Co-Created Launches Sense AI

When AI Gets It Wrong

In the Age of Instant Insights, the Real Competitive Advantage Is Knowing What to Trust

In my first job out of college, I worked as a trombonist in a rock band.  But when I finally made my parents happy and got a proper job later that year (rock and roll trombone is pretty niche, and I’m not that good) I worked in the economic research department at Brown Brothers Harriman. It was the kind of place where precision mattered—a lot. I would spend hours combing through capital flows data from Japan, eventually picking up the phone to call someone at the Japanese Ministry of Finance because I wasn’t sure I was interpreting their reporting conventions correctly. That’s what it took to get the data right.

Fast forward to today, and the idea of getting a polished market research report in seconds—courtesy of generative AI—feels miraculous. With tools like ChatGPT and OpenAI’s Deep Research, Deepseek and Claude’s expected upcoming release of a deep research model anyone can produce a sleek document filled with insights, charts, and stats. But here’s the question: in the age of AI, the research is fast—but is it real?

That’s not a rhetorical concern. As generative tools flood inboxes and decision-making meetings with confident-sounding “findings,” we’re entering a strange new era—one where everyone can create an “insights report,” but few can verify it. And the consequences for business, policy, and public trust are significant.

✦ The Mirage of AI-Generated Research

Benedict Evans recently documented his experience with OpenAI’s Deep Research. The tool generated a slick analysis of smartphone adoption in Japan—complete with citations. The only problem? The data was wrong. Key statistics were pulled from outdated or misinterpreted sources like Statista and Statcounter. How wrong? It doesn’t really matter because the end result was a report that looked authoritative but couldn’t be trusted.

This is more than a footnote in AI’s evolution. It’s a cautionary tale. Most large language models (LLMs), including ChatGPT, aren’t retrieval systems—they’re probabilistic engines. They generate the next likely word based on patterns in training data. That can mean they’re pulling from outdated or irrelevant data sources. Or worse, misinterpreting the data entirely failing to understand the nuance of what a dataset actually represents. Yet the results are presented in polished prose, with an air of confidence that makes errors nearly invisible. 

For consumers of information, this creates a strange asymmetry: the outputs feel credible, but the underlying logic is opaque. It’s a bit like getting stock advice from someone who sounds like Warren Buffett—until you realize they’re just guessing.

And here’s the real danger: unless you’re a subject matter expert, you won’t know what’s been misrepresented because you won’t even know what to question. The mistakes aren’t always obvious. They live in the assumptions, the framing, the fine print. If you don’t already understand the topic deeply, it’s easy to take the AI’s answer at face value—and that’s exactly when it’s most likely to mislead you. What you’re left with is research that sounds right, feels right, and might be right—but that you have no way of verifying without deep domain knowledge. That’s not just inefficient. It’s dangerous.

✦ Getting it Right

At Co-Created, we encountered this problem firsthand. We were using generative tools to speed up internal research, but we kept running into the same wall: we couldn’t trace anything. Outputs changed when we re-ran the same prompts. Citations disappeared. We couldn’t answer basic questions like, “Where did this data come from?” or “Why did the AI say this?”

The good news is that all the getting it wrong, led us to eventually get it right. Instead of chasing sleek one-off outputs, we wanted something that could reliably support business decisions.

A better solution is an AI-powered research tool designed for structure, traceability, and auditability.  Here’s how it can work differently:

• Deterministic Outputs, Not Just Free-Form Text

Sense builds repeatable workflows with structured prompts and data scaffolding. That means it’s not just hoping the AI gets it right—it’s designing for correctness.

• Smart Data Objects, Not Blobs of Text

Sense extracts key primitives—like a problem definition, a customer need, or a competitive insight—and tracks them individually. This enables chaining insights together over time, rather than getting isolated soundbites.

• Full Context Reconstruction

Instead of dropping raw documents into a prompt, the tool should reconstruct and organize relevant content across multiple sources, ensuring the AI model sees the full picture before responding.

• Audit Trails and Source Provenance

Every insight must link back to its origin—whether it’s a public filing, a competitor website, or a user-uploaded artifact. That makes verification easy, and hallucinations much less likely.

• Multi-Model Optimization

ChatGPT relies on one model. A good tool needs to use many—selecting different models for natural language processing, embeddings, or specialized analysis depending on the task.

• Custom Outputs Built for Business

From investor memos to quarterly reports to spreadsheet data dumps, the tool needs to deliver structured, exportable formats that match how teams actually work.

That’s why we built Sense. It’s not just another AI tool—it’s a research system built for teams that need to move fast and get it right. Because in a world where everyone can generate insights, the real edge is knowing which ones to trust.

✦ The Bigger Picture: What We Lose When We Trust Too Quickly

There’s a reason I remember that call to Japan’s Ministry of Finance. It wasn’t about one data point—it was about accountability. When you’re making decisions that affect people’s jobs, investments, or strategies, you need to know what’s real. And knowing means being able to trace back, challenge, and revise—not just consume and move on.

Generative AI isn’t going away. Nor should it. Tools like ChatGPT are invaluable for brainstorming, summarizing, and sparking ideas. But when it comes to research that informs action, businesses need to ask: What are we trusting, and why?

As the AI wave accelerates, the organizations that win won’t just be the ones who use it fastest. They’ll be the ones who build trust into the process—who can separate the insights worth acting on from the noise that just sounds good.

Reach out to start a conversation.

It’s our job to understand the disruptive trends that we are seeing in the market and help our clients and partners spot opportunities for growth. As part of our Field Notes series, our partners share what they heard at key industry events.

Last week, Daniel Shani attended a private dinner hosted by Harman (a subsidiary of Samsung), in collaboration with GP Dinners. The event featured Siddharth Garg, NYU professor and leading AI and cybersecurity researcher, who shared insights on the future of AI, and brought together a small group of innovators, investors, and strategists for an exchange of ideas around the future of AI and digital transformation.

Here are some of the key takeaways and forward-looking insights that surfaced:

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1. The Energy Challenge of AI at Scale: 

Garg remarked on the unseen energy costs of AI. Every AI interaction—whether it’s a chatbot query, image generation, or model training—demands significant processing power. It’s hard to estimate exact energy consumption (based on the model, query, etc) – a single AI-written response can consume as much as 0.14 kilowatt-hours (kWh) of energy, comparable to keeping 14 LED bulbs lit for one hour. Globally, data centers already account for about 2% of total electricity consumption. Garg posed the thought experiment: If every Google search today were powered by generative AI models, the energy consumption behind those searches (est. 700+ TWhs per year) would more than double data center energy consumption. 

