where your workforce already is.

When employees are empowered at the level of their AI cognizance, they work smarter, faster, and more confidently. That energy accelerates transformation.

ROI is both economic and strategic

This conversation is often framed as philosophical. While it is philosophical, it is also economic.

Independent research consistently shows measurable productivity and quality gains from generative AI:

  • A joint MIT and Stanford study found generative AI increased productivity by 14 percent among knowledge workers (Brynjolfsson et al., 2023).

  • Boston Consulting Group found consultants completed tasks 25 percent faster with 40 percent higher quality using GPT-4 (BCG, 2023).

  • GitHub reported developers completed tasks up to 55 percent faster using Copilot (GitHub Research, 2022).

  • McKinsey estimates generative AI could add $2.6 to $4.4 trillion in annual global productivity value (McKinsey Global Institute, 2023).

Across research, the signal is clear. AI improves speed and quality in knowledge work.

Translate that into enterprise economics.

A knowledge worker’s fully loaded cost often ranges from $120,000 to $180,000 annually. That equates to approximately $60 to $90 per productive hour.

If a personal AI assistant saves even 30 minutes per day across drafting, summarizing, research, and cognitive switching, that equals roughly 125 hours per year.

At $75 per hour, that represents approximately $9,375 per employee in reallocated investment capacity against a roughly $360 annual license.

The ROI is not speculative. It is defensible.

The three layers of impact

The financial return is only the first layer. The deeper impact unfolds across three reinforcing dimensions.

Time savings leading to reallocated investment
Drafting happens faster. Summaries replace full-document reading. Preparation is compressed. Time is reinvested into higher-value work.

Optimization and accuracy leading to operational excellence
AI improves clarity and consistency. It reduces revision cycles and communication errors. Small quality gains compound across thousands of daily decisions.

Continuous improvement leading to performance compounding
As AI becomes embedded in workflows, engagement generates insight. Insight drives iteration. Iteration drives improvement. Over time, expertise strengthens and performance accelerates.

There is also a cost to waiting.

Even a conservative 10 percent productivity gain translates into material financial impact. Across 1,000 knowledge workers at $140,000 fully loaded cost, that represents $14 million in annual latent value.

Waiting does not eliminate cost. It accumulates opportunity loss.

If AI is not enabled internally, it does not disappear. It shifts beyond the firewall, creating security ambiguity and fragmented AI norms. That is not risk reduction. It is risk diffusion.

What AI personal assistants actually do well

Personal AI assistants are not magic. They are cognitive accelerators embedded into daily work.

  • They understand context and adapt to individual patterns.

  • They break down goals into structured outputs.

  • They reduce cognitive friction in drafting and synthesis.

  • They provide a unified intelligent interface across tools.

They do not replace enterprise systems, they strengthen the humans who use them.

AI adoption is organizational change

Enabling a personal AI assistant is not a tool rollout. It can be the entry point into enterprise AI transformation.

Research consistently shows that transformation success is driven more by behavioral and cultural alignment than by technology alone. Prosci reports that initiatives with effective change management are six times more likely to meet objectives (Prosci, 2021). McKinsey similarly finds that successful transformations prioritize people and culture alongside systems (McKinsey, 2018).

AI adoption rests on four tenets:

  1. Spark a cultural shift through leadership vision.

  2. Build communities and shared learning.

  3. Provide supportive training and guardrails.

  4. Measure adoption and sentiment, not just usage.

Launching a personal AI assistant broadly makes AI transformation shared. It allows the organization to move into an AI-infused world together and that is cultural evolution as much as technical transformation.

Not if, but when

If you can build powerful point agents, then you should. Preparing Azure. Integrating SAP. Structuring enterprise data. That work matters. But so does enabling your people.

Personal AI assistants are a company’s most cohesive entry point into AI transformation. They build fluency and trust at the human layer while the technical layer matures.

AI is not simply a technology decision, it’s a leadership decision. So don’t ask your workforce to wait, flip the AI assistant switch ASAP.

Regards,

Jarom.

Glossary of terms

AI cognizance
The level of awareness, capability, and confidence an individual has in working alongside AI systems.

Cognitive rhythm
The pace at which an individual processes information and generates output.

Performance compounding
The accelerating effect that occurs when AI capability, workflow iteration, and skill growth reinforce each other over time.

Parallel path strategy
Simultaneously building system-level AI agents while enabling broad personal AI assistants.

Reallocated investment
Time saved through AI assistance redirected toward higher-value work.

Cognitive friction
Micro-barriers in knowledge work that slow productivity.

Risk diffusion
Security and governance exposure caused by unmanaged external AI usage.

Cultural activation
Leadership-driven alignment that embeds AI into organizational identity.

Adoption depth
The degree to which AI becomes embedded in workflows.

Latent ROI
Unrealized performance gains caused by delayed adoption.

References

Brynjolfsson, E., Li, D., & Raymond, L. (2023). Generative AI at Work. MIT and Stanford.
https://www.nber.org/papers/w31161

Boston Consulting Group (2023). How People Can Create and Destroy Value with Generative AI.
https://www.bcg.com/publications/2023/how-people-can-create-and-destroy-value-with-genai

GitHub Research (2022). The Economic Impact of GitHub Copilot.
https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-developer-productivity/

McKinsey Global Institute (2023). The Economic Potential of Generative AI.
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-economic-potential-of-generative-ai

Prosci (2021). Best Practices in Change Management.
https://www.prosci.com/resources/articles/change-management-statistics

McKinsey (2018). Unlocking Success in Digital Transformations.
https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/unlocking-success-in-digital-transformations