Cameo Doran

Cameo Labs

Most founders don’t struggle with ideas. They struggle with getting them built.

You know the feeling:
→ Customers are demanding features your team can’t deliver fast enough.
→ Competitors are launching what you’ve had on the roadmap for months.
→ Investors are pressing for traction while your team is still planning and complaining about tech debt

It’s frustrating. Every month of delay feels like you are slipping further behind: burning runway, missing deals, losing confidence.

That’s where we come in.

At Cameo Labs, we specialize in building the software that founders need shipped yesterday. Using a process we call Insight Driven Development, which combines AI acceleration with senior engineering talent we can turn urgent product ideas into production-ready platforms in 4–6 months. 

And because our process bakes customer insight into every stage, what we deliver isn’t just fast, it’s software your customers actually adopt and stick with. The kind that drives revenue, not churn.

The result isn’t another fragile product that crumbles under real use, or months of your team still stuck in planning. It’s real, revenue-driving software your business can put in front of customers right away.

Proof?
✔ An EdTech founder closed $3M in contracts on a platform we delivered in under 5 months.
✔ An infrastructure company saved $6M after we migrated 1,000+ servers with zero downtime.
✔ An AI training company hit $1.5M ARR in 9 months after we built their custom education platform.

We’re not a typical agency. We’re the partner founders trust when the stakes are high, the team is stretched thin, and the clock is ticking.

If you’ve got a product that can’t wait, let’s talk.

Founder

Nerd United

Nerd United is a Web3 venture studio focused on building blockchain enabled startups that solve real world problems. 

As SVP of Product for the Studio, I lead a high-caliber product team responsible for bringing highly innovative blockchain products to market.  We teach brands to accelerate the pace of product ideation and leverage concept validation methods to evaluate new product ideas and ensure the success of product launches and the startups as a whole. 

At Nerd United, we are systematizing innovation. I am responsible for building processes that allow teams and founders within our incubator to be learning and discovery focused. We teach repeatable processes for problem and solution validation, user discovery, market research, and other aspects of new opportunity evaluation.
I also work with individual brand product leads and founders to define and own product vision and strategic roadmaps. I act as a strategic partner and advisor to both the brands in the incubator and our portfolio companies, and I'm always looking for ways to create value and support the growth of our startups.

SVP of Product - Venture Studio

Theory of Anything Podcast

Co-Host

LeadingAgile

Leading Agile is company that specializes in agile and digital transformations for Fortune 50 companies. As a Managing Consultant, I was responsible for consulting with executive teams at enterprise companies on implementing agile processes and moving towards a product-driven model. 

I coached C-suite executives and their teams on best practices for modern product management, including design thinking tools and lean startup methodologies. I also trained executive teams on OKRs and leveraging an OKR mindset to empower product teams and create more successful outcomes.

Managing Consultant - Product Management & Strategy

SolutionStream

As Head of Product and Delivery, I focus on building innovation & agile development processes that help our clients create world class products.  With our SolutionMap™ process we keep clients laser focused on building the most valuable product features and exploring potential issues and roadblocks before we start writing code. 

I manage and deliver large, complex products from initial engagement to production launch and ongoing enhancement.  I provide product strategy and agile & lean operations training to our clients and employees while focusing on rapid delivery and top-notch user experience.  

I have helped clients from small startup to enterprise implement agile in transformative ways. I guide and train organizations on cutting-edge product management process. I also coach executives and product teams on strategy and process to deliver products that drive business results. 

I have built a framework that uses rapid agile product development processes and focuses on market research, user testing, refining and validating product ideas based on market and user data.

VP Product & Innovation | High-Growth Product Strategy | Worldclass Execution

LogMeIn

Recruited by the CTO as the first VP of Product, I owned all product-related activities including market definition, Product Strategy, User Experience (UX) and User Testing, and product roadmap and planning activities.  I was responsible for improving and managing product management processes speed up product time to market, deliver enhancements that improved user experience and ensure ROI on product development investments. 

▷ Directed a unified product team including product managers, UX/designers, and agile coaches.
▷ Collaborated with executive leadership to evolve product roadmap to match the overall product strategy.
▷ Introduced Design Thinking methodologies to enhance development processes.
▷ Ensured predictable delivery of features/products by using internally published roadmaps and measurable value of product department work
▷ Managed product planning processes with internal and external stakeholders
▷ Collaborated with customer/partner stakeholders to validate the effectiveness of the product team and communicate upcoming priorities
▷ Analyzed and prioritized future product opportunities based on Jive’s unique software platform
▷ Conducted tactical product management functions as needed - including gathering high-level requirements, developing user stories, etc.

VP Product Management ☆Product Innovation | Culture Transformation | Execution Strategy

Kartiva 

We make videos instantly shoppable. 
The future of everything is video and we make the technology that allows consumers to shop directly inside live video.

Advisory Board - Product

As the director of Product Management & Operations, I provided strategic advice and technology expertise to deliver the products and solutions our clients need to be successful. Oversaw strategy, design, development, production, and execution of complex programs for this well-known custom software development firm.
▷ Managed all development and production for Modere.com, a $2.5 million program introducing an all-new name and brand, but also a complete overhaul of their e-commerce system. 
▷ Oversaw creative, account, client, and technical teams to develop comprehensive products and product campaigns for various B2C and B2B clients
▷ Created budgets and maintain dynamic timelines for multiple interactive advertising campaigns simultaneously for various brand/agency clients
▷ Responded to proposal requests; Generate competitive bids, estimate level of effort and technical requirements, and develop project scope to create comprehensive statements of work for clients

Director of Product Delivery | Product Management & Strategy | Agile/Scrum Mentor

OrbSix Software

As a founder of this SaaS startup, I wore many hats. Since we were bootstrapping, every dollar was vital. I was responsible for almost everything that touched Product management and Product Strategy. I was also responsible for Project Management and Customer Experience.
As part of my role, I had to learn how to successfully manage projects and products and teams. In an attempt to develop more successfully, we implemented XP as an agile methodology. This was my first introduction into agile and it changed my perspective on how software should be built. 
I developed and implemented project and product Management guidelines and policies across the organization and monitored effectiveness and geared changes to optimize budget and time and ensure success.

