Kristin Gleitsman

Leadership in Biotech

Tag: genai

Illustration of a desk with a figure from a recent paper about DeepSomatic

DeepSomatic and the question of how AI learns from itself

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On October 18, 2025

In AI ∩ Bio

DeepSomatic, published this month in Nature Biotechnology, represents a milestone for cancer genomics: a deep-learning method that detects somatic small variants across both short- and long-read sequencing data. Built on Google’s DeepVariant framework, it bridges Illumina, PacBio HiFi, and Oxford Nanopore datasets and introduces CASTLE, a new multi-platform benchmark of six tumor–normal cell lines made openly available to the community. For anyone working in precision oncology, the technical ambition here is remarkable: one model spanning technologies, sample types, and variant classes.

Scientific Insight

DeepSomatic converts paired tumor–normal reads into tensor “images” that feed a convolutional neural network capable of distinguishing somatic, germline, and reference variants. The model outperformed leading tools such as Strelka2 and ClairS across variant types and variant allele frequencies, and it maintained accuracy across multiple sequencing chemistries. Beyond its raw performance, the CASTLE dataset fills a major gap in the field: creating a real benchmark for long-read somatic variant detection where none previously existed.

Scientific Rigor Note

Like many GenAI systems, DeepSomatic may fall pray to non-obvious data leakage, and would benefit from more explainability. Some of its evaluation data overlap with the model’s own training inputs, raising the risk of circular benchmarking bias, and the study offers little insight into why the network makes its calls.

AI ∩ Bio

Illustration of scientific paper with the title "AI ∩ Bio: Reading the Revolution"

Leadership & Mentorship Reflection

Building trustworthy AI in medicine requires independent data, transparent reasoning, and humility about limitations that are baked into how these models work.

For early-career scientists, this paper is a case study in responsible ambition: innovate boldly, share your data openly, and interrogate your own benchmarks. AI or not, progress comes from rigorous, open science that understands its own limitations.

Illustration of a desk with a figure from a recent paper about Rentosertib

Hype, Hurdles, and Hepatotoxicity: A Bold Step for AI-Designed Drugs, But Still Miles to Go

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On August 2, 2025

In AI ∩ Bio

This week’s AI ∩ Bio: Reading the Revolution paper spotlights the first randomized Phase 2a trial of a drug discovered using generative AI.

Summary and Context

The trial investigates rentosertib, a small-molecule inhibitor of TNIK (Traf2- and Nck-interacting kinase), developed end-to-end on Insilico’s AI platform. Notably, the system identified both the target and compound de novo—compressing two traditionally distinct phases of drug discovery into a unified AI-led workflow.

The study enrolled patients with idiopathic pulmonary fibrosis (IPF)—a progressive, fatal lung disease with no cure. This trial sets a bold precedent: Can a molecule born of algorithms succeed in one of medicine’s most unforgiving indications?

Scientific Insights & Critical Observations

What’s Promising

  • AI-led discovery pipeline: TNIK identified and rentosertib designed via Insilico’s generative model.
  • Accelerated early development: Reached Phase 2a enrollment in under 30 months from target nomination.
  • Biomarker engagement: Downregulation of fibrosis-linked proteins (e.g., COL1A1, MMP10, FAP) with exploratory ties to lung function improvement.

What’s Concerning

  • Overinterpretation of early signals: The +98.4 mL FVC gain in the 60 mg arm was non-significant, with one-third of patients missing spirometry at Week 12.
  • Safety flags: Hepatotoxicity led to multiple discontinuations, particularly in patients co-treated with nintedanib—a standard-of-care antifibrotic—with an unexplored drug–drug interaction risk.
  • Pharmacokinetics: High interpatient variability and disproportionate exposure increases raise questions about dose optimization.
  • Generalizability: All 71 patients were Asian and enrolled in China, limiting extrapolation across diverse IPF populations.
  • Narrative overreach: The AI platform is being validated rhetorically more than pharmacologically—so far.

A Useful Comparison

To contextualize this AI-driven advance, consider nintedanib—a first-in-class oral tyrosine kinase inhibitor targeting PDGFR, FGFR, and VEGFR. It was the first drug to significantly slow lung function decline in IPF in large, well-controlled trials, establishing antifibrotic therapy as a treatment paradigm.

FeatureRentosertib (AI, Phase 2a, 2025)Nintedanib (Traditional, Phase 2 results in 2011- below; Approved c2014)
Discovery speed~30 months to Phase 2a~10+ years
Target noveltyFirst-in-class (TNIK)Known pro-fibrotic RTKs
Phase II trial size71 patients432 patients
Efficacy signalWeak, non-significantStatistically significant
Safety profileHepatotoxicity, DDI with nintedanibPredictable, tolerable
Trial population diversitySingle-country, homogeneous cohortGlobal, multi-ethnic
Biomarker integrationProteomics exploredFocused on validated clinical endpoints

AI ∩ Bio

Illustration of scientific paper with the title "AI ∩ Bio: Reading the Revolution"

Takeaway for Early-Career Scientists

This is a milestone in ambition, not yet in outcome.
Rentosertib shows what generative AI can compress in timeline—but not what it can yet deliver in therapeutic benefit. The real test isn’t how a drug is designed, but how it performs in humans.

So ask yourself: If this same molecule came from a traditional screen, would we be touting it—or shelving it?

Generative AI may change how we create drugs, but it doesn’t change what success looks like.

