Right-sizing AI Governance
Not all AI applications carry the same risk profile, so a one-size-fits-all approach to governance is neither efficient nor sustainable. Both under- and over-governance introduce distinct enterprise risks, underscoring the need for a practical and proportionate model for governing AI.
This session introduces the CAI framework as a structured tool to calibrate oversight posture based on the operational Context, level of Autonomy, and business Impact of different AI application archetypes. Using concrete examples, we will explore how AI governance controls can be tiered and right‑sized to enable responsible scaling across the enterprise.
M&A As a Chokepoint for AI Governance
AI is emerging as a defining driver of technology mergers and acquisitions (M&A), reshaping how companies compete for capabilities and strategic advantage. Unlike traditional technology assets, AI systems evolve after deployment, carry opaque third-party dependencies, and can propagate hidden risks across enterprise boundaries. Conventional diligence was not designed for these realities.
This session examines why M&A is a critical chokepoint for AI governance, a high-leverage moment when AI capability, liability, and control transfer to the acquirer before they scale inside the new organization. Through practical actions and illustrative scenarios, we will discuss how embedding governance discipline across the transaction lifecycle enables leaders to anticipate and effectively manage the chokepoint while preserving durable value.
Strategic Approach to AI Transformation
In today’s rapidly evolving digital landscape, AI is no longer just a differentiator—it has become a key driver of operational excellence and business growth. Realizing the full potential of AI demands strategic orchestration across the global enterprise, extending well beyond tactical deployment of technology, tools and individual use cases.
This presentation offers a corporate strategy perspective for leaders guiding AI transformation across their organizations. The discussion will focus on two key topics: use case prioritization and cross-functional governance, drawing on illustrative examples from smart manufacturing.
AI Safety Playbook for M&A
AI will be a defining driver of tech M&A in 2026. But AI also introduces a new category of risks that traditional transaction frameworks weren't built to handle. Acquirers need diligence approaches that move beyond conventional financial and technology review to reflect the behavioral and evolving nature of AI systems. Overlooking AI safety assessments during the M&A due diligence process can potentially expose acquirers to regulatory investigations, hidden litigation liabilities, valuation impairment, or reputational damage that may emerge immediately or surface unexpectedly over time.
This document equips strategic buyers, corporate development teams, and decision makers with practical frameworks to treat AI safety as a value-protection mechanism, not a deal constraint. It outlines how to embed AI governance reviews across technical, legal, and business workstreams; calibrate diligence by asset intensity and sector criticality; deploy deal protections and remediation strategies; and signal post-close accountability at scale.
The central premise is clear: Incorporating AI safety governance into M&A standards is now table stakes for credible risk understanding and durable value creation.
Turning IP into growth capital
Innovation is more than a product, a patent, or a compelling founder story. It is a strategic asset that can help turn IP into growth capital and shape how a company finances its future.
I bring this perspective from both sides: as a holder of 50+ patents, a builder who has commercialized and scaled innovative products, and someone who has served on patent evaluation boards, alongside my current work at Techquity Growth Capital assessing IP-rich companies for growth financing.
I’ll share how we, as financing partners, assess an opportunity using not only traditional metrics such as recurring revenue and a strong sales pipeline that demonstrate a proven business model and market adoption, but also the strength and strategic value of its IP portfolio, including how its patents create unique and defensible competitive differentiation.
My goal is to help founders understand how to unlock the full potential of their IP and innovation to fuel growth and scale, connecting their technology, brand, and know-how into a cohesive financing strategy.
Attendees will leave with a sharper way to match capital needs with the right partner and a better understanding of how non-dilutive capital can preserve ownership.
Governing the AI R&D Engine
Research and Development (R&D) is a core engine of enterprise value, translating technical innovation and scientific discovery into differentiated opportunities and long-term competitive advantage. As AI fundamentally expands the scale of research and compresses development timelines, it also creates exposure to IP leakage, uncertainty and provenance risk for the organization. Proportionate governance, built in lockstep with the R&D workflow, can turn oversight into a performance driver, catching issues before they become late-stage constraints.
This session examines AI-enabled R&D through the governance lens across four boundaries: code and open source, inventions and IP, standards ecosystems, and joint-development partnerships. Through illustrative use cases, we will show how the CAI (Context, Autonomy and Impact) framework can be used to calibrate oversight as AI evolves from copilot to collaborator to autonomous agent. Participants will leave with a practical blueprint for protecting what R&D creates.