Orbsix Software specializes in Loyalty Programs and Compensation Management software for the Retail and Network Marketing Industries.

Co-Founder | Head of Product 

FedEx Office

Worked directly with Project stakeholders to define project requirements and success criteria.  Coordinated with project teams to ensure on time project delivery.

Sr Project Coordinator

Chief Product Officer ⇢ Fractional Prices with Full-Time Impact ◆ Digital Transformations ◆ Advising Startup Founders and Executives Building Innovative Products ⇢ AI / XR / Web3

I’ve always been fascinated (and a little frustrated) by how many great companies can’t seem to ship great software. I’ve sat inside teams where brilliant ideas died in Jira backlogs, and I’ve watched the opposite too: features rushed out fast, only to flop because they were poorly executed or customers didn’t care. Both hurt. Both waste time and burn cash.

Cameo Labs is my answer to that problem. I started it because I couldn’t stand watching founders lose momentum on ideas that should have changed their business and their lives. 

My mission is simple: help founders build the right product, fast enough to matter.

Since starting, I’ve led projects that:

- Shipped an EdTech platform that closed $3M in contracts in under five months.
- Migrated 1,000+ servers with zero downtime, saving $6M.
- Built an AI training platform that scaled to $1.5M ARR in 9 months.

I’m dorky about this stuff. I love the messy early product conversations, poking at assumptions, figuring out where speed really matters, and then helping a team pull off what feels impossible. That’s what gets me up in the morning.

If you’re a founder under pressure to ship something that can’t wait, I’d love to hear about it.

Chief Product Officer / Fractional Product Management

As the owner and founder of Cameo Labs, I am dedicated to helping CEOs and executives of $100M - $500M businesses in the Edtech, Fintech, and SaaS sectors achieve rapid and transformative innovation. 

At Cameo Labs, we understand that staying competitive in today’s market requires more than just innovative ideas—it demands strategic execution and a customer-centric approach.


I founded Cameo Labs to empower our clients by streamlining their product development processes, aligning their business strategies with technological advancements, and fostering a culture of continuous innovation.

We operate differently than most agencies. 

Our holistic approach combines technology, strategic foresight, and customer insights to create cohesive and impactful product visions. Our clients have experienced faster innovation cycles, improved product success rates, and higher customer satisfaction.

With a proven track record of success, we are committed to turning your challenges into opportunities for growth and success. Our goal is to be your trusted partner, helping you achieve your business vision and lead your industry.

Let’s Connect:
Discover how Cameo Labs can transform your business. Visit http://cameolabs.com or reach out to me directly to explore how we can work together to drive your business forward.

Everyone wants AI, but almost no one is ready for it. 

And it's not just one thing holding companies back. It's a whole laundry list of problems. 

Companies are rushing as fast as they can to implement AI.
Add it to development. Embed it in the application. 
Hell, embed it in everything and use it to replace headcount.

But so far, the data is pretty clear. Most companies struggle to see tangible, long term impact. I mean beyond more causing dysfunction and confusion

Because AI depends on how well your system already works.
It's the well-worn phrase, "Garbage in, garbage out" at scale and lightening speed. 

the companies seeing the greatest impact are the ones with good foundations. 

If you want to compete seriously with AI, you have to fix the flow of data, decisions, and ownership first.

li_at

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Talks about #agile, #innovation, #leadership, #leanstartups, and #productmanagement

From Vision to Execution | Faster Products, Better Results, Bigger Impact | SaaS Companies | Strategic Software Partner

"If you continue to improve a product enough, eventually you'll ruin it."
- David Pogue


What’s your favorite product that got ruined by improvement?

So many teams fall into the trap of new feature chasing and the. they don’t understand why customers stop using their product..

Friendly reminder:
The code was never the bottleneck

Did you know that even before AI, as much as 60% of code was never used?
Complete waste!

And now AI can spit out code in minutes. But that speed doesnt make it inherently useful. No matter how quickly you can debug it. 

The hard part in product development is:
--> Identifying a clear urgent problem
--> Making real product decisions
--> Keeping your code flexible enough to allow for change
--> Closing the loop between what you ship and what your customers actually want/use

If you want AI to be a multiplier instead of a mess, Treat code as the cheapest part of the system and discipline as the real scarce asset.


Are you using AI to actually improve your product development process, beyond “we make more code faster”?

When everything is #1, nothing is. 
Everyone knows it's true. 

But I bet your organization is still guilty of this: Trying to make everything a top priority.

“But it’s all critical” you say. Sorry friend, it just doesn’t work that way. 

Balancing multiple critical projects seems like the way to get ahead. And especially with AI, everyone is convinced that they can add more and more and more work into the queue.

It almost always results in everything moving slower. 

It’s why effective prioritization is critical. 
It's why the key responsibility of leadership is to effectively decide and communicate what is the most important thing. 

Instead of spreading your resources thin, focus on what truly matters. 
Remember, clear priorities lead to clear outcomes. 

Choose wisely.

AI doesn’t hallucinate. 

It is working as designed. 






Hi, I am Cameo👋

I help transform “we just added AI” into 
”customers now pay more for this.”

No alternative text description for this image

I guess this is an effective way to know your meeting could/should have been an email. 

At least people read emails still (mostly) 
No one is even going to skim the meeting summary.

Your company’s data situation is probably way worse than you think.
(this cake meme doesnt begin to do it justice)

You’ve got plenty of data, but it’s scattered across tools and sources and no one agrees on definitions. 
And half the metrics were made up by some long-gone employee named Bob, and never fixed.

Your Dashboards may look polished, but underneath, it’s messy. 
Employees are stitching numbers together just to keep things moving.

And most people don’t even trust what they’re looking at.

And now you want to talk about AI, or automation? I'm sorry, friend, you're just not ready. 

If your data foundation isnt solid, nothing you build on top of it is going to work

What I often see is a bit different. 