Illustration of a human foreman in a construction helpmet co-editing a blog article from this website with a stylized, green robot

Scaling Thought, Not Just Content: Field Notes from the Messy Middle of Co-Writing with ChatGPT

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On April 13, 2025

In Uncategorized

When I started the revised 2025 version of the Scaling Well series, I didn’t want to simply write ABOUT AI and its impact to scaling companies in 2025. I wanted to experience actively collaborating with it on a project that is deeply meaningful to me.

This piece is my reflection of that experience. While co-writing the previous two articles, I made notes about the process and want to share a behind-the-scenes look at how I think this experiment went. This isn’t meant to be a hype story, nor is it a cautionary tale. It’s field notes from the messy middle of using a new set of tools in hopes of discovering its strengths and weaknesses.

If you’re a curious leader, a stretched-thin builder, or someone who keeps hearing “AI can help” and thinking, “Help with what, exactly?”—you’re in the right place.

In the sections that follow, I’ll walk you through the prompts I used, the questions I asked, the traps I hit, and the patterns that emerged.

The intention is not to provide a plug-and-play workflow* though. In sharing my experiences with structure, friction, and the occasional detour into AI-generated nonsense, I hope to help others scale insight, not just output.

What This Is Really About

Despite all the buzz about AI multiplying people’s output, that wasn’t my primary goal as a writer. Or maybe that was the dream initially, but what became abundantly clear through the process is that one key benefit of working with AI was that it forced me to make my thinking clearer in order to co-produce something useful, without losing my voice.

On its own, ChatGPT didn’t do the work for me. The work became meaningful when I treated ChatGPT like a fast-learning collaborator— prompting it to ask me questions for reflection, or challenging it to critique my logic or clarity.

That’s not to say that there wasn’t a boost to my speed— working with AI definitely helped get me unstuck from staring at a blank page faster than ever before. But the bigger payoffs were the added clarity, repeatability, and a surprising amount of fun.

These payoffs really became apparent once I stopped expecting AI to generate ready-made content and started using it to help me distill what I actually wanted to say.

The breakthrough wasn’t what AI wrote—it was getting past the tyranny of the blank page and getting clear about what I wanted to say.

The Process (Messy, Real, Repeatable)

Approaching this collaboration with ChatGPT as an exercise in “getting AI to write an article for me” did not work.

Here’s what did.

1. Align with yourself and your AI collaborator first

One thing that surprised me in learning to use ChatGPT more effectively is how much of a Rorshark test it can be. If you don’t know what you want to say, you’re very likely to end up with a lot of ‘well written’ fluff. In the ‘olden days’ of writing without AI, clarity creation was part of the outlining and drafting process. Having ChatGPT as a super fast writing partner can sometimes shortcut the crucial step of figuring out what point you’re even trying to make. So, what seems to work better is to start with getting to clarity… with your genAI collaborator.

“Pasted below are my initial thoughts on scaling companies, please ask me 3 multiple choice questions one at a time to make sure we’re aligned.”

That clarity up front mattered more than I expected.

Further along in the writing process, ChatGPT can continue to add clarity:

“Please first ingest the below pasted text, then ask me 5 multiple choice questions one at a time to help us align on how to get to 10-fold improvement across the above metrics (and please suggest one additional metric to improve).”

The responses could be quite revealing. When answering these questions, I realized that half the time I didn’t know what I had really intended in my initial text until had to formulate these answers. It was like mirror to my own ideas, showing me angles I’d otherwise glossed over.

2. Don’t neglect structure and cohesion

“Find 3 places where transitions could be tighter or a seed could be planted for a later section. Give me 3 options for each.”

The results didn’t always land, but they exposed choices I hadn’t seen yet—and that very valuable.

Add something about chunking work – to keep the ai focused and to keep you focused too.

3. Calibrate tone on the fly

Without additional work, AI tends to gravitate towards the middle in its output. If you don’t mind generic mediocre writing, maybe that’s okay. But I wanted writing that sounded like me. Luckily, tone can be moderated fairly easily. In doing so, it provides another opportunity to review what the goals of your writing are, and if the text is achieving those goals.

“Let’s align the tone with the overall tone we are going for in the article.” (Spoiler alert: you’re going to need to define what that tone is)

“Section 1 feels a little fluffy and too long. Ask me 3 multiple choice questions to help us align on what we’re trying to say in section 1.”

Here it can also help if you’ve done your own writing in the past— you can input examples of past writing and ask ChatGPT to create a one-page style guide defining your writing style, and then use that to continually refocus the output. You can also have verbal interviews with ChatGPT and then ask it to ‘write like I talk,’ which is sometimes helpful in breaking free from the default indiscriminate bot language.

While shifting tone might seem like a photo filter at first, these adjustments to tone proved to be more than just editing tricks. They were valuable thinking tools as well.

4. Check your sources

At this point, you’ve probably heard that AI is prone to hallucinate details in order to satisfy your request. As it turns out, that’s not something you can simply ask it to improve upon, although there are ways to tune the model for accuracy versus creativity, and some LLMs are better at one thing than the other. At the end of the day, it’s your name going on the article, so you should probably just buckle up and do the homework of making sure the references are correct and on point.

“Flag anything not backed up by a real source. Let’s verify links and decide together how to fix anything questionable.”
 Spoiler: most links were still broken. I fixed them (okay, actually my husband did that part).