However, Garg argued that scenario is unlikely – given we’re already seeing smaller, more efficient models published, and he believes the world is headed toward highly verticalized models tailored for specific tasks. Specialization and optimization are not just nice-to-haves—they’re existential necessities for a sustainable AI future. How will your company adapt?

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2. Specialization as the Next Growth Frontier

Even as AI models grow more capable, the interim period—where models are very good, but not yet perfect—offers a major strategic window.

The best opportunities (and greatest returns) will likely go to those who:

• Specialize: Build and deploy more focused, task-specific models and agentic workflows rather than relying on generalized large models or singular systems.
  
• Leverage Proprietary Data: Own unique data assets that can give their specialized AI solutions a durable competitive advantage.
  
• Dominate Verticals: Focus deeply within specific industries where precision and domain expertise matter most.
  

This is where innovation, speed, and tight problem-market fit will create outsize returns. How and where can you carve out your specialization?

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3. Private Equity’s Appetite for Growth: A Playbook for Innovation

The dinner also surfaced pointed insights about PE risk appetites over time based on fund lifecycles:

• Pre-Acquisition: Value-creation (i.e. growth) strategies are a critical ingredient baked into diligence and deal terms.
• Immediately Post-Acquisition (Years 0–2): PE firms show the greatest appetite for speculative, growth-oriented strategies. This is the window where they are most receptive to fresh ideas and new initiatives to drive growth and multiples.
• Mid-Hold (Years 3–5): Focus shifts toward execution, hitting numbers, and preparing for an exit. Innovation becomes harder to prioritize unless directly tied to short-term returns.
  
  

Overall, there was general consensus that the larger the PE firm/fund, the more established the execution playbook was – down to preferred vendors, value-creation strategies, and timelines. Perhaps smaller growth equity players may have more appetite for new innovation partners and more disruptive value-creation strategies? 

In this vein, some innovative service providers to PE firms are successfully implementing risk-aligned models—covering the upfront cost in solutions in order to capture the majority of upside through savings or growth gains immediately post-acquisition (where PE firms are less sensitive to bottom line) and a decreasing share of upside over time (as PE firms are maximizing metrics for exit). How might similar models open new doors for venture builders and strategic advisors in your field?

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If you need help solving a complex problem, building a new solution, or unlocking new ideas for good growth, reach out to the team.

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Originally published by The AI Journal on April 26, 2025

Too many companies are racing to define their “AI strategy,” as if artificial intelligence is some new business function that needs a dedicated lane. But the real opportunity isn’t about what you can build for AI—it’s about what you can unlock with it. The smartest organizations aren’t rewriting their playbooks from scratch. They’re using AI to accelerate, enhance, and sharpen the strategies they already care about.

This isn’t about replacing fundamentals. It’s about getting more leverage on the things that already drive impact.

Here are four core pillars of business strategy that are being transformed—not replaced—by working with AI.

1. Keep a Live Pulse on the Market (and Make it Actionable)

Every company tries to track what’s happening around them—competitor moves, emerging customer needs, shifting cultural signals. The problem is, most of that happens sporadically, with a heavy reliance on manual analysis, anecdotal insight, or high-level macro indicators.

AI changes that. Today, intelligent systems can sift through thousands of unstructured sources—Reddit threads, local news, LinkedIn posts, investor decks, product reviews—and convert that chaos into structured, directional insights. You’re not just reading content or collecting data; you’re mapping the market in real time.

The added value? These insights aren’t buried in a quarterly report—they can be delivered to the right teams at the right time. Some companies are even building “living” models of their market environments–constantly refreshed, customized by audience, and embedded into everyday workflows. The outcome is a strategy that doesn’t just respond to change—it contextualizes and anticipates it.

2. Elevate the Value Proposition (Not Just the Toolset)

AI can certainly enhance tools and automate tasks. But its real power shows up when it prompts a deeper rethink of how you create and deliver value.

Take, for instance, a healthcare brand that initially set out to build a product recommendation chatbot—something smart and lightweight that could guide customers to the right supplement or service. As the project progressed, the team realized the same underlying personalization engine could support onboarding, behavior change, educational nudges, and even care team handoffs. The chatbot didn’t just improve customer support—it became a doorway to reimagining the entire experience.

That kind of pivot isn’t about chasing the next tool. It’s about looking at your business through a different lens: now that we can personalize at scale, how else might we create a deeper, better relationship with the people we serve?

3. Experimentation is King (and Now You Can Do It Smarter, Faster)

One of the most powerful shifts AI brings is speed—not just in output, but in learning. Traditional experimentation takes time. You come up with a new message or offer, build the assets, run a test, wait for results… and often learn too little, too late.

AI changes the rhythm. With synthetic data and intelligent agents, you can prototype narratives, simulate reactions across segments, and generate tailored campaigns at a pace that was unimaginable a year ago. (Personal note: I’m old-school in some ways—I still love hearing directly from real people out in the world. But AI can help with that too: transcribing interviews, summarizing themes, even surfacing sentiment you might have missed.)

This shift is already reshaping creative and go-to-market teams. We’re seeing the rise of “vibe marketing”—a parallel to the “vibe coding” movement that gave us platforms like Replit, Bolt, and Lovable. Just as one developer with the right tools can now build and ship a new product in hours, one marketer with the right AI stack can 100X their output– e.g. spin up landing pages, test angles, generate collateral across channels and automate end-to-end workflows with speed and precision.

Emerging tools like PhantomBuster, Jasper, and OpenChair are enabling highly specialized, niche automation for media testing, competitive tracking, and persona-driven messaging. The direction is clear: fast, lightweight, focused systems that do one thing really well. The agency of the future might be one smart person and a “room” full of purpose-built agents.

4. Execute Better, Faster (With Tools You Design)

Every organization wants to move faster and reduce friction. But it’s not just about automating more—it’s about customizing tools that work the way your teams do.

