Beyond The Prompt - How to use AI in your company

Laura Jones explains that generative AI is raising the bar for creativity. When everyone can produce “pretty good” content, the real challenge is creating something that actually stands out. The risk is not poor output, but settling too quickly for what already works.

She argues that as products become more similar, brand becomes a signal of trust. Not in a visual sense, but in the experience behind it. At Instacart, that shows up in details like how a banana is selected. With over a billion bananas delivered and millions of orders including notes on ripeness, customers are expressing very specific preferences. That behavior led to both new product features and the creative idea behind their Super Bowl campaign.

The conversation also explores how teams should work with AI. While it can automate repetitive tasks and speed up iteration, it can also create a tendency to agree with what’s generated, especially when working alone. Laura emphasizes that the best ideas still come from people challenging each other, building on different perspectives, and pushing beyond the first acceptable answer.

Key takeaways: 

Mediocre is easier than ever, which raises the bar for originality

When AI gets everyone to “pretty good,” the work that stands out has to go further. The bar is not lower. It is higher.

Brand becomes trust when products converge

As functionality becomes easier to replicate, the question becomes who you trust to get it right. Brand is the answer to that.

Only do what only you can do

Use AI to take on repetitive work, then spend your time on judgment, insight, and decisions that require a human point of view.

Need-finding still requires real people

Synthetic research can help, but it cannot replace observing real behavior. The banana insight came from what customers actually did.

Human plus bot plus human

Working only with AI makes it easy to agree and move on. The best ideas come from people challenging each other, with AI in the middle, not as the whole process.

Instacart: instacart.com

Super Bowl ad: Super Bowl (Instacart ad)

Laura LinkedIn: linkedin/laurajones

ro's post: ro.co/perspectives/super-bowl-economics

 

00:00 Intro: Originality vs AI Complacency
00:27 Meet Laura Jones
01:23 Brand as trust when products converge
03:50 Personalization and reducing mental load
06:24 What still matters in marketing
10:33 Why need-finding cannot be shortcut
14:09 Using AI without losing judgment
16:33 New channels and where customers actually are
21:35 Why “dopey ideas” matter
25:42 Human plus bot plus human
28:44 Inside the Super Bowl ad
31:47 From banana insight to product
34:49 Taking creative risks at scale
37:34 Fear, pressure, and team chemistry
46:24 AI and faster prototyping
53:26 The debrief

📜 Read the transcript for this episode:


For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:
Henrik: https://www.linkedin.com/in/werdelin
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Show edited by Emma Cecilie Jensen.

Dan Klein, professor at UC Berkeley and CTO at Scaled Cognition, explains that AI systems generate answers based on patterns in language rather than verified knowledge. This makes them highly capable across many tasks, but also means they can produce confident answers even when they are not fully accurate.

He introduces the “jagged frontier,” where AI performs very well in some areas and less reliably in others. Because responses are fluent and convincing, it is often hard to see where those limits are, which makes it important to stay engaged when using these systems.

The conversation also explores hallucinations as a natural part of generative systems. In some cases, this is what makes them valuable, especially for creative or open-ended tasks, while in other cases reliability becomes more important.

Finally, Dan highlights that working effectively with AI is a skill. As more people start using these systems in their daily work, knowing how to guide them, evaluate outputs, and apply them in the right contexts becomes increasingly important. He also shares how his team at Scaled Cognition is tackling this challenge by building AI systems with fundamentally different architectures, focused on determinism and reliability — aiming to ensure systems follow rules, reflect underlying data accurately, and behave predictably in high-stakes, policy-driven use cases.

Key Takeaways:

AI is designed to sound right, not to know it’s right

Models generate fluent answers without knowing whether they are correct, which means users need to actively evaluate outputs

You have to learn where AI works and where it doesn’t

Capabilities are uneven, and understanding those limits is key to using AI effectively

Working with AI shifts your role from creator to editor

Instead of starting from scratch, you are reviewing, refining, and validating what the model produces

Most people are using AI without knowing how to manage it

Skills like delegation, verification, and judgment are becoming essential, but are not widely taught