5. Save what’s working

In working through this process for multiple pieces, one of the products I aimed to create was the repeatable process itself. This was something the AI proved helpful with, consolidating longer conversation threads into something closer to a reusable recipe.

“Review this whole conversation. Create a repeatable process others could follow.”

That became the scaffold I’m still using now.

And when a section still felt fuzzy? I read it aloud to my husband. The AI helped sharpen the thinking and do the initial drafting; he helped catch what still didn’t land.

What Surprised Me (and Might Surprise You)

My biggest surprise: I didn’t expect to enjoy the process quite so much. I thought I’d be sifting through AI garbage and editing it into something usable.

Instead, I found myself getting sharper—because I had to. The tool was fast, literal, and indifferent to context; if I wasn’t clear, it absolutely wasn’t. And that kind of brainstorming was really fun, not too different from a good brainstorm with a fellow human.

That was the first surprise.

The second was how much more I wanted to engage with the work once I had structure for working with ChatGPT in place. I wasn’t stuck at the blank page. I had a system of prompts and checkpoints that gave me momentum. Not faster, exactly—but steadier. And significantly less painful to start.

The last surprise: it really felt like collaboration.

When I gave it real direction—alignment prompts, tone corrections, specific feedback—it became a decent thought partner. Not insightful on its own, but responsive in a way that made my thinking more visible. It didn’t generate depth; it paved the way for me to get there faster.

Final Thoughts

From my experience co-writing with ChatGPT, generative AI isn’t necessarily a shortcut. You certainly can’t skip the thinking part of writing. What it provides instead is scaffolding—something to push against, iterate with, and structure the parts of thinking that usually get stuck in your head.

While it offered language, I honed in on shaping meaning. And the act of shaping—deciding what to keep, what to cut, what to say—was where the clarity came in.

The output was better (*more on that from my husband). The thinking was clearer. And the work of writing felt more engaging and joyful than it has in a while.

Not because the tool was smart—because the process was.

Appendix: High-Impact Prompt Set for Thoughtful Co-Writing with LLMs

Clarify the Thinking First

Use these to align on purpose, sharpen ideas, and build momentum.

  1. “Ingest the following text. Ask 3 multiple choice questions—one at a time—to clarify intent, audience, and core argument.”
    Purpose: Forces you to name your aim before drafting begins.
  2. “Based on the ideas below, ask 5 multiple choice questions—one at a time—to explore how we might 10x the impact. Then suggest one metric I may be overlooking.”
    Purpose: Pushes past surface iteration into leverage-based thinking.
  3. “Review this full exchange. Summarize the workflow we followed. Write it as a 5-step process someone else could reuse.”
    Purpose: Codifies emergent process into shareable frameworks.

Lock in Voice and Tone

Use these to calibrate writing style across tools or collaborators.

  1. “Ingest this sample of my writing. Create a 1-page voice guide: tone, structure, sentence length, common patterns, and language quirks.”
    Purpose: Teaches the model your style explicitly—reusable across tools.
  2. “Match the tone of this section to my writing style guide. Highlight any areas where the tone drifts or feels generic.”
    Purpose: Enforces consistency without losing precision.
  3. “Here’s how I explain this verbally. Mirror the rhythm and phrasing—make it sound like a well-edited transcript of me.”
    Purpose: Removes formality and aligns with natural delivery.

Deepen Structure, Reduce Bloat

Use these to improve flow, argument strength, and readability.

  1. “Identify 3 transition points where flow weakens or ideas shift too abruptly. Suggest 2 alternate transitions or reframing options for each.”
    Purpose: Strengthens internal scaffolding, especially across sections.
  2. “Analyze this section. What’s the underlying assumption? Is it clearly supported? Suggest one way to reinforce or challenge it.”
    Purpose: Prevents unexamined logic from slipping through.
  3. “Review this section. What’s actually doing the work? What can be cut without losing meaning?”
    Purpose: Helps trim filler while preserving depth.

Expand Insight, Avoid Shallow Thinking

Use these when your piece feels too safe, too obvious, or not sharp enough.

  1. “Take the role of a skeptical executive. What would they question here? Suggest 2 ways to preempt or clarify the concern.”
    Purpose: Builds in resistance testing before real-world exposure.
  2. “List 3 ways this idea could backfire in real use. What failure modes or misinterpretations should we proactively address?”
    Purpose: Adds robustness and resilience to big ideas.
  3. “What part of this actually feels new or non-obvious? Highlight it. What parts sound like filler or expected takes?”
    Purpose: Prioritizes originality and edge—especially in thought leadership.

Fact-Check and Refine Responsibly

Use these when citations, claims, or integrity are at stake.

  1. “Review the draft below. Flag any factual claims or data points that lack a source. Suggest how to verify or reframe.”
    Purpose: Reduces hallucination traps, keeps credibility intact.
  2. “List every link, citation, or claim in this piece. Verify each one. Highlight any that are outdated or broken.”
    Purpose: Turns verification into an explicit task—not an afterthought. You should still verify the results

Meta-Prompt for Debugging & Reflection

  1.  “Reflect this draft back to me in plain English. What’s clear, what’s muddled, and what’s trying too hard?”
    Purpose: Works like a mirror. Best used before a big share-out.
Image of a construction manager looking at an oversized tablet computer to select from one of three robots standing in front of them. In the distance, a city is under construction

Scaling Well in 2025: AI Tools That Actually Help

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On April 1, 2025

In Scaling Well

Scaling Smarter Starts with Designing for Resilience

The most successful use of AI in 2025 won’t necessarily come from technological breakthroughs. It will come from thoughtful rethinking how their organizations operate, reshaping how time, knowledge, and decisions flow across our organizations.