In some forward-leaning companies, teams are building internal libraries of GPT-style agents tuned to specific workflows—from customer service to product research to compliance. In one example, a growth-stage startup built over 100 internal agents, each supporting a specific business process. More importantly, the functional teams themselves drove the design—flagging tedious repetitive tasks, brainstorming better flows, iterating on what worked, and benefiting from the AI leverage.

The result? A culture of active optimization, where AI isn’t imposed top-down, but developed ground-up in service of the work that actually needs doing. Building smarter tooling became everyone’s job.

And the long-term effect? Less internal drag. Fewer handoffs. More time focused on creative and strategic thinking—the stuff humans are still uniquely good at.

Reality Check: You Still Have to Change (Just Not Everything)

Of course, working with AI doesn’t mean business as usual. Some shifts are non-negotiable:

• Teams need to build new muscles—prompting, interpreting results, and course-correcting rapidly.
  
  
• Strategy has to move from static planning to continuous, feedback-fed evolution.
  
  
• Data systems must become more integrated, so insight and execution aren’t siloed.
  
  
• Proprietary advantage will increasingly depend on how companies use, integrate, and learn from their own data. Closing the feedback loop—between what your AI outputs and what actually works—creates better results, better models, and better strategy.

In short: the fundamentals stay, but the game speeds up. The teams that win will be the ones that can adapt in-flight, not just in the offsite.

Conclusion: Build with AI, Not for AI

The companies getting ahead right now aren’t the ones spinning up isolated AI pilots or innovation labs off to the side. They’re the ones embedding AI into the heart of what they already do—understanding their market better, elevating the customer experience, iterating faster, and executing with less drag.

You don’t need an “AI strategy” that lives apart from the rest of your business. You need a strategy that uses AI to get sharper, faster, and more responsive. Don’t build something for AI. Build something better with it.

Originally published by The AI Journal on April 24, 2025

The discourse around AI in education often lurches between panic and hype; will it replace teachers, is it the end of thinking, is it a revolution? But in classrooms like ours — at Hudson Lab School, a project-based K–8 program just outside New York City — the conversation is less dramatic and more iterative. The question isn’t whether we should be using AI. The question is: how do we use it well?

At HLS, we’ve spent the last year treating AI not as a separate curriculum or policy mandate, but as a tool integrated into the daily work of learning. We’ve tested it in capstone projects, used it to support differentiated instruction, and introduced it in teacher workflows. We’ve experimented with a range of tools — ChatGPT, NotebookLM, Runway, Inkwire — and collaborated with entrepreneurs from our studio, Co-Created, to bring emerging AI applications into the school environment.

This article is a field report of sorts: a look at what’s actually working, where the challenges lie, and what we’re learning about the practical role AI can play in a real school.

Prompting as the New Literacy

In 2024, AI is already in the hands of students. A recent international survey showed that 86% of students report using AI tools in their academic work, with nearly a quarter engaging daily. Among American high schoolers, the numbers are even more striking — especially in writing-heavy disciplines like English and social studies. 

Yet, the level of fluency with these tools varies widely. Most students know how to ask a chatbot for help. Far fewer know how to interrogate its answers, challenge its assumptions, or build a productive back-and-forth.

At HLS, we’re treating prompting as a new core literacy — a set of metacognitive practices that help students engage with generative systems effectively and responsibly. We’re not teaching (and definitely not allowing) students to use AI to write for them. We’re teaching them to use it to learn with them.

We begin by introducing basic prompt structures in low-stakes contexts — not to produce polished work, but to explore ideas. Students might ask ChatGPT to act as a thought partner while planning their writing assignments, to generate study questions and flashcards based on their own notes, or to offer alternative perspectives on a historical event. Some use it to role-play different scenarios or help them look at multiple sides of an issue. Others prompt it to quiz them on concepts they’ve been struggling with, or to explain the same topic in multiple ways. 

Because each student is engaging the model individually, with prompts tailored to their needs, the interaction becomes deeply personalized — a kind of one-on-one tutorial that adjusts in real time to the learner’s questions, interests, and level of understanding. These early interactions aren’t about getting the “right” answer. They’re about developing the habit of thinking with a tool that responds. Over time, students stop seeing AI as a vending machine and start treating it as a dynamic, imperfect collaborator — one that helps them test ideas, surface blind spots, and stretch their thinking.

When these skills are integrated into real projects, the results are both creative and rigorous.

Student Projects: AI as Amplifier and Provocation

Take, for example, the project one of our eighth graders developed as part of their capstone — actually building a working beta version of a service called “back in my day” which allowed people to converse with individuals from their family tree. The idea emerged from a convergence of personal interests: genealogy, digital memory, and the fact that our school is co-located with a senior living facility. The student wondered: Could you build a system that allowed people to “talk to” deceased relatives by simulating their personalities, speech patterns, and stories? 

He started with family documents and oral histories, then used a combination of tools — including ChatGPT for linguistic modeling, ElevenLabs for voice generation, and a custom prompt scaffold we co-developed — to create a beta version of a persona-simulating chatbot. What started as a technical experiment quickly turned into an ethical inquiry: Should we do this? What does it mean to simulate someone’s voice, or story, or opinions? He also explored what kinds of implications this could have for grieving people and would it be positive or negative for the users. 

This wasn’t a sidebar project. It became a capstone: deeply personal, technically sophisticated, and intellectually provocative. And AI was at the center of it — not doing the thinking, but provoking more of it.

In another example, a sixth grader exploring the U.S. Constitution asked whether AI itself might gain personhood by 2075. Her culminating project was a simulated presidential election featuring AI candidates, designed and animated using Runway. She created original scripts, recorded performance footage, and prompted the tool to render campaign videos. What could have been a speculative gimmick became a lens for discussing democratic values, personhood, and rights — all refracted through the emerging reality of AI’s social presence.

These aren’t hypotheticals or case studies from a lab. These are middle schoolers using real tools to ask real questions about their world — and the one they’re inheriting.

Teachers as AI Practitioners

The shift we’ve seen in our teaching staff over the past year has been just as important — and in some ways more surprising — than the changes among students. When we first introduced generative AI in professional development sessions, the response was cautious. Some teachers saw the tools as gimmicks. Others viewed them as a threat to their professional identity and many simply didn’t see how they could be relevant to their day-to-day work.