The CEO announces a bold AI vision and tells the CTO to make it happen, but refuses to allocate any additional budget and insists that they can make do with Copilot. 
The CTO knows this won't work but can't push back because the CEO already made the announcement at the last all-hands.

Meanwhile, there's no training, but everybody is still using it in the day-to-day, but usually only ChatGPT, and they hide it because they're supposed to be using Copilot. They are sharing prompts in Slack DMs, and getting shit done despite the lack of support. 

They're not hiding because they're rebels. They're hiding because admitting they need better tools means admitting the company's "AI strategy" is a joke.

The real problem isn't that we need more Jacks. It's that Jack is forced to work around a leadership team that thinks AI transformation happens through proclamation instead of investment. 

You can't transform a company with vibes and a press release.

🤖 Generative AI Lead @ AWS ☁️ (150k+) | Startup Advisor | Public Speaker | AI Outsider | Founder Thinkfluencer AI

The real reason AI is failing inside companies (and nobody wants to say it)...

Here’s what the org chart looks like in most companies “going all-in” on AI:

- The CEO announces a bold AI vision and tells the CTO to make it happen.
- The CTO nods, shifts priorities, and hands the CIO the budget to “become AI-first.”
- The CIO gets excited and starts building a brand-new AI empire:

A Chief of AI? Of course.
An AI Center of Excellence? Mandatory.
An AI Ethics Officer? Absolutely.

And yet… you already know the punchline.

Everyone is talking about AI.
Almost no one is building with AI.

Except Jack.
The intern.
The only person actually shipping anything.

And then leaders wonder: “Why isn’t our AI strategy working?”

The answer is simple.
We don’t need more AI committees.
We need more Jacks.

#ai

Sampling bias is the quiet killer of product decisions.

The people who answer surveys are almost never “customers.” They are a specific personality type with spare time, strong opinions, or a grudge.

So a team builds and ships a new feature because: “We asked 200 users and 92% wanted it.”

No you didn’t.
You asked the 200 people who felt like answering.

Meanwhile, the silent majority:
	•	ignores the survey
	•	never opens the feature
	•	churns with zero explanation
	•	complains to peers instead of your inbox

That is how feature bloat happens.
That is how AI turns into product clutter.

You know what kills trust faster than a bad product? A great pitch followed by a mediocre product.

I bet you have seen it again and again. 
Sales overpromises. Product underdelivers, and the customer suffers.

And here is what I rarely see mentioned: Sales overpromises because the product is already underdelivering.

The sales team is out there trying to close deals with a product that doesn't meet customer needs. So they start selling what they wish existed. Because what does exist isn't good enough.

So why are companies building products that are so obviously underwhelming in the first place?

Companies waste 69% of their development investment on features customers don't use. And AI is making this worse. 

Sales teams are pitching AI capabilities that don't exist yet or wildly overstating what the technology can actually do. Product teams are scrambling to deliver on promises that were never realistic in the first place.

But the problem isn't Sales. The problem is companies are building the wrong things and wondering why nobody's excited about them.

Your customers deserve better than this.

Next week I'm running a training for founders who are tired of wasting capital on products their customers don't want. Come learn how to validate what to build before you build it.

Send me a DM if you want an invite.

Everyone is optimizing for speed.

And believe me, I think that speed matters more than ever. 
But dont mistake picking which way to build the wrong thing faster as a strategic choice.

Hey, that new feature was cheap to build with AI, right?

Cheap code almost always has an expensive outcome.
- A surge in support tickets.
- Feature bloat in your app
- Disgruntled customers churn.

And now your team gets to babysits a feature nobody wanted anyway....forever

Just my weekly reminder:
Code was never the bottleneck. Value still is

In the professional world, Is it ok to say, “I am learning something new. I am still bad at it.”?

The rise of AI is crazy. I don’t think we’ve ever had a moment where so many experienced people have to pick up something new at the same time. 

Lots of people talk like they are experts. Most people quietly feel like beginners.

Most business cultures still reward polished confidence over honest, in-progress learning. So people fake it and quietly fall behind while AI is reshaping how real work happens.

So I am am here to tell you, if you want to truly learn and grow, you have to do new things even when you are still bad at them.

With AI, you cannot wait until you feel ready. Be willing to learn in public. It is okay if you feel you are not ready or lack the in-depth experience. 
That is still true of almost everyone. 

What are you going to push yourself to be bad with AI at this weekend?

AI isn’t the problem...
The shoddy way you’re trying add it to your product is the problem.

Slapping AI onto your software doesn’t make it innovative, it is just a gimmick unless it actually solves a real customer problem. 

It seems like most every company is using AI as a buzzword to impress investors or check a trendy box. 
I guess they think that we the customers are too stupid to realize when something's crappy

LinkedIn is the poster child of crap AI products.

Is there anyone releasing innovative solutions anymore? 
Why is the AI age so mediocre?

I will say the exception to this for me is Wispr Flow, which I absolutely love.

What are you seeing out there? Are there products that you feel are going to change the world, or have we entered the "mid" era of the internet? 🎙️

Hey! That's not my music! This is all wrong! 

That was my immediate thought when I saw my Spotify wrapped. 
I suspect that a lot of parents with younger kids had a similar reaction. 

But then I remembered how every time my 13 year old gets in the car she asks, "can I put on music?" And of course I say yes and hand her my phone. 

And so we drive to and from school, or to run errands, or up the canyon late at night while we eat ice cream and listen to the music she loves and for a few precious minutes a day she lets me into her world. 

Of course my Wrapped would reflect that. 

I see the same thing with companies and their data all the time. 
They do not trust it. They argue about it, or second guess it. 
They say, “That cannot be right,” anytime the numbers do not match the story in their heads.

But data remembers the small moments we forget. 

So yeah, maybe those arent the songs I would have chosen, but I wouldnt change them for the world. 

Where are you looking at your data and saying, “That cannot be right,” when it absolutely is?

Trusting Spotify_Cameo Doran

2018: Our Development teams are slow, we need to add more developers.

