Product Impact Podcast | Secrets to unlocking the value of AI

Managing an AI agent isn't using a tool — it's supervising labor. Most companies skipped that step. In Procurify's recent survey of finance leaders, 35% said trust — not model capability — is the single biggest factor in whether their organization can actually deploy agents. The teams already shipping report 63% ROI from time savings and 60% from improved data accuracy, but only after they did the unglamorous work first: defined the operating model, baked in governance and audit trails, and consolidated their data into a single source of truth. Frontier models keep commoditizing generic intelligence. The value is moving up the stack — to the workflow, the context, and the data your company actually runs on.

Procurement has sat in the middle of every enterprise's audit trail for decades — budgets, contracts, suppliers, approvals, compliance, payments. It's the use case AI vendors have been quietly building toward, because if you can make procurement feel less clunky, you've solved governance for the rest of the business. We sat down with Procurify's Chief Product & Technology Officer Jonathan Su to understand what an AI-native operating model actually looks like, why production-grade is now ten times harder than prototype, and what shifts when the bottleneck in your team moves from execution to judgment.

In this episode:

• Why 35% of finance leaders say trust — not model capability — is the biggest factor in whether agents actually ship
• The operating model most companies skip: governance, audit trail, single source of truth — before the agent touches work
• What AI ROI actually looks like — 63% time savings, 60% better data accuracy, plus the business KPIs that prove it
• Why value is moving up the stack as frontier models commoditize generic intelligence — workflow, context, data, distribution
• How procurement teams redesign workflows around agents instead of tacking AI on top of an already broken process
• The hire that beats 20 years of experience: grit, taste, judgment, and the ability to learn in 4-month cycles

"Managing an agent is more than just using a tool. It's sort of like supervising labor." — Jonathan, Procurify

"The cost of producing something is dramatically lower, but the bottleneck shifts to judgment, craftsmanship, and taste. Just because you could do something doesn't mean you should." — Jonathan, Procurify

We built productimpactpod.com to be your AI product insights and strategic playbook hub. Check it out.

Thank you for listening to the Product Impact Podcast — if you have feedback, guest recommendations, or want to chat — contact us.

About Jonathan: Jonathan is Chief Product Officer at Procurify, where he leads product strategy and AI initiatives across the company's spend management platform. He has spent his career in payments, fintech, and enterprise software, and now leads Procurify's transition to an AI-native product organization. Procurify serves finance teams managing budgets, approvals, invoicing, and payments — the workflows where governance and AI agents have to coexist. 

• Procurify: ⁠https://www.procurify.com⁠
• Jonathan on LinkedIn: ⁠https://www.linkedin.com/in/jonathanhaosu⁠

Hosted by:

• Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/
• Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/

Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.com

This episode was brought to you by:

PH1 (https://ph1.ca) — a strategy & research consultancy specialized in delivering evidence about the highest value use cases and customer profiles.

AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.

Every 1% increase in the context your agents receive produces a 0.38% improvement in output quality. LangChain's State of AI Agents 2026 report makes that measurable — and it makes interface design the highest-leverage investment most product teams aren't treating it as. At Atlassian Team '26 in Anaheim last week, Chief Design Officer Charlie made the case: the interface is what determines how context gets captured, which means every design decision your team makes is now directly setting a ceiling on how well your agents perform. Eighty-eight percent of enterprise agent pilots fail to reach production, with context fragmentation as the top blocker. That is a design problem.

For 25 years, adaptive interfaces were the holy grail — software that reads who you are and adjusts to how you work. Charlie's announcement at Team '26: the technology limitation is gone. What remains is a design question about where to set the balance point between a system that adapts and a system a team can actually share. And at the same time, designing for agents and designing for humans has converged into nearly the same problem — Atlassian's design system is consumed by agents and human users from the same object, with 10% variation. Every shortcut taken on design quality now shows up twice.