That changed when we moved from theory to practice. As soon as teachers were given space to experiment — with support, without pressure — attitudes began to shift. They started using generative AI tools not to replace their planning, but to extend it; one teacher used ChatGPT to create differentiated reading materials from a single anchor text, adjusting the prompt to produce versions for different reading levels. Others began using it as a thought partner — brainstorming project ideas, writing prompts, rubrics, and alternate ways to explain tricky concepts. The emphasis wasn’t on perfection, it was on getting started.

NotebookLM received a lot of early attention. Teachers uploaded their weekly notes and used the tool to generate podcast-style audio summaries to accompany classroom newsletters. It was a small experiment, but an impactful one — parents reported actually listening, and it helped deepen the sense of connection between home and school.

We’ve also started piloting Goblins, an AI math tutor developed by an entrepreneur in the Co-Created network, to explore how AI might support individualized instruction in more structured subjects. It’s still early, but we’re already seeing promising signs of how targeted practice and real-time feedback can supplement classroom instruction.  Particularly interesting is how adaptive AI can be to students' different learning approaches and needs, allowing teachers to be more differentiated and personal in their approaches to teaching.  

And then there are the quiet surprises. I remember logging into an administrative view on one platform and seeing dozens of lesson plans that had been built out — not because we had mandated anything, but because teachers had simply started using the tools. They weren’t announcing it. They were just doing the work.

Platforms like Inkwire, which support the design of interdisciplinary, project-based units, have also made a noticeable impact. Teachers report spending less time searching for ideas and more time adapting and refining them — because the foundational materials are already generated. The result isn’t generic AI-driven curriculum. It’s curriculum that reflects the creativity of the teacher, accelerated by the scaffolding these tools provide.

What’s made the biggest difference, though, is targeted support on prompting. Not “how to use AI,” but how to ask better questions. How to engage in a productive dialogue. How to refine and reframe. In our sessions, we treat prompts not as commands, but as design tools — ways to push the model, and the teacher’s own thinking, into new territory. When used that way, generative AI becomes not just a productivity booster, but a source of professional inspiration.

What We’re Learning

The value of AI in the classroom, as we’re seeing it, is not about automation or efficiency. It’s about acceleration — of thought, of design, of iteration. When used well, AI tools help both students and teachers move more quickly from idea to prototype, from question to debate, from concept to execution. And, crucially, they help surface new questions that wouldn’t otherwise be asked.

But it only works when the culture supports it. At HLS, we’re fortunate to have a school structure — interdisciplinary, project-based, agile — that allows us to experiment in real time. We also benefit from our work at Co-Created, where we collaborate with entrepreneurs building the next wave of AI-powered tools for learners and educators. That cross-pollination is essential: it keeps our thinking fresh, and it ensures that our practice is informed by the frontier, not just tradition.

Final Thoughts

AI in schools is not a yes/no question. It’s a how/why/when set of questions — and the answers will vary. What’s clear from our experience is that meaningful integration doesn’t start with policy. It starts with practice. With students experimenting, with teachers testing, with school leaders asking, week by week: What worked? What didn’t? What’s next?

That’s how we’re approaching AI at Hudson Lab School — and so far, what’s actually working isn’t the tool itself. It’s the mindset that surrounds it, especially as we know that the AI we’re using today is the worst AI we’ll ever use. 

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Co-Created Launches AI-Powered Intelligence Platform to Assess Business Growth Opportunities 

Today, we’re announcing the launch of Sense, an AI-powered market intelligence platform to help strategy, product, innovation, and growth teams answer complex questions, identify market shifts, and drive executive-level decision-making—faster than ever.

Sense was initially developed by Co-Created to supercharge our venture building teams and reduce the time, investment, and resources needed to validate growth opportunities and bring new scalable solutions to market.

Unlike traditional research tools, Sense combines public, premium, and private data—everything from press releases and financial filings to social media posts and online video reviews to internal research reports, transcripts, and team notes. Sense gives users control over which data sources to use (and trust) and structures all this information to surface key patterns, detect shifts, and frame decisions based on an organization’s strategy and real-world goals. Leveraging 15 years of venture-building expertise, Sense's reasoning engine and analytical workflows deliver smarter decisions faster. 

Every insight is supported with clickable sources for full transparency—not just summaries, but structured, defensible analysis designed for executive use. Final outputs and reports are customizable, on-brand, shareable slides, memos, or reports.

How It Works & Why Sense is Different

• Data Sources Appropriate for Big Strategic Questions: Maintain control and choice over the data sources included in the analysis to ensure you get high-quality, trusted and traceable insights. Easily add or remove data sources to enrich findings, and combine disparate unstructured inputs to inform more comprehensive perspectives. 
• Differentiated Insights You Can Prove: Use AI to synthesize massive amounts of data and detect meaningful trends, surface anomalies, and highlight the most relevant insights tailored to specific strategic objectives. Sense provides full transparency and auditability throughout the process to preserve root sources and evidence (i.e. rather than a black box, Sense offers a “glass box” your compliance teams will appreciate – double-click anywhere along the process to investigate citations and go deeper). 
• Decision-Ready Outputs, Built for the Boardroom: Receive structured, formatted outputs that distill complex findings into clear, actionable intelligence for leadership teams. Sense creates customizable, shareable branded outputs in the layouts and filetypes most useful and digestible to your team (i.e. no need to copy and paste text outputs into separate presentations).

Rich Wilding, Partner at Co-Created, shared, “AI-powered tools are rapidly becoming embedded in the day-to-day operations of corporate teams. But adoption often remains fragmented— many organizations use AI to automate small, discrete tasks rather than to transform how they think and act at a strategic level. Sense is built to bridge that gap. Instead of focusing only on efficiency gains, it’s designed to expand the way teams approach strategy, investment, and growth. It doesn’t just automate research; it accelerates understanding, reduces blind spots, and ensures that leaders are seeing the full landscape—not just the part that’s most visible.”  

Sense is now available to select partners and teams. Learn more or request early access at sense@co-created.com. 

The best way to predict the future is to sense it and respond before anyone else does.

Click here to see the press release.

It’s our job to understand the disruptive trends that we are seeing in the market and help our clients and partners spot opportunities for growth. As part of our “Field Notes” series, our partners share what they heard at key industry events.