2025: Our development teams are slow, we need to add more AI
Different moment in time, same organizational mistake.

Executives keep assuming that more engineering firepower solves slow delivery.

Wrong ⚠️ 

Without changes to systems and process, more code generation usually slows you down because you just run into the same constraints, faster.

Instead, look at the real roadblocks:
↳ No real customer feedback loops
↳ Vague requirements and shifting scope
↳ Stakeholder chaos and unclear ownership
↳ Release anxiety and brittle delivery pipelines
↳ Governance bottlenecks (security, legal, compliance)

Fix those problems instead. And watch productivity soar.

“Stop mining our souls”

Fiery speech by Utah Governor Spencer Cox today at Utah AI Summit. 

“We will not continue to allow big tech companies to abuse us. 

We own our data and can take it anywhere we want

We are going to tell you (big AI companies) what we will and won’t allow.”


I don’t always approve of the Utah political landscape, but I like the vision of the Utah Pro human AI initiative he announced today.

Albert Einstein famously said, “It is not that I’m so smart. But I stay with the questions much longer.”

When we are building products, success doesn’t require that we have all the answers, 
But it is critical that we are asking the right questions.

The best leaders and teams embrace curiosity. 
They dig deeper when something doesn’t feel right. They ask “why?” Over and over when others stop at one.

Ultimately our goal in product development is to: 
- Discover root causes
- Challenge our assumptions
- and Find insights and opportunities others miss.

The truth is most breakthroughs don’t come from what we know. 
They come from what we’re willing to uncover by asking better questions.

How do you approach curiosity in your work? Do you push past the obvious to find the real insight?

This headline cracks me up. 

I am glad to see Spotify is the benchmark for successful subscriber growth.

Interestingly Open AI currently has 35 million subscribers after 3 years and is giving themselves 4 years to get to 220 million (not quite the 280 million that Spotify currently has)

I would be curious how Spotify classifies users on their family plan. It allows 6 users for $19. 
Do they count those as 6 different subscribers? Or one? 

Will OpenAi try and create a comparable family type plan to drive up their subscription numbers to reach this milestone? 

A lot of people are claiming that we are only a couple of years away from AGI or ASI or whatever name for artificial super intelligence they are using. 

But apparently they think super intelligence will happen before this Spotify subscription milestone is achieved. Lol

Maybe their super intelligent version of ChatGPT can help OpenAI figure out increase subscriber count fast. 

What do you think? 
Will Open AI drive a Spotify level of growth over the next 4 years?

Do you want a key to more happiness?

Start with gratitude.

It is Thanksgiving, so hopefully you are already thinking about what you are grateful for. But gratitude changes your entire life, not just your mood for one day. 

Years ago during a dark time, I started a purposeful gratitude practice, and now being thankful is a constant mindset that has transformed my life. 

Being appreciative of the goodness in my life opens me up to joy. 
Gratitude helps me refocus on my blessings rather than my trials or frustrations. 

People who regularly practice gratitude: 
- Sleep better
- Feel more alive
- Have stronger immune systems.
- Experience more positive emotions
- Express more compassion and kindness

Here are some simple ways to incorporate gratitude into your life:

𝗠𝗲𝗱𝗶𝘁𝗮𝘁𝗲.
- Focus on the present moment without judgment 

𝗞𝗲𝗲𝗽 𝗮 𝗴𝗿𝗮𝘁𝗶𝘁𝘂𝗱𝗲 𝗷𝗼𝘂𝗿𝗻𝗮𝗹. 
- Try and be specific about the sensations you feel when good things are happenings.

𝗪𝗿𝗶𝘁𝗲 𝗮 𝘁𝗵𝗮𝗻𝗸-𝘆𝗼𝘂 𝗻𝗼𝘁𝗲.
- Find excuses to formally express enjoyment and appreciation to the people around you. 

Lastly… on a day dedicated to gratitude, make sure to openly express thanks to the people around you and to your creator for your blessings and fortune. 

Have you incorporated a practice of gratitude into your life? I am curious to hear how it has impacted you.

Ivy Captech Advisors

Seasoned innovation and digital transformation leader guiding organizations through strategic technology adoption. Leverages deep expertise in emerging technologies, including AI, DLT, and cloud, to bridge the critical gap between business and IT. Proven track record of developing data-driven strategies and transforming concepts into scalable solutions that deliver measurable business value across industries.

Managing Partner, Chief Possibilities Officer

Duke Master in Fintech

The Duke University Master's in Financial Technology program prepares future leaders at the intersection of finance and technology. This innovative curriculum combines technical expertise with industry insights, equipping students to drive transformation in the evolving financial services sector.

Board Member

Chief

Chief stands alone as the premier network tailored exclusively for top-tier women executives. Chief's mission to to help empower women leaders, amplify their impact, and create pathways for them to elevate others alongside them.

Member

NYU Tandon School of Engineering

The NYU Cyber Fellows program at the NYU Tandon School of Engineering is a cutting-edge initiative designed to cultivate the next generation of cybersecurity leaders. This innovative program combines advanced technical training with real-world industry insights, preparing students to tackle the complex and evolving challenges in the cybersecurity landscape.

Advisory Council

The Depository Trust & Clearing Corporation (DTCC)

Established the firm's Internal Innovation Strategy and inaugural AI Enablement Teams, led cross-functional leadership committees, and drove the experimentation and adoption of emerging technologies, including Generative AI, DLT, AR/VR, and quantum computing. These efforts supported the firm's strategy, enhanced operational efficiencies, reduced risk, and improved the client experience across global financial markets, while fostering a culture of innovation throughout the organization.

