AI Technical Program Management: Scaling Foundational Investments From Research To Real-World Impact
Sandeep Jha is an award-winning AI expert and Principal Staff / Director TPM at LinkedIn, where he drives the company’s GenAI strategy.
AI is evolving at an unprecedented pace, yet most organizations struggle to turn research breakthroughs into scalable, production-ready systems. While AI-driven features can deliver quick wins, foundational AI investments such as generative AI, core AI infrastructure and enterprise-scale machine learning (ML) models demand years of sustained effort, disciplined execution and cross-functional collaboration.
Despite massive investments, only about half of AI projects make it beyond the prototype stage. Many companies prioritize short-term wins, creating chaos, fragmented efforts and endless pivots. This gap between AI innovation and tangible business impact is where the role of an effective AI technical program manager (TPM) can prove indispensable.
For TPMs looking to make this difference, this article provides firsthand advice for how you can ensure you’re effectively fostering a culture of long-term AI investment, managing uncertainty, mitigating risks, guiding teams to avoid common pitfalls and ultimately bridging the divide between research and real-world impact.
Overcoming Cultural Barriers
A major challenge with foundational AI initiatives is not the technology itself but the organizational mindset. Many leaders focus on short-term product gains rather than multi-year investments. When teams are measured solely by feature delivery speed, they rarely invest the time and resources required for transformative initiatives.
Based on my own experience, there are a few key ways I’ve found the TPM role can help given their unique position. First, secure leadership support. One way this can be done is by highlighting the long-term competitive advantages of stable and scalable AI, ensuring executive buy-in for multi-year initiatives.
Second, redefine success metrics to go beyond feature velocity. This might include incorporating milestones like reusable training pipelines or early-stage architectural validations for foundational AI.
Finally, foster a culture of experimentation that embraces rapid hypothesis testing, learning from failures and continuous iteration to propel innovation forward.
Embracing The Unknown For AI Breakthroughs
Transformative AI solutions often involve complex, open-ended challenges with no clear path to success. For AI TPMs, leading teams in these scenarios is crucial. A few key steps I’ve found helpful are to:
• Solve real problems. Anchor AI efforts in genuine business challenges and customer needs rather than following transient trends.
• Pilot in phases. Begin with small, proof-of-concept pilots to validate critical assumptions before scaling.
• Embrace iteration. Design flexible project structures that evolve with new insights.
• Think long term. Advocate for systematic, breakthrough innovation over incremental improvements.
Building A Robust AI Ecosystem
A common pitfall in AI programs is reinventing the wheel for every new use case, leading to fragmented infrastructure and costly technical debt. AI TPMs can avert these issues by championing a unified platform strategy and making informed decisions about when to build solutions in-house or leverage external offerings.
Some best practices for a scalable AI ecosystem include:
• Standardize core AI components. Centralize data pipelines, training frameworks, evaluation systems and responsible AI practices, enabling teams to focus on innovation rather than infrastructure maintenance.
• Leverage proven platforms. Evaluate and adopt open-source and commercial platforms that accelerate time-to-market and mitigate vendor lock-in risks.
• Balance custom and external solutions. Use a strategic build-versus-buy approach to ensure investments are scalable and aligned with the organization’s unique business needs.
De-Risking With Strategic Alignment
Long-term AI initiatives are inherently risky when teams operate in silos. In today’s fast-paced AI landscape, effective collaboration is essential to mitigate risks and drive long-term success. For AI TPMs to drive success, there are three elements I’ve found especially helpful:
1. Establishing “Pivot Or Persevere” Criteria: Define performance thresholds and timelines to decide whether to continue, adjust or discontinue projects.
2. Assigning Directly Responsible Individuals (DRIs): Ensure accountability across research, engineering, product and TPM teams with clear decision-making frameworks.
3. Shifting Resource Allocation: Move from short-term quarterly planning to long-term commitments aligned with multi-year strategic goals.
Optimizing Meeting Cadence
Many AI teams struggle with meeting overload, fragmented communication and inefficient workflows, leading to misalignment and slow execution. A lean operating model ensures meetings are purposeful while minimizing unnecessary overhead. As an AI TPM, you can drive this optimization through:
• Bi-Weekly DRI Syncs: Short, focused meetings to review progress, resolve blockers and align on strategic priorities.
• Customer Feedback Panels: Regular, structured sessions to gather input and refine AI solutions based on real user needs.
• Weekly Demo Reviews: Replace time-consuming executive reviews with lightweight, demo-driven check-ins that promote continuous iteration and transparency.
• Monthly Knowledge Exchanges: Encourage cross-team learning and best practice sharing in an open, collaborative format.
• Async Collaboration: Use tools like Slack and shared docs to reduce meeting load while keeping communication clear and decisions well-documented.
Conclusion
Foundational AI investments demand more than technological breakthroughs—they require disciplined, long-term execution. AI TPMs can be the catalyst in this transformation, standardizing infrastructure, managing risks and fostering a culture of continuous improvement and collaboration.
By championing strategic decision making and scalable execution models, AI TPMs enable organizations to bridge the gap between research and real-world impact. The future of AI is not just about breakthroughs, but about the execution, adaptability and leadership that transform potential into lasting impact.
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