Charlie Sutton is Chief Design Officer at Atlassian, where he leads design across Jira, Confluence, Rovo, and the newly announced Dia browser. He sat down with us at Team '26 in Anaheim. 

In this episode:

• Why 783 tab interactions a day means even tiny friction changes produce outsized aggregate gains — and where to look first
• The 25-year holy grail of adaptive interfaces is technically solved — what remains is the design question of how much is right for teams
• Why structured objects (goals, strategy, people) beat expensive inference — and why most vendors are paying more for worse results
• How Atlassian's design system serves agents and humans from the same object with 10% variation — and what the 10% tells you
• Why vibe coding raised the floor so everyone can build, which is exactly why the ceiling on what design must deliver also rose
• Why video captures intent that text never can — and how Atlassian is encoding it into the Teamwork Graph

"The floor goes up — everyone can make things awesome. But the ceiling has also gone up. Expectations increase, what is possible has increased. Design is still focusing on that ceiling."

Charlie Sutton is Chief Design Officer at Atlassian, where he leads design philosophy and execution across the company's full product suite — including Jira, Confluence, Rovo, and the newly announced Dia browser. He was involved in building the demos showcased at Atlassian Team '26 and works at the intersection of enterprise product design and AI-native interface development. (Verify Charlie's full name before publishing.)

Guest resources:

• Atlassian: https://www.atlassian.com
• Dia browser: https://www.atlassian.com/software/dia
• Charlie on LinkedIn: https://au.linkedin.com/in/charliesutton

We built productimpactpod.com to be your AI product insights and strategic playbook hub. Check it out.

Thank you for listening to the Product Impact Podcast — if you have feedback, guest recommendations, or want to chat — contact us.

Hosted by:

• Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/
• Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/

Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.com

This episode was brought to you by:

PH1 (https://ph1.ca) — a strategy & research consultancy specialized in delivering evidence about the highest value use cases and customer profiles.

AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.

The fastest teams didn't switch to a better AI model. They gave their AI memory. At Atlassian Team 2026 they showed us the next evolution of AI capabilities: 150 billion connected objects across an organization, an agent reviewing 2 billion lines of code in 2 minutes, and 44% better answers using half the tokens. Inside teams, the change is concrete: a junior analyst gets years of knowledge instantly, and a product leader can oversee an entire enterprise's deployment.

Our guest, Jamil Valliani leads AI product at Atlassian, where he has spent three years building the context layer that will help 300,000 companies. They also shocked everyone by announcing that the Teamwork Graph — is open to be connected to your work in Microsoft, Adobe, and Google.

In this episode you'll learn:

• Why Atlassian made their context graph open
• Evidence that context improves token usage
• What the future of work will look like
• The key to delivering value at scale

We built https://productimpactpod.comproductimpactpod.com to be your AI product insights and strategic playbook hub. Check it out.

Thank you for listening to the Product Impact Podcast — if you have feedback, guest recommendations, or want to chat — contact us.

About Jamil Valliani: Jamil Valliani is VP / Head of Product, AI at Atlassian, where he leads Rovo and the Teamwork Graph across the company's full product suite. He has been building AI product strategy at Atlassian since before the Rovo launch and works across the enterprise customer base to understand where AI adoption is actually working and where it stalls. Atlassian's tools — Jira, Confluence, Bitbucket, and connected third-party systems — are used by over 300,000 companies worldwide.

• Atlassian: https://www.atlassian.com
• Rovo: https://www.atlassian.com/software/rovo
• Jamil Valliani on LinkedIn: https://www.linkedin.com/in/jamil-valliani-b131881/

Hosted by:

• Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/
• Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/

Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.com

This episode was brought to you by:

PH1 (https://ph1.ca) — an strategy & research consultancy specialized in delivering evidence about the highest value use cases and customers profiles.

AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.

Most enterprise AI investments in customer experience are stuck somewhere between a demo and a disappointment. The Qualtrics 2026 Customer Experience Trends Report found that nearly one in five consumers who used AI customer service saw zero benefit from the interaction. The bar for what enterprises are calling AI innovation is shockingly low, and customers feel it every time they're routed to a bot that reads from an FAQ.