Executive Director, Head of Internal IT Research and Innovation

Ivy CapTech Inc

Led product strategy and digital transformation initiatives for Fortune 500 companies across global markets. Managed budgets from $4M to $10M, driving innovation from concept to market success.
Partnered with CXOs and led cross-functional teams to drive innovative tech initiatives, developing and executing comprehensive go-to-market strategies:

Reckitt - Head of Digital & Innovation for North America, Health & Nutrition brands 
•	Grew Digital and Innovation team from 3 to 20+ members, implementing a Digital Factory model, achieving 70% increase in digital initiatives and 50% faster time-to-market
•	Led digital transformation strategy, achieving 200% cost reduction across 20+ brands globally through a headless architecture design

Estee Lauder - Head of Beauty Advisor Digital Innovation 
•	Guided implementation of emerging technologies (Mobile, AI/ML, AR/VR, Digital Learning) across 20 brands, 5 regions, impacting 100K+ beauty advisors
•	Implemented the firm’s first digital learning solution, increasing operational efficiency by 35% and driving 11% sales boost

UBS Asset Management - Strategy Consultant 
•	Led the data and model development team for firm's first AI-driven robo-advisor platform, which grew assets by $200M+ within three months of launch
•	Developed go-to-market strategy and technology architecture for new lending product targeting the firm’s ultra-high-net-worth clients

Innovation and Digital Transformation Executive

Bats Hotspot SEF LLC (aquired by Cboe FX)

Javelin SEF, which was acquired by Bats Hotspot SEF are regulated electronic trading platforms that facilitate the execution of swap transactions in the derivatives market. These platforms emerged in response to post-2008 financial crisis regulations, particularly the Dodd-Frank Act, aiming to enhance transparency and reduce systemic risk in over-the-counter (OTC) derivatives trading.

Cohen & Steers

Led the team responsible for driving digital transformation across Cohen & Steers, overseeing strategy, implementation, and cultural change.

VP, Head of Strategic Projects & Initiatives

Fractional Chief Product Officer for multiple tech ventures across diverse sectors, driving products from concept to market launch and scale. Notable achievements include pioneering a mobile digital content platform for emerging markets that achieved 500%+ monthly user growth and a $125MM+ exit valuation, developing an AI-driven medical advice platform, and guiding a fintech startup through complex regulatory processes to successful acquisition.

Startup Co-founder and Head of Product

Bear Stearns Companies

Led enterprise-wide digital transformation and innovation initiatives, helping to bridge technical and business domains to enhanced efficiencies and improved client experiences.

Managing Director, Digital Innovation

Expert catalyst in driving Innovation from 0 to 1, excelling in building innovation teams, launching new products, embracing emerging tech, and a passionate couture design enthusiast

Strategic innovation leader with a proven track record of driving digital transformation and product success across fintech, retail, CPG, and technology sectors. Over two decades of experience guiding both startups and Fortune 500 companies from concept ideation through successful market launch and beyond.

Expert in leveraging emerging technologies, including AI/ML and blockchain, to solve complex business challenges and create new market opportunities. Skilled in translating intricate technical concepts into actionable strategies for C-suite executives, enabling data-driven decision-making that accelerates growth and optimizes efficiency.

Demonstrated ability to lead cross-functional teams in developing and implementing innovative product strategies, secure significant funding and strategic partnerships for high-growth ventures, and orchestrate successful exits. Track record of scaling operations from startup to multimillion-dollar enterprises while fostering cultures of innovation that drive continuous improvement and market leadership.

Passionate about helping organizations navigate digital disruption, launch groundbreaking products, and achieve transformative business outcomes through technology-driven innovation. Combines deep technical insight with business acumen to identify market opportunities and guide companies from concept to successful product launches and sustained growth.

Darlene Newman

Another research report. Another set of failure statistics. Another article explaining why AI projects don't deliver.

95% failure rate. Only 26% see ROI. The usual suspects.

But this HBR piece actually gets to the point… leaders are treating AI like a tech purchase when it's a behavior change problem.

And that's why millions are being lost in abandoned projects.

Let me translate what this report is stating…

What leaders think…
"We'll buy sophisticated AI, deploy it, and employees will adapt."

What actually happens…
“Clinicians ignore life-saving decision-support tools because alerts interrupt their workflow.”

The perceived loss of 30 seconds > saving lives.

That's not irrational. That's human.

The research breaks it down:
→ 71% of CIOs say they drive AI innovation
→ 32% say they drive organizational transformation

That 39-point gap? That's where your AI initiatives seem to stall.

You can't separate technology from the humans who use it. But, for some reason, we also focus on the technology implementation or successful implementation of it… we;

🔹 Build AI to technical specs… but leave out the human impact
🔹 Survey employees only AFTER deciding which tool to buy (as a way to support the decision)
🔹 Assume people will "figure it out"
🔹 Double down on failing projects (sunk cost fallacy at scale)
🔹 Measure efficiency, not trust or adoption

And when things don't go as planned, we wonder why AI doesn't deliver.

This is emerging tech adoption at its best.

If you want to success at AI adoption, here's what actually works (backed by behavioral science):

☑️ DESIGN: Co-create with diverse users. Add friction where it forces scrutiny. Test with real users before launch. (Fact: Major speech recognition systems had double the error rate for Black speakers versus white speakers… a bias that proper diverse testing would have caught before launch.)

☑️ ADOPTION: Stop positioning AI as infallible. Disclose limitations. Frame it as augmentation, not replacement. Reiterate it will not be perfect. Make mistakes relatable. (When healthcare providers were transparent about AI limitations, trust went UP.)

☑️ MANAGEMENT: Kill failing pilots fast. Measure trust and fairness, not just efficiency. Acknowledge your own biases, you probably overestimate your AI expertise. Invest in change management as a core competency.

None of this is revolutionary. It's basic change management.

Let me be clear… the most sophisticated AI in the world fails without the foundational people work.

Your processes need documentation. Your data needs structure and context. Your organization needs change management expertise. Your employees need ownership and trust.

AI transformation = People transformation

The technology is the easy part. The people work is where transformation lives or dies.

Stop pretending otherwise.

Strategic partner for leaders' most complex challenges | AI + Innovation + Digital Transformation | From strategy through execution

HBR: How Behavioral Science Can Improve AI ROI

Deloitte’s lessons learned report hits the nail on the head about implementing Agentic AI correctly 

Deloitte’s SFL Scientific team just released one of the more practical reports on what really happens when you try to scale Agentic AI... and for those of us in the trenches, it validates what we already know.