This summer, Stacey Seltzer helped lead a collaboration between Hudson Lab Ventures and Unicorn Factory Lisboa. Here are some of the key perspectives on entrepreneurship and innovation in the broader education ecosystem and how large corporations can play a critical role:

Unlocking Potential: How Co-Created is Empowering Portugal’s Youth through Innovation

Fostering entrepreneurship and innovation among the next generation is not just beneficial—it’s essential. At Co-Created, we work with our partners, testing ideas to build new products and companies. We understand that nurturing a pipeline of talent,  equipped with the skills and mindset necessary to navigate and shape the future is foundational for business success.  That’s why we partnered with Hudson Lab Ventures and Unicorn Factory Lisboa and progressive companies like José De Mello, to develop the Innovation Summer School program; an initiative that’s making a significant impact on the entrepreneurial landscape in Portugal.

Real-World Challenges, Real-World Solutions

The Innovation Summer School is not your typical educational program. It’s an immersive experience where high school students are given real-world challenges from leading companies and tasked with developing innovative solutions. These aren’t hypothetical scenarios; these are genuine problems that these businesses face, and they’re looking to the students for fresh, creative perspectives.

Last year, for example, we partnered with NOS and its youth brand WTF, asking students to rethink what a phone proposition should look like for young people after they were forced to completely change their youth offerings due to changes in EU regulations. The results were nothing short of impressive. The students didn’t just meet expectations—they exceeded them, offering insights that challenged conventional thinking and provided actionable solutions.

This year, our focus has shifted to tackling some of Portugal’s most pressing societal issues, including education and inequality. José De Mello has been at the forefront of this initiative, providing students with the opportunity to address these critical challenges head-on. Students in this cohort wanted to get real-world experience that would be applicable when they started their careers.   Team Boost, developed a concept for creating opportunities for students to gain experience volunteering or working with companies to expand their skill set and be better prepared for the workforce, and the team has gone beyond the scope of the program to start building it on their own this summer.  Others focused on the application of technology to the classroom like team Edu Know who designed a concept for an analytics platform to help teachers track grades, identify students’ weak points and offer helpful resources. 

Building 21st Century Skills

At the heart of the Innovation Summer School is the development of 21st century skills. The program is designed to equip students with the tools they need to thrive in the modern world. This includes:

• Creativity and Collaboration: Students learn to work together, leveraging diverse perspectives to create innovative solutions.
• Product Development and Design Thinking: Through hands-on experience, students gain a deep understanding of the process of bringing ideas to life.
• New Tech Tools: From AI platforms to cutting-edge design tools, students are exposed to the latest technologies, preparing them for the future of work.

These skills are not just theoretical—they’re put into practice as students work in teams to tackle the challenges posed by our partner companies. The result is a dynamic learning experience that is both engaging and impactful.

A New Perspective on Entrepreneurship

One of the key aspects of the Innovation Summer School is the fresh perspective that students bring to the table. Uninhibited by the “it doesn’t work like that here” mentality, these young innovators approach problems with an open mind and a willingness to explore new possibilities. This has led to some creative ideas that have not only impressed our partner companies but have also shown students that entrepreneurship is a viable and exciting path. Below is direct feedback on the program from some participating partners.

“The program both produced and practiced awesome educational principles–the  students were so clearly passionate about their topics and did a phenomenal amount of work in just two weeks! I can’t wait to see what comes of these ideas.” Parth Sarin, Stanford Digital Education

“This program is not only about innovation. It is about an experience that will prepare the next generation with key competencies that will be able to transform our society’s problem solving, peer-to-peer behavior, flexibility, creativity, and emotional intelligence. Let’s expand it and together create today a better tomorrow for all!” Joana Maia, Brand, Communication and CSR Senior Manager at SONAE SGPS

The Impact

The impact of the Innovation Summer School extends beyond the classroom. Students leave the program with a newfound confidence in their abilities and a deeper understanding of the entrepreneurial journey. They’ve learned that with the right tools and mindset, they can make a real difference in the world.

Our partner companies have also reaped the benefits. The innovative solutions generated by the students have provided valuable insights and actionable ideas, demonstrating the power of youth-driven innovation. As João Mil Homens from Unicorn Factory Lisboa remarked in a recent LinkedIn post, “The energy and creativity brought by these young minds is nothing short of inspiring.”

Looking Ahead

As we continue to iterate on the Innovation Summer School program, we’re excited to see where the journey takes us next. The success we’ve seen so far is just the beginning, and we’re committed to further empowering the next generation of entrepreneurs in Portugal and beyond.  Together, we can build a brighter future, one innovative solution at a time.

For those interested in learning more about our work or in getting involved, please feel free to reach out. Is there a challenge that your team can’t unlock? We love solving problems. 

If you need help solving a complex problem, building a new solution, or unlocking new ideas for good growth, reach out to the team.

Field Notes from Startupfest 2023: Insights and Inspiration from Montreal

For the second year in a row, I had the privilege of attending Startupfest in Montreal, one of the most community-focused tech events I've experienced. Last year, I spoke about the various models and ways venture studios deliver value plus reconnected with some fantastic folks. This year, I was thrilled to return as a mentor.

What's notable about Montreal's environment, especially after recently writing about the Brooklyn tech ecosystem, is how distinct it feels. A Canadian investor asserted that while U.S. founders often tell grander, more compelling stories, Canadian founders tend to feel they need to be more grounded in actual accomplishments. I thought that was an interesting insight and kept my ears open. At every event, I was struck by the depth of experience of all the speakers and mentors; but I also noted that so many of the founders were off and running, operating young but growing businesses. There were almost no entrepreneurs there slinging good pitches based on vaporware and jargon. That investor was onto something. FWIW Startupfest’s organizers have been deeply embedded in tech for years, and it shows in the community they’ve built. Programming reflected an incredible interplay of technology and societal trends over decades, with conversations led and facilitated by luminaries.

Several big ideas kept surfacing during the event, and I wanted to capture three that particularly stood out to me:

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1. Truth in AI

There was a fantastic panel about Truth in AI. The discussions really got me thinking about the implications of hyper-enabled humans with bionic powers (aka the Bionic Consumer to gather, synthesize, and apply vast amounts of information to decisioning. The critical question becomes: Is the information I'm using true, and where did it come from?