AI tools should support and augment the pillars of scaling that I’ve written about previously. They should free up capacity for deep work, enable culture that scales, and support infrastructure that can carry the weight of change.

This means the companies scaling well in 2025 aren’t asking, “How do we use AI?” They’re asking, “How do we use AI to make our company better, faster, and more resilient?”

In this piece, we explore how AI can support each of those pillars. Each section includes real-world tools, examples, and decision cues to help leaders think more clearly about what to pilot and how to scale well.

Let’s dive in.

Designing for Slack & Innovation

“Slack isn’t waste. It’s where creativity, resilience, and serendipity live.”

In the original article, we framed slack not as inefficiency, but as infrastructure for innovation—a concept grounded in queuing theory and visible in everything from engineering velocity to decision-making latency. In 2025, AI makes it possible to unlock “smart slack”: creating time, reducing bottlenecks, and diffusing knowledge—all without adding headcount.

Below are three AI tools that enable this.

Tool 1: Glean – Contextual Search to Reduce Bottlenecks

Problem: Teams lose time recreating knowledge that already exists—or waiting on the one person who has the answer.

Tool: Glean indexes all your internal systems (Docs, Slack, Jira, Drive, Confluence, etc.) and uses AI to deliver relevant, secure search results contextualized to the user.

Impact: Reduces time spent answering repeat questions, reduces key-person dependency, and reclaims hours per week for deep work.

Real Example: Confluent Case Study – Glean

Piloting Tip: Start with one department and track internal support volume, time-to-answer, and repeated questions. Pair with light documentation practices to amplify results.

How to Know if This Belongs on Your Roadmap

  • You’re hearing complaints about repetitive internal questions or knowledge buried in Slack
  • Onboarding timelines are dragging because institutional knowledge isn’t searchable
  • A few individuals are serving as knowledge routers across functions
Glean is worth piloting if you want to reduce dependency on key people and unlock capacity without increasing headcount. Ask your department leads how much time is spent answering questions that should already be documented.

Tool 2: Notion AI – Workflow Co-Pilot to Free Cognitive Slack

Problem: Knowledge workers are overloaded with routine documentation, synthesis, and follow-up work.

Tool: Notion AI automates meeting summaries, drafts project plans, and generates next steps across team workspaces.

Impact: Frees up “cognitive slack” for strategic thinking, synthesis, and creative problem-solving.

Real Example: Figma Case Study – Notion

Piloting Tip: Begin with retros, meeting notes, or OKR drafts. Then explore more structured workflows like customer issue triage or roadmap generation.

How to Know if This Belongs on Your Roadmap

  • Your OKR cycles feel slow or painful to prep
  • Meeting notes, follow-ups, and documentation tasks are draining your highest-leverage people
  • Strategic initiatives stall because everyone’s too busy with tactical maintenance
Notion AI is worth funding if your teams need more “thinking time” but can’t find it. Look for small pilots where cognitive load is high but output is low-value admin work.

Tool 3: Stack Overflow for Teams – Capture and Share Tribal Knowledge

Problem: Tribal knowledge lives in people’s heads or buried in Slack threads—causing bottlenecks and slow onboarding.

Tool: Stack Overflow for Teams creates a company-specific Q&A hub. AI enhances search, recommends related questions, and flags potential duplicates to reduce noise and improve speed-to-answer.

Impact: Accelerates onboarding, reduces repetitive internal questions, and builds resilience when key people leave.

Real Example: Box Case Study – Stack Overflow for Teams

Piloting Tip: Seed it with your most-asked questions. Create a recognition system for contributors. Link key answers directly from onboarding flows.

How to Know if This Belongs on Your Roadmap

  • You’re hearing “I didn’t know that existed” across teams
  • Ramping new hires feels fragile and overly dependent on 1:1 support
  • The same technical questions are being asked—and answered—on repeat
This tool is worth considering if your resilience is too dependent on institutional memory. Ask your leads how much time their teams spend re-answering solved problems.

Operationalizing Slack

Strategic slack isn’t found. It’s designed. The above tools create visibility into where time, attention, and knowledge are leaking—but the next step is turning that visibility into action.

  • Map your bottlenecks: Where does work pile up or depend on a few people? Use these tools to surface hotspots.
  • Reclaim 10–20% capacity: Track time saved, then explicitly reinvest it into innovation sprints, mentorship, or deep work time.
  • Start with one team: Pilot in a high-friction area with motivated adopters. Share wins before scaling org-wide.
  • Don’t over-automate: Tools should augment, not replace, human judgment. Start simple, layer complexity later.

Reference: For deeper guidance on build vs. buy vs. fine-tune, see Appendix A.

Once you’ve carved out slack, the next challenge is ensuring your teams use it well—without needing top-down oversight every time. That starts with culture.

Scaling Culture & Leadership

“Culture is how decisions get made when no one is watching.”

If slack gives teams processing power, then culture is the operating system that determines how that power gets used. It determines how teams behave under pressure, how decisions get made across time zones, and how mission translates into action when leaders aren’t in the room.