You can showcase new GenAI features in minutes…
but deploying production-grade solutions still takes time.

Not because the work vanished, it's simply shifted.

According to the report, the effort once spent on upfront data collection and planning has moved downstream into:

🔹 Post-hoc validation
🔹 Guardrail + reliability engineering
🔹 Change management + user adoption
🔹 Infrastructure scaling

Basically, rapid prototyping can actually be “rapid”, but enterprise-grade production is still hard… just harder in new places.

And that sets up the most important insight in the entire report…

Stop keeping humans “in” the loop. Start placing them “on” the loop. (And yes, I did have to look up the term "on the loop")

Most enterprise “AI automation” still looks like this…
AI → human approval → AI → human approval.

That's not autonomy.
That’s workflow automation with a new UI, not Agentic AI.

In the loop = humans confirm every step.
On the loop = AI acts autonomously within guardrails, while humans oversee exceptions, anomalies, and summaries.

This distinction determines whether AI stays a digital tool… or becomes a force multiplier.

Here’s what “on the loop” looks like

Contract review:
🔹 In the loop: Review every extracted clause.
🔹 On the loop: AI processes the whole contract, approves standard terms, flags risks.

Customer service:
🔹 In the loop: Agents edit every AI draft.
🔹 On the loop: AI handles Tier 1; agents address escalations and receive daily summaries.

Throughput starts compounding.

But, how do organizations get there? Most won’t jump straight to autonomy, nor should they. 

So, here's a path you can take: 

1️⃣ Automate the tasks, use human-in-the-loop to validate

AI handles discrete tasks; humans stay fully in the loop
Goal: trust, baselines, error patterns

2️⃣ Establish confidence thresholds

Use operational data to determine where AI is reliable.
High confidence = auto
Low confidence = human review

3️⃣ Let AI run high-confidence tasks autonomously

AI executes, summarizes, and escalates anomalies
Humans shift from approving transactions → reviewing exceptions

4️⃣ Continuously refine boundaries

As guardrails mature, more work moves on the loop, where enterprise ROI finally shows up.

The real shift means rethinking what "control" means.

In traditional transformation, control = human approval.
In agentic transformation, control = guardrails, triggers, monitoring, and escalation paths.

And evaluation changes too. The question isn’t just “Is the AI accurate?”
It’s “Is the system generating business value?”

If humans stay in every loop, you haven’t built an AI solution.
You’ve built expensive middleware.

Five Lessons Learned from Building Agentic AI Solutions

The real blueprint for AI transformation…

I just finished AWS's latest executive guide on AI transformation. They hit the right notes, but like most big tech frameworks, it misses the operational reality where transformation actually happens (or dies).

After decades leading innovation through multiple hype cycles, here’s the truth... the gap between AI hype and AI execution isn’t technology. It’s strategy and enablement.

Here’s what AWS gets right... with the nuance most miss:
 
1️⃣ "Think big" = Define your north star
You need a transformation thesis, not a list of use cases.

What workflows shouldn’t exist in 5–10 years? What new capabilities will differentiate you?

Innovation isn’t incremental efficiency… it’s rearchitecting how work gets done. And yes, it takes years.

2️⃣ CEO belief beats CEO ownership

Your CEO doesn’t need to run AI, but they must understand it as a significant shift in everything the firm does, not an IT project.

When leaders believe in the long-term vision, they remove blockers instead of chasing quarterly wins.

3️⃣ Data is everything, but governance can't be retrofitted as easily

52% of CDOs say their data foundation is inadequate for AI.
You can’t bolt AI onto chaotic, siloed, inconsistent data.

This is a multi-year modernization effort… real-time pipelines, governance by design, data as a product.

If you’re unwilling to commit to that timeline, you’re not ready for AI in production.4.

4️⃣ Federated enablement > centralized bottlenecks

Most companies either centralize everything in an AI COE (bottleneck) or decentralize fully (chaos).

Instead…  Federated enablement.

A shared platform + clear guardrails + domain-led execution.

5️⃣ "Outside expertise" ≠ Big consulting

AWS says 90% of successful AI projects involve partners… true.
But you don’t need someone who writes a report and leaves.

You need practitioners who’ve shipped production AI, navigated governance, and built real enablement platforms. That’s a unique skill.

Why 95% of AI investments fail

Not because AI doesn't work, but because organizations look at this as just another digital transformation. But, here’s the nuance;

AI isn’t hype. It’s the next generation of how work gets done.
Is it fully ready today? Partially.

But the companies that understand where this is all heading, they’re following a very specific playbook:

🔹 Believe in the future state → anchor on a long-term transformation vision
🔹 Build a strategy to get there → work backwards not forwards
🔹 Choose high-value use cases → where data is mature and impact is clear
🔹 Create a federated platform → one place to experiment, one clear path to production, universal guardrails and easy to understand processes
🔹 Pick partners who build with you → execution-minded innovators.
🔹 Commit to the timeline → protect funding, momentum, and the long game

AI will transform everything we do… some will lead, some will follow. Choose your path.

🔁 Follow me if you want innovation to stick

The Executive Blueprint for AI Transformation: AWS

Rare moment... OpenAI’s new “From Experiments to Deployments” guide actually reflects how AI gets built and deployed in the real world.

Their four-phase approach... foundations, fluency, prioritization, then build and scale, tracks almost exactly with what many of us have been talking about… what it actually takes to move AI from experimentation to production at scale.

It all starts with the foundation.

1️⃣  The guide opens with something that can’t be emphasized enough… executive alignment, governance designed for speed, and data access come before the flashy deployments.

Not as a blocker, but as the enabler for everything else.

They’re explicit about this. Companies that skip this groundwork “move fast at first but stall when gaps appear.”

And the governance framing is exactly right… it’s governance for motion, not gatekeeping. Create fast paths for experimentation while your frameworks mature alongside real use cases.

Once you have that, then build fluency everywhere.