Maybe best poised to tackle this question are folks like Shingai Manjengwa and her team who are reimagining applications of Blockchain to solve this problem. We need smart mechanisms for consensus to govern – not just monetary provenance—but ALL IP. Its promise is to help us with a simple question: Do I believe the information or transaction I’m observing is real? Do I understand its origin? As AI agents operate on our information, trust in AI itself and its conclusions becomes paramount. As more of us turn to AI for perspective and interpretation and then turn around to pass them along…how will the next person trust us? Our judgment in AI selection? Our sources? This need for trust and transparency will only grow as AI continues to evolve.

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2. Evil Enough

Startupfest organizer and OG startup vet Alistair Croll and rockstar brand strategist & marketer Emily Ross, co-authored a book called "Just Evil Enough," which rethinks marketing for the attention economy. They explore how certain individuals find loopholes and hack systems, like Farmville's meteoric rise to 72 million users or Kraft Mac & Cheese selling a million boxes a day. The book analyzes and deconstructs various playbooks that can help us rethink marketing strategies to discover advantages and ultimately hack the system.

For me, the fascinating part was discussing with them where the lines were? How far is too far? And maybe most importantly to me, how might we subvert these tactics for positive impact. Often, I talk about “hacking” companies to help those companies do good while making money, in spite of themselves. 

I wonder if there's something around “good enough to be great”...collabo for next book y’all? 

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3. Champions, Not Charity

I had the great fortune of sitting next to Deborah Price, a member of the Board of the National Indigenous Fisheries Institute (NIFI). The Institute aims to promote national consistency and standards across Indigenous programs and practices related to fisheries, aquaculture, oceans, and aquatic management. The Board of NIFI includes various regional and national Indigenous executives who collaborate with communities, regional organizations, and governments to enhance the potential and benefits of these programs for Indigenous peoples across Canada. She had just spoken about sustainable business and connecting First Nations people to the broader more sustainable economy. Her unambiguous statement to me when I asked her what she saw as the biggest opportunity for impact: “I came to do business and make deals”

Not charity. Not handouts.

I missed her on stage but our convo was a masterclass. Deborah's focus on sustainable participation—empowering communities that have been commercially and economically marginalized—was poignant. She spoke about giving communities not just a seat at the table but the means to build their own tables, define production methods, and engage in deal-making, monetization and their own community reinvestment. This resonated deeply and aligns with my belief that doing good and good business need to become indistinguishable. Targeting new markets aimed at serving traditionally commercially marginalized populations is a sizable business opportunity.

In Summary

Startupfest in Montreal was an amazing experience once again, filled with incredible community, a vibrant ecosystem, and fresh, diverse perspectives. I'm grateful for the opportunity to participate and hope to continue to build bridges and portals between NYC and Montreal.

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Big shout outs to Rebecca, Alistair, Suchi and the whole StartupFest crew! I’m already looking forward to next year.

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Hi there,

What if I told you that Primary Banking, as we know it, was dead?

Since the old days, the “gold standard” for growing a consumer banking businesses has been relationship banking.

Become the one institution a household primarily relies on. Win outsized share of wallet. Easy to understand. High ROI if you got it right. Primacy for the win.

The playbook was simple:

1. Deliver decent onboarding: Make it easy to walk into a bank and open a primary checking or savings account. Incentivize connecting that account to payroll, and get direct deposits every two weeks.

2. Create consistency: Provide seamless coverage across products with a skilled account manager and a good CRM (customer relationship management system). Cross-sell into higher margin products and services like credit cards and wealth management.

3. Remain relevant: As customers approach major milestones (Getting your first car? Buying a house? Saving for college?) put relevant offers and rewards in their path and cross-sell into a multi-line relationship.

Done right over time, you’ve increased loyalty, earned sticky deposits that provided a low-cost source of capital to grow a lending business, and transitioned a single account holder into primary banking relationship.

But, a few things have changed that are making it harder to predictably win “primacy.” Perhaps even more concerning for institutions, is the possibility that idea of primacy itself may be gone for good.

So what’s changed/changing?

Competition and fragmentation of deposits

A near-zero interest rate environment and explosive FinTech innovation created an unprecedented level of competitive pressure on every part of banking over the past 15 years. Almost overnight, consumers could send money faster, borrow more cheaply and — even with small balances — be offered superior interest rates just for opening up new deposit accounts. Ultimately, while most consumers did not move all of their money to challenger banks and digital wallets, a shift in willingness to have multiple relationships did occur. From new banks, to payment apps, to embedded finance players, consumers suddenly had lots of new places to stash deposits.

More informed consumers with real needs

I recently wrote about the rise of the “bionic consumer” in the context of the shifting power dynamic between providers of commodity services and their customers. One big takeaway was that it’s now easier than ever for customers to understand and optimize all of their purchasing decisions:

Do I have the right financial products (rate, reward, cost, service, values, etc) given what my needs are today?

The even bigger takeaway is that consumers will be able to passively optimize their financial lives; their AI money “agents” will hunt, compare, and analyze choices and changes in the market for them 24/7 across a huge set of factors and dimensions.

Better informed customers navigating a challenging economy will mean more demands on every provider and a greater portion of deposits up for grabs.

Open banking and money servers

As US regulators establish the particulars of what open banking will look like, one thing seems clear: much like in the UK, the advent of open data and seamless account-to-account (A2A) money movement will likely increase fragmentation and impact share of dollars kept in the “primary banking” relationship unless that provider is serving up the very best rates.

How?

1. Ease of A2A money movement
2. Rules-driven (aka algorithmic) money movement based on risk appetite, timing of different cash flow needs and yield

In the future your average family may break *all* of their money up into buckets they need to access over 15, 30, 60 and 90 days+, with AI “re-balancing” their money all the time to optimize.

Sounds a lot more like web servers than static financial deposits, doesn’t it?

While this future of an always on, always optimizing version of consumer banking might not be a reality tomorrow, it feels almost certain that financial institutions (FIs) of all sizes will no longer be able to rely on steady paychecks (we didn’t even touch the explosion of 1099 income) into a direct deposit account as a predictor of primacy.

So what, then? A new time calls for a new playbook.

Enter the  “Command Center” strategy: Become the very first place consumers turn to when they are even thinking about what to do with their money.