In 2025, generative AI unlocks new ways to scale cultural alignment, leadership development, and human connection—without requiring armies of coaches or L&D staff. Below are three tools that make culture more scalable, distributed, and actionable.

Tool 1: Humu – Culture-Driven Behavioral Nudges at Scale

Problem: Culture often degrades in the middle. Execs may model values, but middle managers struggle to reinforce them day-to-day.

Tool: Humu delivers science-backed, personalized nudges to encourage behaviors aligned with company goals.

Impact: Reinforces key behaviors across roles, time zones, and contexts—without requiring more oversight.

Real Example: Humu Case Studies

Piloting Tip: Start with one leadership behavior to reinforce—like recognition, inclusive meetings, or timely feedback. Layer nudges on top of any existing training or communications push.

How to Know if This Belongs on Your Roadmap

  • You’re struggling to turn stated values into visible day-to-day behaviors
  • Cultural consistency breaks down between levels or locations
  • Your engagement surveys surface issues like low feedback or recognition
Humu can bridge the gap between intention and action. Prioritize it if you’re investing in manager enablement or aiming to reinforce cultural behaviors without scaling your HR team.

Tool 2: Pinnacle AI – Custom Coaching & Leadership Simulations

Problem: Leadership coaching rarely scales beyond the executive level.

Tool: Pinnacle AI enables companies to create custom GPT-based avatars that simulate high-stakes conversations, coach leaders in real time, and reinforce values.

Impact: Expands coaching access, builds soft skills through practice, and embeds culture into day-to-day behavior.

Real Example: Pinnacle AI Overview

Piloting Tip: Start with a common friction point (e.g., giving tough feedback). Use a leadership avatar to simulate the conversation, then track usage and impact.

How to Know if This Belongs on Your Roadmap

  • You need to scale coaching or practice-based leadership development without adding headcount
  • Managers are hesitant or underprepared for hard conversations
  • Soft skill gaps are impacting team dynamics or delivery
Pinnacle is worth trying if you’re investing in middle manager capability-building or want to embed feedback culture through safe, scalable practice.

Tool 3: Together Platform – Peer Mentorship with AI-Enhanced Matching

Problem: Cultural transmission often relies on informal relationships that don’t scale.

Tool: Together uses AI to match employees into mentorship relationships based on goals, skills, and values.

Impact: Reinforces culture through peer connection. Supports belonging and cross-functional leadership.

Real Example: Together Case Studies

Piloting Tip: Start with onboarding cohorts or ERGs. Use AI-generated prompts to anchor discussions in real cultural moments and shared learning goals.

How to Know if This Belongs on Your Roadmap

  • You’re looking to scale belonging and connection in a hybrid or distributed org
  • Current mentorship programs are ad hoc or unsustainable
  • Peer learning feels like a missed opportunity across levels or silos
Together is a strong fit if you’re building internal talent pipelines or trying to support new hires and ERGs without overloading HR.

Cultural Playbook: Scaling Values, Not Just Policies

  • Pick your pressure points: Identify where cultural gaps show up—new manager transitions, post-reorg teams, or moments of change. Deploy nudges (Humu), simulations (Pinnacle), or mentorship (Together) at those friction points.
  • Time it right: Behavior change sticks when it’s timely. Align tools with onboarding, performance reviews, or team formation.
  • Reinforce with data: These tools generate signals. Use them to monitor alignment, not just activity.
  • Connect the dots: Use Together to reinforce Pinnacle simulations. Follow Humu nudges with live discussion prompts.

Of course, with every new AI tool comes another integration, another dashboard, another cognitive switch. It’s worth asking: do we really need three tools, or can one flexible platform get us 80% of the way there? This isn’t just a user experience issue—it’s an infrastructure one. And that’s where we go next.

The Case for Fewer, Flexible Tools

Leaders face a strategic question: should you prioritize tools that do one thing exceptionally well, or ones that solve multiple problems “well enough”?

Some considerations:

  • If your org is resource-constrained, favor tools that integrate natively with your existing stack (like Pinnacle AI into Slack) and solve for multiple moments (onboarding, feedback, DEI).
  • If your org is scaling fast with complex silos, best-in-class tools may still make sense—as long as IT can support them.

Several tools featured here offer multi-purpose value: Pinnacle AI can support onboarding, leadership development, and cultural alignment. Together can double as an onboarding accelerator and a peer learning platform. We’ll explore how to evaluate tools across breadth vs. depth in Appendix B.

The Hardest Part Isn’t the Tool. It’s the Terrain.

Cross-functional adoption is where AI efforts live or die.

Even the best tool won’t gain traction if:

  • IT says they’re already at capacity
  • Legal worries about risk
  • Enablement teams are juggling five other rollouts
  • Or end-users don’t trust the tool (or the motive)

If this sounds familiar, try this instead:

  • Start where there’s pull. Pilot with teams who are already stretched and asking for help.
  • Solve their problem, not yours. Frame the tool around a pain they already name.
  • Invite, don’t mandate. Use early adopters to tell the story, not top-down pressure.
  • Build in feedback. Create fast feedback loops between teams and tool sponsors.

Adoption isn’t just change management—it’s coalition-building. And how you roll out the first tool will shape whether people lean in or tune out when the next one comes.

Infrastructure & Enablement: Lightening the Load, Quietly Powering Scale

“You can’t scale what you can’t support.”

