The AI Report

In this episode, Ian Kahn, PwC partner and global leader for its Customer and Commercial Excellence platform, joins Liam to discuss what AI adoption actually looks like inside large organizations. Ian has spent 27 years at PwC and leads a global team of almost 10,000 practitioners focused on helping clients build stronger customer relationships and drive growth.

Ian explains why the biggest challenge with AI is not access to the technology, but getting people and business models to change with it. He shares why PwC sees a small group of companies capturing most of the value from AI, how leaders can reduce resistance to change, and why the number of agents deployed matters less than how the business itself evolves.

They also get into how Ian uses his own portfolio of AI agents, the shift toward voice-based interaction, and how agents can take over routine administrative work so teams can spend more time on higher-value customer relationships.

Key Topics Covered

Why Ian calls AI the biggest change of his 27-year career

Why 20% of companies are capturing 75% of the value from AI deployment

How leaders can overcome resistance to AI adoption

How PwC is embedding AI into its business model

How Ian uses his own portfolio of AI agents

Why AI agents should free people for higher-value work

The role of voice in the future of work

Why leaders need to explain the "why" behind AI transformation

Episode Timestamps

00:00 - Introduction and Ian's 27 years at PwC

02:15 - Why AI is the biggest change Ian has seen in his career

02:56 - Ian's role leading almost 10,000 practitioners at PwC

04:44 - Why continuous learning matters in a 175-year-old firm

06:08 - Why 20% of companies are capturing 75% of AI's value

07:57 - The role of culture and leadership in AI adoption

10:08 - Why Ian spends most of his time with clients and teams

11:06 - How PwC is embedding AI into its business model

12:55 - Using agents to free up higher-value capacity

14:17 - How Ian uses his own portfolio of AI agents

16:29 - Why leaders need to explain the "why" behind AI

18:28 - Career advice, mentorship and leading high-performing teams

Where to find Ian:

LinkedIn - https://www.linkedin.com/in/ianwkahn/

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In this episode, Mike Flynn joins Liam to break down how companies are moving from AI experimentation to measurable business value. Mike has spent more than 20 years in consulting, including nearly a decade at EY, nine years at PwC, and now leads the technology sector for EY's consulting business.

They discuss why businesses need to redesign workflows around AI instead of simply adding AI to existing processes, how EY's "Design for Zero" approach works, and why AI agents are changing the economics of software.

Mike also explains the real costs behind AI agents, what enterprises want from AI vendors, how AI is reshaping consulting, and why EY is investing in forward-deployed engineering.

Key Topics Covered

Moving from AI experiments to measurable ROI

EY's "Design for Zero" approach to AI-first workflows

Why per-seat software budgets break with AI agents

The real cost of an AI agent task

What enterprises want from AI vendors

How AI is changing consulting and forward-deployed engineering

Episode Timestamps

00:00 - Introduction and Mike's path from the Air Force to EY and PwC

01:58 - What Mike's role at EY looks like today

03:57 - Why point solutions create "trapped work"

04:29 - From AI experiments to end-to-end transformation

07:47 - Where AI-first redesign is working today

10:00 - Why AI is breaking the per-seat software model

13:01 - The real costs behind running an AI agent

16:00 - Measuring AI spend versus business value

18:28 - How much work will AI actually take over?

20:52 - Why companies need more granular AI cost controls

25:53 - Building automated control loops for AI

31:21 - Experimenting with enterprise AI tools at scale

36:02 - Why enterprise AI adoption moves slower than personal AI

39:37 - CIOs shift from adoption to process redesign

41:29 - What enterprises want from AI vendors

45:24 - Consulting versus AI deployment at EY

46:36 - Why AI may create more consulting work, not less

49:00 - EY's forward-deployed engineering strategy

52:16 - The two qualities Mike looks for when hiring

55:33 - Why Mike does what he does

Where to find Mike:

LinkedIn - https://www.linkedin.com/in/michaelbernardflynn/

EY - https://www.ey.com/

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In this episode, Cillian Kieran, CEO and founder of Ethyca, joins Liam to talk about why data privacy and AI governance need to be treated as engineering problems, not just legal and compliance challenges.

Cillian explains how a consulting project for Heineken ahead of the GDPR pushed him to rethink privacy from an engineer's perspective and eventually build Ethyca. He breaks down how the company's product suite works, why Fides has become a widely adopted open-source privacy standard, and what changes when AI agents are given read and write access to tools like Stripe, QuickBooks or a CRM.

They also get into what foundation model providers may be missing on governance, why AI needs a harness that directs its capabilities without slowing it down, the responsibility engineers have when building AI systems, and why he thinks much of what we call AI is still statistical math wrapped in a marketing label.

Later, Cillian and Liam discuss where AI startups may consolidate, Yann LeCun's work on world models, the human impact of increasingly agent-driven work, and how growing up around art shaped Cillian's view of software as a creative pursuit.