The Command Center strategy has a few key elements that banks will need to address:

• From walletshare to mindshare: Moving from simply being a payment method or a place to store money to being a trusted partner and co-pilot
• Lead with help: To be a partner, banks will need to create value in non-traditional, non-transactional parts of the journey. Don’t wait until a person is ready to buy a house to compete for their attention with rate and rewards… Give them tools and experiences to make and manage their long term buying plan.
• New kinds of partnership models: Consumers have a large and increasing number of FI relationships. Rather than try to get those consumers to switch completely in service of the traditional primary relationship, smart institutions will leverage their institutional platforms to convene partners and create robust experiences; capturing commercials even when they don’t fully own the journey end to end.

We’re all going to need to learn new tricks to serve tomorrow’s customers in a dynamic market with new rules:

“I didn't come here to tell you how this is going to end. I came here to tell you how it's going to begin.” - Neo

You ready?

Cheers,

Ron J Williams

Partner at Co-Created, Empire Startups Contributor

Contact our team to start a conversation!

The Assignment

Citi Ventures is the ventures and innovation arm of the bank. The team's remit is to help catalyze innovation and look for opportunities to accelerate growth. Spotting new and emergent trends and understanding the behavioral, structural and technological changes that will impact the business is part of the mandate.

The product development team wanted help to build a business case and launch a digital first product that addressed a financial pain point for families.

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The Solution

Starting with ‘Modern Family’ as the foundation, Co-Created kicked-off the ideation process with a week-long intensive workshop with specialists from the consumer bank product and tech teams, external subject matter experts, and seasoned tech founders. Co-Created then did a customer discovery deep dive – bringing in real people (consumers) to hear directly what challenges they were facing and what services they wanted from their bank.  These sessions, complemented by prior research, helped our team create a half dozen family 'archetypes' to broaden our exploration of problem categories, market segments, and consumer needs, in order to ideate across a range of potential solutions. In short order, our process facilitated and forced prioritization to land on the top three concepts. At the end of the week, we delivered three high-fidelity pitch decks (“Lean Product Plans”), each one for a discrete, disruptive concept, one of which was a way for co-parents to seamlessly manage and track shared financial expenses related to their children.

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Co-Created then worked with the Citi team to drive the in-market validation process. This first phase we call “signal mining” – a sprint (in this case 10 weeks) designed to help us get closer to customers and validate the critical assumptions underpinning the concept, primarily which problems and value propositions resonated most with different target customer segments (co-parents in this case). Following this phase, with strong signals (validation) around concept resonance and cost per lead, Co-Created moved to the MVP phase in which they designed, built and launched an iOS app in the app store under independent (non-Citi) branding within roughly two months. Over the following four months, Co-Created iteratively launched marketing tests and product designs to progressively improve key metrics. The product launched first as a simple free collaboration tool for co-parents. Despite intentionally not building a fully featured app with money movement, Citi saw unprecedented monthly engagement per user compared to its own mobile app.

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Outcomes

• 4,000+ downloads in the first two months after launching with limited marketing. 
• Executive support for a spin out in 2021 – Onward. 
• Onward then went on to raise over $13 million across its Seed and Series A fundraises, including investment from the Citi Ventures fintech team
• A new playbook for the Personal Banking business

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How can we help you?

Contact our team to learn more

It’s our job to understand the new and emerging signals we are seeing in the market and to help our clients and partners spot opportunities for growth. As part of our “Field Notes” series, our partners share what they are seeing and hearing at key industry events plus their💡quick thoughts.

Co-Created Partner, Ron J. Williams attended the Brooklyn Founders and Funders event during NY #TechWeek at the Domino Sugar Factory in Williamsburg. The event brought together founders, operators, and investors to hear about the latest trends and enabling tech that are driving innovation across the ecosystem. Event hosts included Elana Berkowitz of Springbank VC, Aubrie Pagano from Alpaca, and Elliott Robinson of Bessemer Venture Partners, among others.

Below is just a snapshot of what he heard on the ground (and, as always, his hot takes) –

1. Is Brooklyn in the House? Without a doubt. ;) The Brooklyn venture ecosystem is thriving! There was an incredible diversity of venture focus from health and wellness to fintech, sustainability, learning and development, adtech as well as a healthy number of folks thinking about what I now call “famtech” (solutions focused on easing the burdens around raising a family, providing for a family and keeping everyone (including yourself) healthy.
2. Impact, impact everywhere. I was excited to hear about the unapologetic interest in commercial ventures that improve the human condition. I got to join several conversations about how parents are navigating the process of getting advice and/or diagnoses for children who may be displaying learning or developmental differences, and the incredible challenges around that. I also got to hear about some of the incredible work the PINE Program team (a venture out of NYU) is doing at the intersection of building more inclusive classrooms (for kids of all kinds with learning differences, starting with ASD) and the K-12 professional development (“PD”) ecosystem. Fun fact: we spend more than $10B a year on PD in US public schools even though methods for training teachers haven’t been updated in 40 years. 
3. Keeping it Real..Estate: I talked to several founders and Commercial Real Estate professionals who are actively reimagining the future of built space; esp inside an evolving work ecosystem. One of the more interesting questions that kept coming up was around trends on residential vs commercial space. Folks keep wanting to move here to *live* but in a post-covid world, space needs to be flexible in terms of usage and ease with which it can be reconfigured. One founder was talking about leaning into the idea of “3rd space” (i.e. not home, not office but other places you access as needed) as experiential. Instead of the utility of office space, might I wind up exploring the city differently if I could plan where I worked around local delights? Maybe even with my girls on daddy-daughter office days (read: camp or school out and I’ve got no childcare), we can turn work trips into adventure time.

In summary, as a guy who almost left Brooklyn in 2008 to head out west because I wasn’t sure there was enough vital ecosystem energy, it feels like we’re just getting started. The future of building the future is distributed. It’s not on one elite road in California where a bunch of VCs hung a shingle over the past 50 years.

It’s wherever the people, problems and community of intrepid problem solvers meet. And Brooklyn is for sure one such place. We’re just hitting our stride.

Stay tuned for more from the field!

If you need help exploring, unlocking and doubling down on the future, reach out to the team.