Slack and culture shape what’s visible—but infrastructure is the foundation. It decides how much complexity your organization can carry, and how well it holds up under change. And yet, infrastructure teams—especially IT, platform, and internal tooling—are often the most overburdened and underappreciated.

Generative AI won’t solve infrastructure debt overnight. But it can reduce manual load, increase visibility, and turn reactive work into proactive systems management. Below are a few tools helping infrastructure teams do more with less—and sometimes even breathe.

Tool 1: Moveworks – Conversational AI for IT Support

Problem: IT teams are flooded with repetitive tickets, onboarding requests, and access issues.

Tool: Moveworks resolves common IT support requests instantly through Slack, Teams, or email.

Impact: Reduces ticket volume, accelerates onboarding, and frees up IT capacity for more strategic work.

Real Example: Moveworks Case Studies

Piloting Tip: Start with high-volume, low-complexity tickets. Track resolution speed, team bandwidth, and internal satisfaction.

How to Know if This Belongs on Your Roadmap

  • Your IT support teams are drowning in high-volume, low-complexity tickets
  • Employees complain about slow or inconsistent onboarding and access
  • You want to improve internal experience without increasing IT headcount
Moveworks is worth piloting if you want to unlock IT capacity for infrastructure and security work. Ask: where could automation improve internal credibility?

Tool 2: Cortex – Internal Service Catalogs & Engineering Visibility

Problem: Engineering teams lose time navigating unclear ownership and shadow systems.

Tool: Cortex offers internal scorecards, visibility tools, and service catalogs that clarify what’s running—and who’s responsible.

Impact: Supports platform health, reduces hero culture, and increases resilience.

Real Example: Cortex Customers

Piloting Tip: Map key services with unclear ownership. Use Cortex to build scorecards and reinforce accountability.

How to Know if This Belongs on Your Roadmap

  • You’re seeing repeated outages or friction due to unclear service ownership
  • Engineering productivity depends on a few heroic individuals
  • Your team lacks visibility into internal reliability or SLA gaps
Cortex is a fit if you’re trying to mature your platform engineering or prepare for scale. Ask: who owns what—and do they know it?

Infrastructure Insight: Design for Longevity, Not Just Load

“If you’re thinking about infrastructure only when it fails, you’re already behind.”

  • Engage infrastructure early when evaluating AI tools—especially those touching data or permissions.
  • Give infrastructure leaders a seat at the table in AI strategy and tooling decisions.
  • Track infrastructure health visibly—and treat internal platforms as core products.
  • Recognize and reward the teams that make scalable systems possible.

Conclusion: Scaling Well Means Leading Differently

The leaders pulling ahead aren’t just adopting tools—they’re rethinking how their organizations operate. They’re treating slack as strategy, culture as infrastructure, and infrastructure as leverage.

This transformation isn’t really about the tech—it’s a business transformation that demands real leadership and coordination.As KPMG’s Vice Chair of Artificial Intelligence (AI) and Digital Innovation, Steve Chase, put it, we’ve entered a phase where companies need someone to lead the movement—someone who can cross silos, hold tension, and help the organization build real muscle memory around transformation.

Leading the movement to implement AI successfully means starting with intention—with intelligent pilots, cross-functional alignment, and a clear view of what you’re solving for.

The tools are here.

The stakes are real.

And how you lead this transition will define how well your company scales… or stalls.

Appendix A: Choosing the Right Tool – A Strategic Framework

Before choosing a tool—or even beginning a pilot—executives should step back and ask: What kind of leverage are we trying to create, and what constraints must it navigate?

This framework provides a higher-level lens to help you prioritize the right kind of AI investment:

1. Clarify the Friction

What are you actually trying to solve?

  • Time bottlenecks: Teams are stuck doing repetitive work or can’t move fast enough
  • Cultural inconsistency: Values and behaviors vary wildly across functions or levels
  • Infrastructure drag: Tooling, access, or service ownership are slowing everything down

2. Map Organizational Readiness

How prepared is your org to absorb a new AI tool?

  • Technical readiness: Do you have IT capacity and integration capability?
  • Behavioral readiness: Will people use it—or resist it?
  • Strategic alignment: Does this tool support a broader goal you already care about?

3. Decide the Mode of Leverage

What kind of support are you trying to amplify?

  • Automate: Remove low-leverage tasks (e.g. ticket triage, note-taking)
  • Augment: Enhance high-leverage talent (e.g. coaching, mentoring, cross-functional insight)
  • Diffuse: Spread knowledge and behavior (e.g. onboarding, Q&A, nudges)

4. Start Where You Have Urgency + Ownership

Don’t start where the problem is loudest—start where a function wants the help.

  • Look for “pull,” not “push” opportunities
  • Pilot in functions already experimenting or stretched thin but open-minded

This framework helps you move from “interesting tool” to “clear decision.”

Example: Applying the Framework by Company Size

Here’s how this framework might guide tool selection across company stages, using high-leverage, multi-purpose tools that can solve for multiple frictions:

Small Company (≤50 employees)

  • Friction: Time bottlenecks, no L&D team, documentation debt
  • Readiness: Agile culture, limited IT bandwidth
  • Mode of leverage: Automate and diffuse
  • Tool Match: Notion AI for streamlining workflow + basic coaching content; Together for onboarding + connection-building

Medium Company (50–300 employees)

  • Friction: Scaling teams, mixed leadership capability, mounting ticket load on IT
  • Readiness: Willing early adopters in some functions
  • Mode of leverage: Diffuse and augment
  • Tool Match: Pinnacle AI for scalable leadership coaching + feedback support; Moveworks to relieve IT and improve employee experience

Large Company (300+ employees or multiple sites)

  • Friction: Cultural inconsistency, distributed knowledge, infrastructure complexity
  • Readiness: Dedicated IT, HR, and Enablement functions—but political complexity
  • Mode of leverage: Systematize and augment
  • Tool Match: Pinnacle AI for global leadership training + cultural reinforcement; Notion AI to reduce admin burden; Moveworks for automated support across functions

→ Use this framework not to pick “the best tool,” but to clarify what kind of leverage your org actually needs.