Dan's LinkedIn: linkedin/dan-klein/

Scaled Cognition Website: scaledcognition.com

Scaled Cognition LinkedIn: linkedin/company/scaledcognition/

Scaled Cognition X: x.com/ScaledCognition

00:00 Intro: Fluency vs Truth
00:34 Meet Dan Klein
02:53 Why Fluency Misleads
05:11 How LLMs Guess
07:30 What Is Hallucination
08:54 Deception and Alignment
11:22 Why Agents Break
12:48 Chaining and Determinism
16:01 When Hallucination Helps
22:33 Beyond Scale for Reliability
30:40 Synthetic Data Training
31:10 Enterprise Agent Use Cases
33:44 Healthcare Risks
39:13 Enterprise Literacy Gap
41:27 Delegation and AI Management
54:37 The Debrief

📜 Read the transcript for this episode: nobody-is-getting-new-manager-training-for-their-ai-team-with-dan-klein-uc-berkeley/transcript


For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:
Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Melissa Cheals leads Smartly, a payroll and people management platform serving 24,000 small and medium businesses in New Zealand. In this conversation, she shares how AI is reshaping product development, leadership, and how organizations operate.

A key moment comes when her team estimates new features will take 12 months and $1M to build. Instead of accepting it, Melissa pushes back, using AI to better understand her team’s perspective and communicate the need for change more effectively. This becomes a broader shift in how she approaches leadership, using AI to think more clearly and navigate conversations with less friction.

The discussion expands into strategy. Companies now face a fundamental choice: become AI-native or continue building on existing systems. As AI adoption increases, it also exposes silos and bottlenecks. Melissa shares why cross-functional collaboration—and leaders actively engaging with AI themselves—is critical to navigating this shift.

Key Takeaways: 

Becoming AI-native is a defining decision

It’s not just a technology shift. Leaders need to decide whether to rebuild around AI or continue layering it onto existing systems, and that choice shapes how the company operates.

AI shifts us from scarcity to abundance

Many organizations still think in terms of limited time and resources, but AI changes what’s possible and forces leaders to rethink how big they can think and what they can achieve.

AI is a leadership amplifier

Beyond productivity, AI helps leaders think more clearly, reframe conversations, and communicate change in a way that is both effective and respectful.

Leaders can’t delegate AI

Without hands-on experience, it becomes difficult to challenge assumptions, guide teams, or make informed decisions about what’s possible.

Smartly: smartly.co.nz

LinkedIn Melissa: linkedin.com/melissa-cheals

LinkedIn Smartly: linkedin.com/company/smartlynz/

00:00 Intro: Challenging AI Assumptions
00:28 Meet Melissa Cheals
01:17 The Spark For Change
02:36 Vision And Early Signals
03:48 Hiring For Transformation
06:12 Unlocking Data With AI
08:27 Breaking Silos Across Teams
10:39 Why Leaders Must Learn AI
13:42 Leading With AI And Clarity
17:05 The AI-Native Decision
21:45 Thinking Bigger With AI
25:23 Less Meetings More Writing
26:33 The Self-Disruption Imperative
29:11 Breaking Silos With Value Streams
31:28 Managing Fear And Change
32:50 Learning And Shipping Faster
34:58 Debrief

📜 Read the transcript for this episode: ai-native-or-not-the-defining-choice-for-companies-right-now-with-melissa-cheals-ceo-of-smartly/transcript


For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:
Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Greg Shove describes a growing gap between individual and organizational AI adoption. A small group of employees are already using AI effectively, while most companies are still early. AI is generating real productivity gains, but those gains are not being captured at the company level. Instead, they are absorbed by individuals who use AI to work faster, often without changing team outputs or structures — raising a central question: if AI creates time, where does that time go?

The conversation explores why enterprise AI adoption remains uneven. Many organizations lack a clear point of view on AI, and workflows take time to adapt, making it difficult to turn individual gains into coordinated results. At the same time, AI is breaking capability boundaries, allowing people to take on work across roles while companies remain structured around existing ways of operating.

From a leadership perspective, Greg emphasizes that the challenge is not just efficiency. AI creates capacity, but without clear direction on how to use it, that capacity disappears. Leaders must decide how to reinvest the time AI creates if they want to capture real business value.

Key Takeaways: 

AI’s ROI is leaking, not missing

Companies are generating value from AI, but it’s being captured by employees rather than the organization.

A small group drives most of the impact

Roughly 10–15% of employees adopt AI early and use it effectively, creating an uneven distribution of gains.