2️⃣ Phase 2 is about creating organizational literacy before expecting innovation to scale.

Not theoretical workshops… hands-on practice, champion networks, daily office hours to answer questions.

BBVA is a perfect example: 3,000 → 11,000 ChatGPT licenses, 83% weekly usage, 2,900 GPTs created. That only happens when fluency reaches critical mass.

And OpenAI calls out something most reports miss. Those SMEs you develop in Phase 2 are the ones who later define what “good” looks like as use cases get more complex.

Then comes structured intake and prioritization.

3️⃣ Phase 3 tackles the “now we have 500 ideas” problem.

Open intake, but disciplined scoring around impact, effort, risk, and reuse.

That reuse lens is critical. Every pattern you build once should accelerate multiple future projects. Without it, you end up with duplicate agents, duplicate data, duplicate context, and duplicate risk.

Finally, incremental building with continuous evaluation.

4️⃣ Phase 4 gets into the real operating rhythm… gated checkpoints (MVP → Pilot → Production), constant evaluation, and iterative improvement.

OpenAI is clear here: AI systems don’t improve by scaling infrastructure.

They improve by iterating the product itself… refining prompts, workflows, evals, grounding data, failure modes, cost efficiency.

The core message is the same many of us have been seeing in the field.

--> Organizations stuck in pilots are usually waiting for conditions that will never be perfect.
--> Organizations reaching production put structure around experimentation and learn by doing.

And the guide is honest about what actually drives scale: not the latest model, but the less glamorous work… building skills, capturing learnings, and creating reusable systems.

Definitely worth reading if you’re in the “how do we make this repeatable?” phase rather than the “should we even be doing this?” phase.

From experiments to deployments: OpenAI

You should evaluate and govern AI agents the same way you onboard employees.

Smart advice from the World Economic Forum and Capgemini in their most recent white paper.

It was an interesting point in their report, and here's why…

If you think of agents as "software," you focus on features.
If you think of them as digital employees, you focus on:

🔹 What role are we hiring them for?
🔹 What authority should they have?
🔹 How do we monitor performance?
🔹 When do they need supervision?
🔹 How do we safely expand their responsibilities?

The report builds its entire governance model around these questions.
And they might be right. 

Here's what the white paper outlines.

1️⃣ A classification system that feels like a job description

Agents are defined by their function, role, predictability, autonomy, authority, and environment… the same way you'd define the scope, responsibilities, and working conditions for a new hire.

This forces clarity on what the agent is and what it should and shouldn't be allowed to do.

2️⃣ Evaluation that looks like performance management

Most benchmarks don't capture real operational complexity.

This report emphasizes contextual evaluation… success rates, latency tolerance, error types, tool-call reliability, edge-case handling, and ongoing behavioral drift monitoring.

In other words, don't test the agent in a lab, test it in its actual job.
Which means… you need to move from experimentation to controlled deployment with clear guardrails.

Start small, monitor closely, expand deliberately.

3️⃣ A risk assessment model tied directly to autonomy + authority

The report's risk model is specific: the more autonomy and authority you give an agent, and the more complex the environment, the more governance you need.

Sounds pretty obvious, right?

But just like you wouldn't give a new analyst the keys to the trading desk, you shouldn't give a new agent high-risk permissions.

So plan for how you would oversee a new hire. Then expand responsibilities over time with specific oversight. See #4 below…

4️⃣ Progressive governance that scales as capability scales

Baseline safeguards for every agent.

Let’s repeat the… EVERY agent… least-privilege access, sandbox testing, audit logs, monitoring, compliance checks.

Then, as responsibility increases, oversight evolves from HITL (human-in-the-loop) to HOTL (human-on-the-loop), to continuous monitoring and incident response.

And, this is exactly how organizations mature human oversight.

This report takes a unique approach to agentic implementation.

Agents aren’t “tools”. They’re decision-making systems operating inside your business.
And that requires a management system, not a feature checklist.

But I'll argue… it still misses the most valuable aspect of all of this: corporate context.

Every new hire still needs to understand "the company" in order to be successful.

Agents in Action: Foundations for Evaluation & Governance

OpenAI trained an AI to confess. The punchline writes itself.

OpenAI just published research on training GPT-5 to “confess” when it misbehaves. The idea... if we can’t trust AI to behave, maybe we can trust it to tell us when it's been bad.

I'll give you a moment to think that one through…

The paper describes a system where, after an LLM answers a question, it produces a “confession report”... a self-evaluation of whether it followed instructions or did anything shady.

And imagine… they even invoke the Catholic seal of confession as an analogy.
A safe space for the model to come clean.

Let me rip the bandaid off… it doesn’t.

LLMs don’t know. They don’t know anything... they predict.
In fact, they really just predict the next word in the sentence.

It’s impressive… don’t get me wrong.

But when GPT writes “I intentionally gave wrong answers,” it's not introspecting.
LLMs generate text word-by-word... a guess based on what came before.

There is no completed thought before it’s expressed, or understanding of knowledge.

Asking whether the model is being “honest” is like asking whether your keyboard meant what it typed... It doesn’t even know the sentence it’s writing.

And let’s talk about the foundation

The model’s entire concept of honesty and confession comes from its training data... Reddit threads. Outdated Wikipedia. Contradictory news articles. Blog posts by confused people.

The model doesn’t have beliefs.
It has compressed representations of a noisy, frequently wrong corpus.

Now here’s where the research really gets interesting…

To evaluate whether confessions are honest, they use "another" LLM as the judge.

That judge has all the same problems as the original LLM being evaluated... yet we’re supposed to believe it?

So you have one pattern-matching system producing confession-shaped text... and another producing judgment-shaped text.

Neither understands anything.

What OpenAI actually built... a text generator producing confession-shaped outputs, evaluated by another text generator producing judgment-shaped outputs.

And the punchline is that they published this with a straight face.

Let's just leave it at that...

Training LLMs for Honesty via Confessions: OpenAI

Coding agents guarantee a federated future, whether companies are ready for it or not.