Appendix B: Questions to Evaluate AI Tools Before You Commit

Before piloting or purchasing an AI tool, ask:

Strategic Fit

  • What exact friction is this solving?
  • Will it augment high-leverage work—or just automate noise?

Integration & Infrastructure

  • Does it work with our current stack (Slack, Teams, Drive, etc.)?
  • How much IT lift is required?
  • Who owns implementation?

Cultural & Change Readiness

  • Will people trust and use this tool?
  • Does this require behavior change—and do we have a plan for that?

Measurement & Momentum

  • What leading indicators will show this is working?
  • What story will we tell if we want to scale it org-wide?

Example: Evaluating Two Tools in Practice

Let’s say you’re weighing Notion AI vs. Pinnacle AI—both multi-use tools with clear executive appeal, but very different strengths.

Scenario 1: Notion AI

  • Strategic Fit: Your teams are overloaded with repeat documentation and task spillover.
  • Integration: Already using Notion lightly; integration is easy and user-friendly.
  • Cultural Fit: Teams are open to automation, especially if it feels like a personal productivity win.
  • Measurement: Track adoption in meetings and goal-setting workflows; estimate time saved weekly.
Great pilot if your goal is reclaiming capacity and improving org hygiene. Low-risk, broad benefit.

Scenario 2: Pinnacle AI

  • Strategic Fit: You’re struggling to scale coaching, leadership readiness, and feedback culture.
  • Integration: Can be deployed via Slack or Teams, with configuration support available.
  • Cultural Fit: Managers want help but don’t have time for traditional development.
  • Measurement: Track usage of leadership avatars, user satisfaction, and reported confidence in high-stakes conversations.
Worth piloting if you’re focused on middle-manager enablement or cultural consistency. Medium complexity, but potentially high cultural leverage.

These tools don’t compete—they complement. But knowing what type of leverage you need first is key. Use these questions to sharpen internal alignment, clarify trade-offs, and set realistic success metrics before making big bets.

Image of a construction manager looking at an oversized tablet computer with a bar graph indicating growth, standing in front of a city under construction. Depicted in a painted collage.

Scaling Smarter in 2025: Resilience, Adaptability, and Intelligent Growth

By

On March 22, 2025

In Scaling Well

Back in 2022, I wrote an article on how to scale companies and teams well.

At the time, I had just come out of a role at an ambitious, high growth company at the apex of the biotech market. This experience weighed heavily on my thoughts in that first article, which focused on how companies can leverage insights from cities to scale better.

Here’s the main gist from that article: the research would indicate that the vast majority of companies grow sublinearly—meaning that as headcount increases, per-employee economic output declines. Cities, on the other hand, grow superlinearly, becoming more innovative and efficient (in terms of economic output) as they expand. Some of the key features of growing cities is that they foster continuous idea flow, optimize judicious infrastructure, and embrace decentralized decision-making. So the question is: can organizations adopt these principles to unlock superlinear growth? (Geoffry West, Scale)

It’s now 2025, and things look very different. For one, ‘growth at all costs’ has been replaced by ‘get to profitability.’ Secondly, generative AI has provided a whole new set of tools for scaling businesses without necessarily adding new headcount or employing human contractors.

With these new forces shaping our collective reality, I want to revisit some of the main themes from the original article, and provide some new perspectives on what it means to scale well. And because genAI has incredible potential in scaling workforces and companies, I decided I would collaborate with ChatGPT in this article, so what you are reading is a combination of me and ChatGPT (I’ll come back to how this experience was in my final post on this topic— stay tuned). Let’s dive in.

1. Designing for Slack: Innovation and Bottleneck Prevention

One of the key frameworks discussed in the original article is that scaling well requires deliberate slack in resourcing. Some level of slack and redundancy should not be seen as inefficiency, but as a strategic lever for innovation and resilience.

The scientific backbone behind this idea comes from queuing theory. Queuing theory tells us that as a system nears 100% utilization, inefficiencies spike exponentially. In cities you see this phenomenon during rush hour traffic, when roads are at capacity. In companies, you see this phenomenon in supply chains, network traffic, decision-making, and engineering velocity (Kashef, 2019). Organizations that operate at full capacity leave no room for iteration, problem-solving, or market shifts.

Whereas in 2022, designing for slack mainly meant allowing for some strategic redundancy in human resources, AI now offers new ways to create smart slack, not just by freeing up human capacity, but also by helping teams preempt bottlenecks before they emerge.

For example, modern AI-driven knowledge systems (like Notion AI or Stack Overflow for Teams) reduce key-person dependencies, ensuring critical expertise is documented, searchable, and shareable.