AI is breaking capability boundaries

Individuals can now take on work across roles, but organizations are still structured around fixed responsibilities.

Most companies lack a clear point of view on AI

Without direction from leadership, adoption becomes fragmented and employees are left to figure it out themselves.

Leaders must decide what to do with the time AI creates

Efficiency gains alone don’t create value. Organizations need to define new, higher-value work or the gains disappear.

Greg's LinkedIn: linkedin/gregshove

Section LinkedIn: linkedin/company/sectionai

Section AI: sectionai.com

Prof AI: prof.ai

00:00 Intro: Entering the Era of AI Chaos
00:31 Meet Greg Shove
01:32 Enterprise AI Is a C Minus
01:51 AI’s ROI Is “Leaking” to Employees
03:04 When Individuals Outrun the Organization
05:44 When AI Breaks Workflows
06:47 Disposable Software and New Ways of Building
09:10 Cut vs Create
12:01 Using the Calendar as a Lever
16:24 Why Enterprises Don’t Move
17:32 When Customers Force Change
21:31 AI Breaks Capability Boundaries
25:44 The Productivity Firehose
27:49 Who Actually Captures the Value
28:45 Why Everyone Needs Good AI
32:00 Adoption Beats Buying More Tools
40:17 Teaching the 90 Percent
43:48 Where Humans Still Matter
48:09 The Debrief

📜 Read the transcript for this episode: greg-shove-on-why-most-companies-are-not-seeing-roi-on-ai-yet/transcript


For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:
Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Leidy Klotz has spent years studying a simple but overlooked phenomenon: when we try to improve something, our first instinct is to add rather than remove. He shares the Lego bridge experiment that sparked his research and explains how this additive bias scales from small design decisions to entire organizations. Over time, companies accumulate reporting lines, meetings, software, and policies without questioning what no longer serves them.

Henrik and Jeremy explore how AI tools intensify this pattern. When generating ideas, launching projects, writing code, or producing content becomes effortless, the temptation to add grows stronger. The cost of producing information drops, but the cost of consuming it rises. Without guardrails, organizations risk what Leidy calls “organizational indigestion.”

The discussion moves from insight to implementation. Leidy outlines practical ways to counteract additive bias, including stop-doing lists, default kill dates on projects, and designing environments that make subtraction visible and acceptable. In a world of accelerating AI output, leaders must intentionally decide what to remove, what to protect, and what truly matters.

Key Takeaways: 

We default to adding, not subtracting

When faced with a problem, our instinct is to introduce something new. Subtraction rarely occurs to us, even when removing something would improve clarity and performance.

Generative AI amplifies additive bias

AI makes producing content, code, and ideas easier than ever. Without constraints, this frictionless creation can accelerate complexity instead of progress.

More organizations die from indigestion than starvation

Over time, companies accumulate tools, processes, and policies that quietly slow them down. The real risk is often not too few ideas, but too many unexamined additions.

Architecture beats willpower

Rather than relying on discipline alone, leaders can design systems that encourage subtraction. Stop-doing lists and default expiration dates make removal expected instead of exceptional.

Protect what matters before adding more

Before introducing new tools, workflows, or AI systems, leaders must define what is already working and worth protecting. Subtraction requires clarity about what should stay, not just what should go.

Subtract: amazon/Subtract-Untapped-Science-Leidy-Klotz

In a Good Place: amazon/Good-Place-Spaces-Where-Thrive/

Leidy's Speaking: https://leidyklotz.com/

Clip from Bear: Subtract - this is how you do better

00:00 Intro: Our Instinct to Add
00:28 Meet Leidy Klotz
01:15 The Subtract Idea
02:56 Organizations Get Bloated
03:49 Scandinavian Design Mindset
04:32 New Book: In a Good Place
05:59 AI Abundance and Indigestion
08:12 Curate Context, Not More
11:38 Cues and Stop-Doing Lists
15:00 Default Debt and Kill Dates
17:10 Odysseus Contracts and Biases
21:28 Reengage the Physical World
29:17 Bike Shedding and Priorities
36:10 Making Is Thinking
49:16 The Debrief

📜 Read the transcript for this episode: how-to-subtract-the-most-underrated-skill-of-the-ai-era-with-leidy-klotz/transcript


For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:
Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.