Rikki Singh leads product innovation at Twilio. Before Twilio she was at McKinsey, where she co-authored the definitive research on what makes a great PM. Before that she was a PM at Microsoft. She's now running the team behind what Twilio is calling its biggest launch in 17 years — an agent-native channel with conversation memory across voice, text, and email.

In this episode we cover:

➜ Why most AI customer experiences are still just RPA with better packaging — and the right metric to anchor on instead

➜ Why token consumption made AI spend as unpredictable as AI ROI, leaving enterprise decisions with uncertainty on both sides

➜ Why the LLM wrapper creates false confidence — the model is not thinking, it's generating strings non-deterministically

➜ Vitamins vs painkillers: how to parse the signals customers don't say out loud from the ones that don't actually matter

➜ How to protect long-horizon bets inside a public company: separate PMs by horizon and celebrate what you disprove

➜ Why the brand owns the accountability when AI gets a high-stakes interaction wrong, regardless of which vendor caused it

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If you found this episode useful, please like, share, and send it to anyone on your team who'd find it helpful.

We built ⁠https://productimpactpod.com⁠ to be your AI product strategy and AI product news hub. Check it out.

Thank you for listening to the Product Impact Podcast — if you have feedback, guest recommendations, or want to chat — contact us.

Hosted by:

➜ Arpy Dragffy Guerrero — ⁠https://www.linkedin.com/in/adragffy/⁠

➜ Brittany Hobbs — ⁠https://www.linkedin.com/in/brittanyhobbs/⁠

Go to Substack to get AI strategy frameworks, news, and jobs: ⁠https://productimpactpod.substack.com⁠

This episode was brought to you by:

➜ PH1 (⁠https://ph1.ca⁠) — an AI strategy consultancy specialized in improving the measurable success of AI products.

➜ AI Value Acceleration (⁠https://aivalueacceleration.com⁠) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.

Most companies are running AI in production right now without any plan to govern and secure their businesses. This week Claude Code wiped out a business' entire database in 9 seconds. Anything is possible when an agent is given access to everything without governance. John Willis co-wrote The DevOps Handbook a decade ago because software teams were shipping code the same way — fast, manual, no visibility. He sees the same pattern repeating with AI, and he has spent five decades watching what happens when the gap between vendor promises and operational reality gets this wide. He's written 6 books and also happens to be a historian about AI.

In this episode we cover:

• Why shadow AI — no ban, no guidance, company data on personal phones — is the most dangerous place to be
• Why higher throughput and higher instability at the same time is the predictable outcome of speed without feedback loops
• Why governance creates flow instead of stopping it — and how that lesson from DevOps applies directly to AI now
• Why most teams think they have AI observability when they actually have ML evaluation tools solving a different problem
• Why every team — even a five-person startup with no CTO — needs digitally signed audit trails for agent decisions
• What the history of AI winters and springs tells us about where we actually are in the current cycle

If you found this episode useful, please like, share, and send it to anyone on your team who'd find it helpful.

We built https://productimpactpod.com to be your AI product strategy and AI product news hub. Check it out.

Thank you for listening to the Product Impact Podcast — if you have feedback, guest recommendations, or want to chat — contact us.

Hosted by:

• Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/
• Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/

Featured guest:

John is an accomplished author and innovative entrepreneur with over 35 years of experience in enterprise IT and research, driven by a deep passion for exploring the intersection of Generative AI and the transformative principles of Dr. W. Edwards Deming. He is the author of Rebels of Reason, a book that traces the history of artificial intelligence while uncovering the human stories behind its rise, connecting today’s AI landscape to the ideas and people that shaped the field and offering a unique perspective on its future in business. As a co-author of foundational DevOps works, John brings a rare blend of technical expertise and insight into the human dynamics of innovation, helping leaders cut through hype to focus on creating real customer value through a deeper understanding of AI’s context and systems.