OpenAI’s Building an "AI-First Engineering Team" guide tells you what agents can do.

What it doesn’t tell you is what happens to your org chart.

Yes, agents can now handle multi-hour reasoning, draft entire features, write tests, refactor across dozens of files, and even triage production logs. The report walks through concrete examples:

🔹 converting Figma designs into React components
🔹 generating test cases that catch edge cases engineers miss
🔹 reviewing pull requests for race conditions and security vulnerabilities
🔹 auto-updating documentation as code evolves

And underlying it all… task duration is doubling every seven months. Models went from 30 seconds of sustained reasoning to over 2 hours.

But there’s a far bigger operational shift leaders need to pay attention to.

Coding agents don’t just accelerate engineering, they make it possible for anyone in the organization to become a builder.

And once that happens, centralized control of development becomes a non-starter.

Think about it... agents generating new payment validation logic, encoding KYC rules in ways legal never saw, modifying customer data flows in support systems, and creating region-specific forks to meet local regulations... all happening inside team-owned repos.

Work isn’t “done by the center” anymore, or built into an admin tool

It’s built at the edges, where domain expertise actually lives.
The center’s role fundamentally changes.

It won’t be fully responsible for execution, and frankly, it shouldn’t.

Its job becomes enabling teams to build safely… setting guardrails, defining patterns, providing infrastructure, ensuring compliance.

Is that unsettling? For anyone in governance or risk… probably.

But IT federation will happen whether you plan for it or not.

If you don’t design for it, it will happen in the shadows.
If you do design for it, it can happen in a controlled, secure, scalable way.

The only sustainable operating model is federated enablement:
🔹 Domain teams own execution
🔹 Platform teams own the standards
🔹 Governance owns the non-negotiables
🔹 Agents amplify the organization instead of fracturing it

The future of engineering won’t be “AI-first” or “agent-first.”

It will be federated-development first.

Federation has been happening quietly, coding agents are just making it impossible to ignore.

Building an AI-native engineering team

If you want to understand the biggest blind spot in AI, start with the Louvre heist.

The thieves didn’t outsmart the technology and surveillance.
They outsmarted the assumptions all the security was trained on.

On October 19, 2025, four men walked into the Louvre Museum in Paris pushing a furniture lift and wearing hi-vis construction vests.

Eight minutes later, they walked out with €88 million in French crown jewels.

--> No explosions. 
--> No elaborate tunnels. 
--> No George Clooney’s hacking into sophisticated security systems.

Just four guys dressed like workers, doing what workers do.

The entire heist happened in broad daylight, in one of the most surveilled museums on earth, with hundreds of witnesses who saw nothing wrong.

Sounds too far-fetched, right?

Not really.

The thieves understood how humans “actually” see the world. 

We don’t see objective reality. We see categories… patterns we’ve learned to treat as “normal.”

--> A furniture lift in Paris? Normal
--> Construction workers near an old building? Normal
--> Maintenance activity at the Louvre? Also normal

We see categories.

The thieves didn’t avoid detection... they fit the category so well that no one questioned them.

They used the lift to reach a balcony, entered through a window into the Galerie d’Apollon, smashed the display cases holding jewels belonging to Napoleon III and Empress Eugénie, and walked out.

The entire operation depended on one thing… performing the role of “ordinary” convincingly enough to slip past everyone.

And it worked.

The sociologist Erving Goffman called it the "presentation of self"… humans perform social roles by adopting the cues others expect to see. 

When someone fits the category? We just associate it with "normal," invisible to scrutiny.

AI systems work the exact same way. Just with math instead of culture.
Computer vision models learn what "normal" looks like from training data.

--> Facial recognition systems learn who appears "suspicious" based on historical patterns.

--> Anomaly detection learns what behaviors to flag based on what past incidents looked like.

The museum guards looked directly at the thieves and categorized them as "safe." An AI system trained on the same environment would have done exactly the same thing.

And in the enterprise, we continue to repeat this same pattern:

🔹 We train AI on workflows that are already broken
🔹 We encode subjective judgments as objective rules
🔹 We mistake pattern detection for comprehension
🔹 We scale our blind spots at machine speed

The result?

AI misses what matters.

We're teaching machines to see the world through our categories, our norms, our inherited assumptions about what "should" look like… and then we're surprised when it misses what's actually happening.

The thieves didn’t hack the museum
They hacked perception... human and algorithmic

So the moral of the story?

Before we can teach machines to see better, we have to question how we see.

Article in comments

When Deloitte gets caught… twice… every consulting firm loses.

Not long ago, an Australian government study valued at AU $440,000 had to be partially refunded after a researcher flagged fabricated quotes and non-existent academic citations. 

Now, a 526-page healthcare workforce report commissioned by a provincial government in Canada, paying nearly CAD $1.6 million, has been exposed as containing multiple false citations tied to made-up research and mis-attributed authors. 

This isn’t just Deloitte’s problem.

It makes every consulting engagement harder because now clients have to wonder… how do I know YOU didn't do this too?

Every time a consulting firm ships AI-sourced "research" or uses AI to create deliverables without verification, it chips away at what the industry is supposed to stand for... trusted expertise, careful analysis, and accountability.

Yes, LLMs are powerful. But they don't know. They don't reason. They don't fact-check. 

They statistically stitch words together.

And when you treat autocomplete as research, you get reports built on thin air.

That puts decision makers... leaders, boards, executives at risk of acting on faulty premises.

Which is why organizations now need to ask different questions before engaging consultants who claim to be "AI-enabled":

🔹 Show me your verification chain, not just the final slide deck.
🔹 Prove what was human judgment vs. machine output.
🔹 Disclose where AI was used… and how you validated it.
🔹 Tell me who is personally accountable if the AI gets it wrong.

Because trust isn't rebuilt by small footnote corrections.

It's rebuilt by partners who understand when AI helps, and when human insight and integrity are non-negotiable.

If you care about building strategy that lasts, you should expect nothing less.

Original Report: https://lnkd.in/gG7XpNub

Second catch: https://lnkd.in/gEk6C5Hn

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