Answer extracted from the Unprompted: Real AI in IC podcast — listen to the full episode below.
Why does productivity with AI initially decrease before increasing?
Productivity with AI initially decreases because teams must invest significant time in learning, experimentation, and training rather than immediately reaping rewards. As Adele McIntosh explains, there is no shortcut to unlocking AI's deeper capabilities—organizations that stick to surface-level uses like email writing miss the transformative potential entirely.
The Learning Investment That Changes the Game
The Economist piece that Adele references illustrates a counterintuitive truth: AI adoption requires a deliberate productivity dip upfront. Teams spend time experimenting with new tools, reading documentation, running tests, and discovering use cases beyond obvious automation. This is not wasted time—it's the investment period that determines long-term returns.
Adele has witnessed this pattern directly at Arm, where ChatGPT Enterprise was rolled out in early 2025 to approximately 10,000 employees. She emphasizes that organizations must dedicate genuine time to learning, moving beyond quick wins like automating routine emails to understanding how AI can reshape core workflows, decision-making, and creative work.
The stakes are real: according to Microsoft's 2026 Work Trend Index, 58% of AI users now produce work they could not have a year ago. That shift only happens when communicators and teams accept the learning curve as non-negotiable, as Adele discusses in this episode.
"You have to put time aside and put time in to learn and experiment and read and do the training and experiment again and that does take time there's no kind of shortcut to it really."
Adele McIntosh — VP of Internal Communications and Community at Arm. At Arm for over eight years, McIntosh leads internal communications and other functions within a major technology company of approximately 10,000 employees operating in the semiconductor space. Arm designs and builds chips for applications ranging from smartwatches and phones to data centers and robots.
Planning for the Productivity Curve as a Communicator
For internal communications teams, the implication is clear: plan for an initial productivity dip when introducing AI tools. This is not a sign of failure—it's a sign of real adoption happening. Teams that rush through onboarding or expect instant payoff will underestimate the time needed to build competence.
The path forward, detailed further in this episode of Unprompted: Real AI in IC, involves messaging this investment cycle to leadership and teams alike. Communicators should set realistic expectations: productivity falls in month one or two while learning happens, then climbs as people move beyond surface-level experimentation to high-value applications in content creation, data synthesis, and strategic thinking.
One additional insight worth hearing from the full conversation: Adele explores how organizations can position AI adoption narratives to address employee concerns about job displacement and build trust in the transition—a consideration that runs parallel to managing the productivity dip itself and shapes how teams receive training and experimentation time.
Key takeaways
- Productivity with AI initially decreases because teams must invest time in learning and experimentation beyond surface-level uses.
- There is no shortcut to unlocking AI's deeper capabilities—organizations that rush through adoption risk missing transformative potential.
- 58% of AI users now produce work they could not have produced a year ago, but only after accepting the learning curve.
- Internal communicators should plan for and explicitly message the productivity dip to leadership as a normal, necessary phase of adoption.
See also
What new skill sets are required for internal communications teams managing AI tools and GPT agents?
Adele identifies the need for skills to understand AI tools, keep communication GPTs updated, and manage their performance like any team member, ensuring AI outputs remain aligned with organizational values and accurate.
What is the future role of intranets and trusted information sources as AI agents become the primary interface for employee communications?
Adele believes intranets will not disappear in the short term because organizations need a single trusted source of truth from which AI pulls information, making data governance a critical foundation for AI-driven communication.
How should organizations frame internal narratives around AI adoption when addressing concerns about job displacement and ethical issues?
Adele's approach at Arm involves developing messaging around philosophy, ambition, and the importance of human judgment, then cascading the CEO's vision to ensure employees understand AI as a tool that amplifies their capabilities.