John’s LinkedIn: https://www.linkedin.com/in/johnwillisatlanta/

Link to John’s Book Rebels of Reason: https://www.amazon.com/Rebels-Reason-Aristotle-ChatGPT-Heroes-ebook/dp/B0FCD8TW8R

Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.com

This episode was brought to you by:

• PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products.
• AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.

Stanford's 2026 AI Index just dropped. China closed a thirty-point AI performance gap to under three percent — on twenty-three times less investment. Apple picked their head of hardware as the next CEO. Anthropic's Mythos model found 271 zero-day vulnerabilities in Firefox. And Vercel and Lovable both got breached this month.

We break down the numbers that should be on every product leader, designer, and founder's desk this week — what they mean, and exactly what to do about each one.

In this episode we cover:

➜ Stanford AI Index 2026: 88% organizational adoption, $581 billion in investment, and why China closing the gap on a fraction of the budget is the most important data point in the report

➜ Token economics explained — what tokens are, what they cost, and why the shift from flat-rate licensing to usage-based pricing changes your AI budget math overnight

➜ Why replacing Figma with Claude Design costs $0.22 for a first draft and $2,600 at refinement scale — and what that reveals about real-world AI costs

➜ Why Apple chose John Ternus as CEO and elevated Johny Srouji to Chief Hardware Officer — and what that says about where AI value will actually live

➜ Mythos, Vercel, Lovable: why vibe coding has never been easier and information security has never been more important

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If you found this episode useful, please like, share, and send it to anyone on your team who'd find it helpful. ⁠https://productimpactpod.com⁠ — Our news platform just launched. It is the best place to get the AI product news that matters.

Hosted by:

➜ Arpy Dragffy Guerrero — ⁠https://www.linkedin.com/in/adragffy/⁠ 

➜ Brittany Hobbs — ⁠https://www.linkedin.com/in/brittanyhobbs/⁠

Go to Substack to get AI strategy frameworks, news, and jobs: ⁠https://productimpactpod.substack.com⁠

This episode was brought to you by:

➜ PH1 (⁠https://ph1.ca⁠) — an AI strategy consultancy specialized in improving the measurable success of AI products.

➜ AI Value Acceleration (⁠https://aivalueacceleration.com⁠) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.

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Sources referenced in this episode:

Stanford AI Index 2026 — https://productimpactpod.com/news/stanford-ai-index-2026-product-team-takeaways 

Stanford: US can't buy an AI lead — https://productimpactpod.com/news/stanford-ai-index-proves-us-cant-buy-ai-lead 

Claude Design vs Figma — https://productimpactpod.com/news/figma-claude-design-source-of-truth-for-design 

Apple CEO transition — https://productimpactpod.com/news/how-tim-cook-leaves-apple-future-of-ai 

Anthropic Mythos Preview — https://techcrunch.com/2026/04/07/anthropic-mythos-ai-model-preview-security 

Vercel breach — https://techcrunch.com/2026/04/20/app-host-vercel-confirms-security-incident 

Lovable vulnerability — https://thenextweb.com/news/lovable-vibe-coding-security-crisis-exposed 

AI token pricing — https://www.cnbc.com/2026/04/17/ai-tokens-anthropic-openai-nvidia

A small cohort of engineers — Andrej Karpathy, Mitchell Hashimoto, Simon Willison — are producing in a week what used to take a month. Meanwhile, seventy-eight percent of enterprise AI deployments show no bottom-line impact. Ninety-five percent of pilots fail within six months. The gap between the people getting extraordinary results and the organizations getting nothing is not talent. It's architecture. And it has a name.

In this episode of the Product Impact Podcast, Arpy and Brittany break down why enterprise AI is failing at scale, what the engineers who are eighteen months ahead have figured out, and the two radically different futures that orchestration makes possible.

