On the 64th episode of Enterprise AI Innovators, host Saam Motamedi (General Partner, Greylock Partners) talks with Briggs, Chief Technology Officer at Deloitte. Bill spent 28 years inside the firm and even created the CTO role to incubate emerging technologies and turn them into real systems for clients. He describes Deloitte’s scale plainly, a “30 billion plus” organization across “150 countries” with “half a million people,” and then quickly pivots to why that scale matters: if technology is now at the heart of business strategy, services firms and their clients have to codify knowledge into platforms, products, and agents that actually get work done.
Most enterprises are still trying to automate yesterday’s steps. Briggs thinks that is the wrong target. In his view, the point is not to recreate the human workflow faster; it is to redesign the outcome so the workflow changes shape entirely. “You want to have folded clean laundry when you need, not step-by-step replacement,” he says, using a simple analogy to describe a hard truth in corporate operations: if you keep the old process as the anchor, you cap the ceiling of what you can change.
Briggs’s starting point for any “AI native” ambition is not a tool, it is governance and intent. He would begin by sitting down with the CEO and the chair to ensure the mandate is real. “If this isn’t coming from you… we should stop the conversation,” he says, because it is too easy to reduce the goal to short-term cost takeout. He is blunt about the common trap: incremental efficiency gains paired with a commitment to SG&A savings might satisfy a near-term narrative, but “I’ve yet to find any client that can shrink themselves to success and growth.” The higher-value move is reimagining where decisions come from, what information is assembled, and which steps can disappear entirely when the system is designed around outcomes.
That mindset shift is why he encourages leaders to start close to the frontline, where the work is “actually getting done.” Briggs points to a trust gap between executives and the people living inside the process. In a Deloitte study he references, leaders in the “ivory tower” were “70 plus% bullish” about AI, while frontline confidence was “6.7%.” His point is not to litigate the exact numbers, it is to underline the operational implication: the people closest to the pain usually know which steps exist because of “institutional inertia,” not regulation or necessity. He gives healthcare as an example, where claims and pre-approval processes can be reframed into a bounded outcome (“pre-auth approvals faster, better for patients”) instead of a sprawling transformation slogan.
He also explains why the agent demos that delight leadership teams often fail to show up in production. Many organizations hoped generative AI would let them skip the “hard work” of data foundations, modernization, and service-level orchestration. But most large enterprises, he says, are not sitting on clean microservices backbones with well-instrumented capabilities ready for multi-agent orchestration. Their environments are “bubblegum together over the years,” and the guardrails required in enterprise control planes are fundamentally different than “you or I playing… in our homes.” The result is predictable: without modernized systems and clearer interfaces to core capabilities, the “bounds” of what agents can do safely stay narrow. The remedy is not a multi-year “modernize everything” program, but a surgical plan that focuses modernization where it unlocks real execution, and then uses AI itself to accelerate refactoring and sense-making across the data mass.
Finally, Briggs frames trust and security as engineering problems, not audit checklists. If security becomes “compliance-based… applied after the fact,” he warns, you are “close to dead in the water.” Instead, policies and guardrails have to be embedded into environments, pipelines, testing automation, and deployment flows, because that is where modern systems actually take shape. Across all of this, his advice is consistent: stop treating the moment like a spectator sport. “You have to roll up sleeve,” he says, because there is “no substitute leaning in.”
