How 80,000 companies build with AI: products as organisms, the death of org charts, and why agents will outnumber employees by 2026 | Asha Sharma (CVP of AI Platform at Microsoft)

Why Products That Think and Learn Are Changing Everything

In a wide-ranging conversation, Asha Sharma, Microsoft’s chief vice president of product for its AI platform, lays out a practical, large-scale view of how generative models and agents are transforming product design, organizational structure, and the skills companies need. Rather than shipping static features, product teams are now building systems that behave like living systems—continuous, self-improving, and tuned to measurable outcomes. That shift reframes intellectual property, roadmaps, and how companies capture value from user interactions.

Product As Organism: Metabolism Over Feature Sets

Asha describes the move from product as artifact to product as organism. The core idea: modern models can be tuned and continuously improved through feedback loops, creating products that evolve with each interaction. Rather than counting shipped features, teams should measure a product’s metabolism—the speed and fidelity with which it ingests signals, updates internal models, and produces better outcomes.

Post-Training, Not Just Pre-Training

As the upfront cost of training enormous foundation models rises, Asha argues the most effective economic play is post-training: fine-tuning and reinforcement learning on top of existing models using proprietary or synthetic data. For many teams, adapting an off-the-shelf model and investing in evaluation, labeling, and reward design will deliver better returns than trying to build a foundation model from scratch.

Agents, Org Design, And The Work Chart

Agents—autonomous software components that act on tasks—are already proliferating. Asha predicts an "agentic society" where tasks, not titles, drive throughput. With agents embedded across workflows, the org chart may shift from hierarchical reporting to task-based routing and monitoring, emphasizing observability, evals, and policy controls.

Code-Native Interfaces And The Rise Of Full-Stack Builders

The interview maps a familiar technological arc onto AI: GUIs give way to composable, code-first interfaces because streams of text and programmatic composition work better with large language models. That technical shift accelerates demand for polymath builders—product people who can spec, code, evaluate, and iterate quickly—so teams can run tighter end-to-end loops and ship continuously.

Operational Priorities For AI Platforms

• Invest in platform fundamentals: availability, data residency, privacy, and reliability to win enterprise trust.
• Design for the slope: choose interchangeable components and leave strategic slack to adapt rapidly.
• Build measurement systems: continuous evals, A/B testing, and fine-grained observability are the new table stakes.

Seasons, Roadmaps, And Leadership

Asha recommends planning by seasons—short spans defined by secular shifts such as the current "rise of agents"—combined with loose quarterly objectives and tight four-to-six-week squad goals. This hybrid cadence gives teams a North Star while leaving room for rapid technological changes. Leadership, she says, is about sustaining optimism and direction, a trait she credits to effective leaders who renew commitment and clarity across large organizations.

Across product, engineering, and design, the imperative is clear: prioritize the signal loop over siloed lanes, treat models as adaptable components, and institutionalize evaluation and observability. These changes will reshape how products are conceived, operated, and improved, replacing static roadmaps with living systems that learn from every interaction.

In summary, Asha Sharma's view compels product leaders to rethink investment toward post-training and agent tooling, embrace code-native composability, and train teams to operate as full-stack builders who optimize continuous feedback loops—foundations for durable AI-enabled products and organizations.