In this episode we cover:

• The $490 billion AI value crisis — why adoption is surging and returns are near zero, and what Forrester, McKinsey, PwC, and Gartner are documenting
• Five failure patterns hiding inside every enterprise deployment — and why more training, more change management, and more executive support won't fix any of them
• The pioneers building the future of work in public — Karpathy's vibe coding, Hashimoto's production-code throughput, Willison's hundreds of public experiments — and what they've proven about orchestration as engineering discipline
• Two outcomes of orchestration that most organizations aren't ready for: building bespoke deterministic software at a scale that was never economic before, and building an operating system where agents work autonomously on your behalf
• Why markdown — not PDFs, not databases, not dashboards — is emerging as the knowledge substrate for the agent era, and why Karpathy himself is now calling for AI to organize wikis rather than chat

"These are not technology failures. They are failures of imagination about what work actually is and how AI fits into the way we work." — Arpy Dragffy

"The primary failure mode in AI adoption is not capability. It is transferability." — Brittany Hobbs (citing Harvard Business Review)

https://productimpactpod.com

Thank you for listening to the Product Impact Podcast (formerly Design of AI) — Prove impact. Improve impact. Scale impact.

Hosted by:

• Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/
• Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/

Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.com

This episode was brought to you by:
PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products

.
AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.

The Apple Industrial Design Group. The original PowerBook. Beats by Dre. The June Oven. The Polaroid Cube. Square Stand. Lyft Amp. One designer is behind all of them. Now Robert Brunner is turning his attention to something the entire AI industry is getting wrong: how intelligence should actually feel in the physical world.

In this episode of the Product Impact Podcast, Robert Brunner — founder of Apple's Industrial Design Group, the man who hired Jony Ive, design partner on Beats by Dre, and founder of Ammunition — makes the case that the next generation of AI products needs less technology and a lot more taste.

In this episode we cover:

➔ Why the best AI feature is the one you never notice — and why engagement-driven AI is already eroding the trust every product is built on

➔ The Apple and Beats lesson every AI founder should steal: technology enables, but design establishes

➔ Why "AI for everyone" is the trap that guarantees mediocrity — and how to pick the right audience without shrinking the market

➔ The cognitive asset AI will never have: taste, insight, and judgment shaped by a life actually lived

➔ What Brunner's new venture Object is building: calmer, distributed consumer AI that respects attention instead of competing for it

"The companies that build things that matter always have a clear point of view about people." — Robert Brunner

"The next great technology companies will be the ones people trust with their lives, not just their data." — Robert Brunner

Robert Brunner founded Apple's Industrial Design Group (Apple IDg), hired Jony Ive, and led the design of the original Macintosh PowerBook and Newton. After a partnership at Pentagram, he founded Ammunition in 2007, where he co-created Beats by Dre with Jimmy Iovine and Dr. Dre and designed the June Intelligent Oven, Polaroid Cube, Square Stand, Lyft Amp, and the Limitless Pin. He is co-author of Do You Matter? How Great Design Will Make People Love Your Company and is currently building Object, a new venture developing AI-powered consumer electronics designed to improve digital wellbeing.

• Ammunition Group — https://ammunitiongroup.com
• Robert Brunner on LinkedIn — https://www.linkedin.com/in/robertbrunner/
• Do You Matter? How Great Design Will Make People Love Your Company — https://www.amazon.com/Matter-Great-Design-People-Company/dp/0137142447
• Robert Brunner on Prototyping Your Life, Leaving Apple, and Forging Beats by Dre (Yanko Design) — https://www.yankodesign.com/2025/09/28/robert-brunner-on-prototyping-your-life-leaving-apple-and-forging-beats-by-dre/

https://productimpactpod.com

Thank you for listening to the Product Impact Podcast (formerly Design of AI) — Prove impact. Improve impact. Scale impact.

Hosted by:

• Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/
• Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/

Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.com

This episode was brought to you by:

PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products.

AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.