Key Topics Covered

Why privacy and AI governance are engineering problems

How Fides became a widely adopted open-source privacy standard

The risk of giving AI agents read and write access to business systems

Why AI needs a harness that directs its capabilities without slowing it down

What foundation model providers may be missing on governance

Why engineers, not just users, carry responsibility for AI safety

Why Cillian calls AI "statistical math" with a marketing label

World models, startup consolidation and the future of AI infrastructure

Episode Timestamps
00:00 - Introduction and welcome
00:58 - From a physics dropout to a data consultancy for Fortune 500 brands
03:17 - The Six Problems of Privacy and becoming the world's most trusted technology company
07:20 - Inside the product suite: Fides, Helios, Janus, Lethe and Astralis
11:29 - How Fides became a widely adopted open-source privacy standard
14:20 - What Cillian believes foundation model providers are missing on governance
16:08 - Why AI needs a harness, not just guardrails
18:19 - Ethyca's business model and the rise in demand for consulting
23:07 - Why Ethyca went after enterprise customers first
24:50 - Testing Grok's agent tools
27:34 - MCP proliferation, OpenClaw and the risk of "permissive access"
31:11 - The seatbelt analogy for building safe AI systems
34:28 - Why AI governance isn't getting the coverage it deserves
36:26 - AI as "statistical math" and Ted Chiang's take on the label
38:43 - Foundation model economics and the future of AI startups
39:49 - Yann LeCun, world models and where Cillian would place his next bet
50:12 - Zen and the Art of Motorcycle Maintenance: classical versus romantic thinking
56:05 - Why Cillian still does what he does
01:03:34 - Where to find Cillian and closing thoughts

Where to find Cillian:
LinkedIn - https://www.linkedin.com/in/cilliankieran
Ethyca - https://ethyca.com

Partner Links
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Brad Axen, Block's Head of AI Capabilities and the original author of Goose, joins Liam to talk about what actually makes AI useful at work. They cover how Block went from building an early open-source AI agent to MoneyBot, ManagerBot, BuilderBot and Buzz, and why Brad thinks the hardest problems now are memory, access and interface, not just model intelligence.

Brad also explains why AI memory should belong to the business rather than a single bot, what ants can teach us about shared memory systems, why "meat proxy" is becoming a new office problem, and how Buzz is testing a multiplayer model where humans and AI agents work in the same space.

Key Topics Covered

Goose, Block's open-source AI agent, and the Agentic AI Foundation

Agents vs. harnesses vs. interfaces

MoneyBot, ManagerBot and BuilderBot

Why AI memory should belong to the business, not the bot

Stigmergy and ants as a model for shared memory

Buzz and Block's "multiplayer" approach to AI at work

"Meat proxy" and the new office busywork AI can create

Why the bottleneck is shifting from writing code to deciding what to build

How AI is changing hiring, interviews and day-to-day work

The human cost of spending all day working with AI

Episode Timestamps

00:00 Intro

00:07 What Block actually is

02:40 From CERN to Block

05:19 Building Goose and taking it open source

06:29 Agents vs. harnesses vs. interfaces

10:28 MoneyBot, ManagerBot and BuilderBot

15:56 Memory, access and learning over time

22:53 What ants can teach us about AI memory

26:48 Two versions of where AI could go

28:26 Buzz and the idea of multiplayer AI

29:56 "Meat proxy": the new office problem

35:09 The Buzz case study and a 50% productivity jump

37:06 The new bottleneck now that AI can write the code

49:11 Rebuilding institutional knowledge after team restructuring

52:00 How AI is changing hiring and interviews

57:35 The loneliness of working with AI all day

59:13 Why Brad does what he does

Connect with Brad on LinkedIn:

https://www.linkedin.com/in/bradleyaxen/

Partner Links

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In this episode, Loni Stark, VP of Strategy and Product at Adobe, joins Liam to talk about what happens when a 25 year tech career runs alongside a full creative practice in painting, sculpture and writing, and what that split brain teaches her about building for the AI era. Loni explains why she thinks brands may already be invisible, or worse, misrepresented, inside AI answers, why every company needs to start treating AI as a new kind of audience, and how she is running her own home AI lab, complete with a self built server and a personal agent that has now run continuously for over 150 days, to understand what actually gives an AI agent an identity.

Along the way, Loni and Liam get into her Harvard Extension School research into "orphan values," the personal values people can't express in any of their current life roles, and what happens to that alignment as AI reshapes the roles themselves. She also breaks down how she balances Adobe's biggest enterprise bets, including Experience Manager, Commerce, Brand Concierge and LLM Optimizer, against the need to experiment without limits in her own time.

Key Topics Covered

Why Loni keeps a full art practice, painting, sculpture and writing, alongside her tech career

Growing up with parents who didn't understand the arts, and using creativity as a form of rebellion

Whether humans are innately creative, and why AI makes protecting your own voice more important

Why Loni has stayed at Adobe for 25 years, and how she thinks about "growing the aquarium"

Building a personal AI server at home instead of a garden, and what that setup actually involves

Swapping the underlying model and the agent harness to test what gives an AI agent a persistent identity

Her Harvard Extension School research into "orphan values" and how AI is reshaping the roles we express them through

The shift from human mediated to AI mediated experiences, and why that changes what "traffic" even means

Why being invisible to AI isn't the worst case, being misrepresented by it is

How brands should start preparing their content and catalogs to be "agent ready"

The placebo effect of working with agents, and how that belief shapes performance and creativity

How Loni balances limitless experimentation with the governance enterprise AI actually requires

Why she does what she does: an insatiable need to grow, create and become more than she currently is

Episode Timestamps
00:00 - Introduction and welcome
00:05 - Balancing a full art practice with a 25 year tech career
05:38 - Why she's stayed at Adobe for 25 years
08:30 - AI as the biggest creativity enabler she's seen
12:50 - Inside her home AI lab: hardware, memory, and swapping the agent harness
18:11 - Studying psychology at Harvard, and what "orphan values" mean
26:28 - The shift to AI mediated business, and why invisible isn't the worst case
31:46 - How brands become "agent ready" for the AI agent economy
36:23 - The placebo effect of working with AI agents
37:35 - Balancing limitless experimentation with enterprise governance
47:05 - Why Loni does what she does
49:14 - Where to find Loni and closing thoughts

Loni's Socials:
LinkedIn - https://www.linkedin.com/in/lonistark/

Loni’s Art Gallery: https://atelierstark.com/work/

Partner Links
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