The other possibility that AI opens up is simply reduced time spent on routine tasks, thereby opening up additional capacity in your existing workforce. In other words, training your workforce on how to effectively (and safely) use AI tools can help companies ‘find time.’ Instead of loading up the team with new work, allowing this ‘found’ time to be used for structured slack can pay dividends. Imagine if every knowledge worker in your company suddenly had 20% more free time. Could this slack be used like Google’s 20% time, which produced Gmail and Google Maps (Tran, 2017) to produce outsized returns on innovation?

2. Investing in Infrastructure and Other Shared Resources Without Slowing Down

One of the biggest failure points in scaling is making the wrong decisions on what functions to centralize vs distribute, particularly in moving from a one product company to having a portfolio of multiple products. Too much centralization slows decision-making and leads to conflict as multiple projects try to draw on the same resources. Too much decentralization leads to inefficiency, duplication, and chaos.

Cities scale by balancing shared infrastructure with distributed autonomy— local businesses tap into municipal sewer lines and electric grids, for instance, and leverage shared city parking lots. Companies should think hard about which functions they want to be their ‘shared infrastructure,’ and cultivate those resources thoughtfully. At the same time, most cities don’t strictly mandate the number of Burmese restaurants or whether yet another rug store can move into a vacant storefront, nor do cities micromanage local business beyond compliance to city ordinances. Analogies for these ideas exist in corporations as well, and providing local autonomy with reasonable guardrails can enable companies to move fast without being overly centralized.

Based on how cities approach infrastructure, in 2022 my guidance was the following: (1) Put highly interdependent teams together, since organizing closely linked functions into clusters reduces cross-team friction. (2) Invest early in infrastructure teams. Underfunding internal platforms, DevOps, and enablement functions will stall scaling efforts. Just as you can’t expect new housing developments to be successful in areas with no power or water (at least in the US), you can’t expect employees to be productive if they have to spend half their energy fighting ineffective infrastructure. (3) Adopt a ‘Team of Teams’ approach by organizing into dynamic, goal-oriented teams and decentralizing decision-making to shorten communication paths within the organization.

In 2025, all of these approaches remain highly relevant. If anything, investing in infrastructure teams that enable smart AI usage has become even more critical to scale well, as unlocking AI tools is a powerful lever for improving efficiency across the organization.

These generative AI tools can then significantly enhance a ‘Team of Teams’ approach. For instance, enterprise-level AI-driven platforms have begun to emerge that leverage conversational AI to streamline internal communication, automate employee support, and provide real-time analytics (example: Moveworks).

3. Scaling Culture Through Intentional Culture Creation

Effective use of slack and structure is foundational, but truly scalable growth requires individuals to independently advance the mission, openly share information, and foster collective intelligence without constant oversight, and this comes from the culture and norms you create. A cohesive culture—essentially “how things get done around here”—is shaped by shared knowledge, expectations, and behaviors, thereby reducing the need for top-down control (Joly, 2022; Clayton, 2019).

Unlike cities, where culture evolves organically, organizations must intentionally shape cultural norms. Leaders must articulate clear behavioral expectations, reward aligned behaviors, and visibly model cultural values.

Three key perspectives from 2022 for building the cultural foundations for scaling well included: (1) Focus on new leadership for maximal cultural impact, because leader behavior is a key determinant of overall organizational culture (ed. Constable, 2023). (2) Onboard to build belonging. Culture creates the foundation for belonging, which in turn leads to better employee engagement and lower attrition. (3) Scale belonging through peer-led groups. Allow peer networks to reinforce cultural norms as well as fostering connection and belonging.

In 2025, these perspectives continue to be crucial. By leveraging generative AI tools, companies now have powerful tools for providing real-time feedback and personalized coaching at scale without hiring an army of coaches or HR business partners. Companies can fairly easily create/train their own custom GPT tools to be specific for their culture and values, or partner with any number of emerging companies on the task of integrating AI tools across the enterprise communication suite (Teams, Slack, WorkDay, etc), so that coaching for culture can occur in the flow of work and has a larger specialized training dataset (example: Pinnacle AI).

Here are few additional specific use cases I have been noodling on over the past few months as I myself have started diving in to my own AI learning journey:

  1. Create a customized leadership avatar that gives your team a safe, low-stakes environment to practice high-impact conversations. We’ve all experienced moments we’d like to replay or improve critical interactions; an AI-powered avatar offers your team real-time feedback and personalized coaching, building confidence before they step into crucial discussions.
  2. Use generative AI to tailor your communication based on DISC or other behavioral profiles, ensuring your message resonates effectively with different audiences. For example, as someone who tends toward detailed, lengthy emails, I’ve found AI incredibly helpful for crafting concise messages better suited to busy executives, as well as thoughtfully adjusting tone in high-stakes or potentially sensitive situations.
  3. Amplify the impact of peer-led coaching circles by leveraging AI-driven matching platforms (such as Together or Circles) to pair participants thoughtfully, based on shared goals, interests, and cultural alignment. With customized training on your organization’s values and norms, AI tools can also generate meaningful discussion prompts and real-time insights, strengthening cultural cohesion and engagement across distributed teams.

Conclusion

Scaling companies is still messy and hard in 2025, maybe even harder because of increased global uncertainty and current economic conditions. However, by drawing inspiration from cities and harnessing new generative AI tools, we can scale more intentionally and adaptively, even amid inevitable chaos. And maybe, just as with cities, embracing that chaos—with a touch of AI-powered magic—is precisely what can help organizations thrive and scale despite the challenges.

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