We have benchmarks for model performance, metrics for productivity, and KPIs for everything the economy can quantify. But the impact of AI on how we think, who we become, and what we lose in the process? Nobody's measuring that — because nobody knows how.

Helen and Dave Edwards have spent a decade studying what AI does TO humans, not just what it can do for us. In this episode, they challenge the binary of AI hype vs. AI fear and lay out a framework for something far more important: cognitive sovereignty — our ability to remain the authors of our own thinking in an era of automated cognition.

In this episode of the Product Impact Podcast:

• Why the AI industry's business model is capital replacing labor — and why that's a path to economic collapse, not growth
• The concept of cognitive sovereignty and why preserving your ability to think independently is the real competitive advantage
• Research showing AI is increasing scientific citations but decreasing exploration — pulling everyone toward the median
• Why the one-person billion-dollar company is a fantasy that breaks down the moment you do the math
• The products getting it right: Bass (trust-first healthcare AI), Latimer (data sources that don't exist anywhere else), and creative tools treating AI as collaborator, not replacement

"If AI can replace the humans in your business, does your business have any value at all?" — Dave Edwards

"There is no point having this technology if it makes us dumber, if it makes us less kind, if it makes us more lonely, if it makes us less able to show up for others." — Helen Edwards

Helen and Dave Edwards are researchers and founders of the Artificiality Institute, where they lead a transdisciplinary community of scientists, designers, philosophers, and artists exploring what it means to be human in the age of AI. They are currently publishing Stay Human — a chapter-by-chapter book on how AI changes our thinking, identity, and relationships.

Guest resources:

• Artificiality Institute — https://artificiality-institute.org
• Stay Human (free, published weekly) — https://journal.artificiality-institute.org
• LinkedIn: Helen Edwards (https://www.linkedin.com/in/helenedwards/) | Dave Edwards (https://www.linkedin.com/in/daveedwards/)

Artificiality Summit 2026 — Oct 22-24, Bend, Oregon. A human gathering to figure out what it means to be human. Learn more at artificiality-institute.org

productimpactpod.com

Thank you for listening to the Product Impact Podcast (formerly Design of AI) — Prove impact. Improve impact. Scale impact.

Hosted by:

• Arpy Dragffy Guerrero — https://www.linkedin.com/in/adragffy/
• Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/

Go to Substack to get AI strategy frameworks, news, and jobs: https://productimpactpod.substack.com

This episode was brought to you by: PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products. AI Value Acceleration

(https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stall

AI went from chatbots to assistants to agents in three years — and each era moved the failure one layer deeper. First we got wrong answers, then wrong context, now wrong actions. The tools are moving at an extraordinary pace, and almost nobody is keeping up.

In this episode of the Product Impact Podcast we tackle The Agents Era Will Change How We Work:

* Why vibe coding was the proof of concept for the entire agent era

* Agents aren't automating tasks — they're automating your thinking

* Why you'll be using a dozen agents within a year, not because you chose to, but because the work will demand it

* The better you understand how you think, the more you'll succeed with agents

* How we need to retrain ourselves — because decades of linear, process-driven work haven't prepared us for this

* The startups most people haven't heard of that are already replacing how entire functions operate

https://productimpactpod.com

Thank you for listening to the Product Impact Podcast (formerly Design of AI) — Prove impact. Improve impact. Scale impact.

Hosted by:

* Arpy Dragffy Guerrero —https://www.linkedin.com/in/adragffy/

* Brittany Hobbs — https://www.linkedin.com/in/brittanyhobbs/

Go to Substack to get AI strategy frameworks, news, and jobs:https://productimpactpod.substack.com

This episode was brought to you by:

PH1 (https://ph1.ca) — an AI strategy consultancy specialized in improving the measurable success of AI products.

AI Value Acceleration (https://aivalueacceleration.com) — The consultancy specialising in enterprise value creation. Make sure that your spending doesn't go to waste. Find out exactly where the value creation of adopting AI products stalls.