Making technology faster does not automatically make the business better.
John Marcante spent 28 years at Vanguard, including serving as Global CIO and leading its high-net-worth and advice business. As Deloitte’s U.S. CIO-in-Residence, he works with senior technology leaders navigating change.
In our conversation, John and I explore why AI investment needs to change how work gets done. John shares what happened when Vanguard’s cloud journey made IT faster than the business could absorb, and how shifting to cross-functional teams and shared outcomes helped address that gap. We also discuss human judgment, the anxiety people feel when familiar tasks are automated, and how leaders can direct freed-up time toward work that matters.
This is the first podcast in The Adaptive Enterprise, our five-part article and podcast series on redesigning work, governance, and leadership for the age of AI.
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Key Takeaways
- Redesign work around new capabilities: Speeding up one step in a process can leave the overall business unchanged. Leaders need to bring people together to rethink the work from end to end.
- Align teams around a business outcome: At Vanguard, engineers and business colleagues needed a shared definition of success. Measuring each function separately made it harder to improve the result for clients.
- Decide how recovered capacity will be used: Time saved through automation creates an opportunity. Leaders are responsible for helping people use that time where judgment and expertise can improve outcomes.
- Help people redefine their value: As portfolio activities became automated, Vanguard advisors could spend more time helping families navigate important life decisions. John describes how client loyalty became the goal guiding that shift.
- Build a continuous cycle of adaptation: The Adaptive Enterprise Loop connects emerging capabilities, work redesign, outcome measurement and governance, and learning. Organizations need to repeat this cycle as new possibilities emerge.
Additional Insights
- Technology foundations still matter: John cautions that organized data, modern platforms, and addressing technical debt remain important. Work redesign does not remove the need for those foundations.
- Technical capability and organizational influence are different: John describes a gap between technology leaders’ confidence in scaling AI and their confidence in changing how the business operates.
- Leaders need to model the change themselves: Barry shares how Progyny CEO Pete Anevski experimented with new ways of working and openly discussed what he was learning, helping others feel able to experiment.
- Humility helps reduce learning anxiety: John emphasizes honest communication and vulnerability when people are uncertain about their roles. Leaders do not need every answer before showing how they are learning.
- Stay close to the people doing the work: John moved his office onto the advisor floor to listen to client conversations, understand concerns, and help connect the change to what mattered for clients.
Episode Highlights
00:00 – Boards Are Asking Where the AI Value Is
John opens with the gap between AI adoption and business value, and the questions boards are asking about their investment.
01:41 – Introducing The Adaptive Enterprise Series
Barry introduces John and explains how their conversations grew into five articles and five companion podcasts.
02:54 – What Should Change Because of AI?
Barry frames the leadership challenge around what new capabilities make possible for work.
05:30 – When IT Gets Faster Than the Business
John recounts Vanguard’s cloud journey, the bottleneck that shifted into the business, and the move to cross-functional product teams.
07:50 – Business and Technology Need to Move Together
Barry shares a lesson from American Airlines about involving the wider business in transformation.
09:45 – Scaling AI Without Changing the Operating Model
John discusses why technical readiness does not necessarily give leaders the influence to change established ways of working.
11:07 – Redesign the Whole Process
Barry explains how local productivity gains can leave end-to-end work unchanged, while John describes aligning teams around shared outcomes.
15:21 – When Automation Challenges People’s Roles
Barry explores the anxiety people feel when tasks that have defined their work begin to change.
17:13 – What Will You Do With the Time You Free Up?
John explains why efficiency alone is insufficient and why leaders need to direct capacity toward valuable work.
19:01 – Leaders Go First
Barry shares how Progyny’s CEO modeled experimentation, and John discusses humility and vulnerability in leadership.
22:46 – Helping Advisors Redefine Their Value
John describes moving the focus from portfolio mechanics and larger client books to client loyalty and important family decisions.
27:26 – The Adaptive Enterprise Loop
John explains the continuous cycle of capabilities, work redesign, outcomes, governance, and learning.
30:50 – Where Executive Teams Can Start
John offers practical starting points: rethink work, identify where judgment creates value, and align people around business outcomes.
FAQs
Q1: Why can AI adoption fail to improve business performance?
Organizations can speed up individual tasks while leaving decisions, handoffs, and the wider process unchanged. Barry and John explain why leaders need to redesign work and measure the business outcome rather than rely on usage as evidence of value.
Q2: What did John Marcante learn from Vanguard’s cloud transformation?
John describes how IT became faster, but the business could not absorb the changes at the same pace. The response involved cross-functional product teams, common outcomes, and faster learning tied to client value.
Q3: Why does human judgment matter as AI becomes more capable?
AI can help generate information and automate activities, but people still need to decide what matters and where to direct attention. John emphasizes using recovered capacity for decisions and relationships that improve outcomes.
Q4: How can leaders help people when automation changes their work?
Leaders can explain the intended outcome, listen to concerns, and model their own learning. John’s advisor example shows how focusing on client loyalty helped people understand where their expertise could create value as familiar activities were automated.
Q5: What is the Adaptive Enterprise Loop?
It is a repeating cycle of identifying new capabilities, redesigning work, measuring and governing outcomes, and capturing learning. John and Barry describe it as an alternative to treating transformation as a program that ends once technology is deployed.