The Assignment

HP Labs excels at R&D and the broader organizational machinery is incredibly efficient at scaling operations around existing products. However, the executive team wanted help accelerating pipeline activity and bridging the gap in their commercialization process between R&D and compelling commercial opportunity. Several projects had been in various stages of development for years, without a clear path to market. Co-Created stepped in to help identify and prioritize the strongest use cases for driving organic growth and new revenue streams and developed a repeatable process to commercialize new IP as it emerges/matures.

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The Solution

Our team bridged the gap between R&D and market success by helping the executive team align on new opportunities, increase agility, and go to market with anchor customers. The engagement kicked off with technical presentations by the R&D leads, and continued through a structured, phased approach to surface a wide variety of potential use cases and hone in, with increasing levels of validation, on compelling commercial opportunities. Co-Created facilitated working sessions with the R&D teams and executives, conducted extensive market research and discovery, and engaged with dozens of industry experts and prospective customers through its own outreach across the use cases identified. 

To help prioritize use cases and force decisions throughout the engagement, we leveraged a scorecard, consensus voting, and templated use case summaries and business cases. The scorecard, for example, was customized to HP Labs commercial and strategic criteria, and enabled the collective team to evaluate dozens of potential use cases in a structured way – looking at evolving market dynamics, shifts in consumer behaviors, the size of the opportunity, technical feasibility, and the competitive landscape. The ultimate goal was to narrow in on the few most compelling opportunities that had strongest alignment to HP Labs’ strategic priorities, biggest market opportunity, and clearest signal of demand from large, qualified customers.

The three most impactful capabilities Co-Created brought into the process were (1) digesting and translating technical descriptions into market-ready propositions, (2) bringing structure and granularity to evaluating use cases and the criteria that matter most, and (3) spearheading market outreach and direct customer engagement. 

Co-Created engaged directly with the research teams, over several working sessions, translating their ‘technical’ info into market-facing value propositions and critical assumptions to be tested further when surfacing business use cases and validating customer interest. Prioritization, through scorecard evaluation and use case ranking, was based on factors such as market size, potential for differentiation, alignment with the company's strategic goals, and the feasibility of development and commercialization. To engage prospective customers and validate use cases, we leveraged our global network of corporate clients and subject matter experts, as well as third party platforms and direct cold outreach, to find the right people in market. We iteratively built up our understanding of and point of view on the use cases, digging deeper in each phase of the engagement as we progressively narrowed our list to the prioritized few.

By building out the detailed business cases with direct input from prospective customers, Co-Created helped cultivate relationships and build credibility with those customers – making way for an organic transition to negotiate and sign Letters of Intent (LOIs) with those customers for large scale pilots. This step was about moving from theory to action, translating strategic planning into tangible partnerships and projects. Finally, we handed off the projects in flight to HP Labs’ corporate development and supporting teams. 

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Outcomes

Within four months, the HP Labs team was able to advance from the exploratory stage to customer validation, working with several qualified partners on projects in flight. The HP Labs team built conviction around the prioritized use cases, as well as those opportunities not worth pursuing. This rapid progression from early thinking to actionable plans for commercialization underscored the fast paced, structured process that Co-Created applied to R&D commercialization.

The overall process was incredibly iterative, data-driven, and collaborative. It involved constant feedback loops with the market and the HP Labs teams, ensuring that every decision was backed by solid market-based feedback and validation and had internal buy-in. The ability to sift through dozens of potential use cases and pinpoint the most and least viable ones demonstrated a strategic clarity and focus that can be lacking in traditional R&D processes.

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How can we help you?

Contact our team to learn more

It’s our job to understand the disruptive trends that we are seeing in the market and help our clients and partners spot opportunities for growth. As part of our “Field Notes” series, our partners share what they heard at key industry events.

This week, Daniel Shani, attended the ADRP: The Association for Blood Donor Professionals annual conference with our client, Delcon, to share more about our collaboration and to help increase engagement with blood centers.

Here are some of the challenges the industry is facing and some forward-looking solutions that are driving progress:

1. As you’d expect – when a patient with medical bleeding or severe trauma is in transit to the hospital, every second counts. Life saving blood transfusions are given at the hospital, and travel time delay with EMS often has dire consequences for patients. Some leading organizations are collaborating to test making blood available on EMS vehicles (ambulances and helicopters) and are already demonstrating impactful results.
2. There is a big imbalance in the diversity of the US donor pool. Over 70% of donations come from caucasian males. As little as 2% of donations come from African American donors. The diversity of the donor pool is super important for a variety of reasons, but especially for conditions like sickle cell disease, primarily affecting African Americans, where the donor’s genetic likeness to the recipient is critical for effective transfusion. Alayna Maxson and her team at Solvita are pushing boundaries and seeing results, nearly doubling their participation by African American donors in a 2 year timeframe. They followed a rigorous social science research framework to really understand their audience, and translated their findings into direct community engagement, debunking of common misconceptions and perceived risks, and tailoring of incentives and operating hours to best meet the community’s needs.
3. Lots of prospective donors drop off during the online booking process and there are lots of innovations around finding ways to make it more convenient for blood donors to get the information they need, including the use of new technology and AI. As estimated 35-40% of donors that begin the appointment booking process, drop off before confirming an appointment. That represents a huge amount of effort and resource to engage a donor that does not ultimately convert into a much-needed donation to maintain blood supply levels. New solutions range from AI chat bots to white glove service concepts, aimed at delivering a better donor experience and presenting the right information at the right time, all while reducing the level of effort and input needed by short-staffed blood center teams.
4. ✨Bonus✨ New line we heard and loved: What’s the rock in your shoe? We often ask about problems keeping people up at night. This is a different flavor that really effectively gets at a wider range of day to day issues – constraints slowing down your team, blocking step function improvement, or sapping people’s motivation.
   

If you need help solving a complex problem, building a new solution, or unlocking new ideas for good growth, reach out to the team.

I’m a partner at Co-Created, one of the few “venture builder” firms specializing in helping large organizations make big, strategic bets on building better futures (aka “good growth”). That means I spend a LOT of time thinking about how we deliver real value. Not just ideas, workshops, and decks full of theoretically sound recommendations, but real value. Real progress. Real outcomes.

In this rapidly changing world, the needs of companies, their customers and the world as a whole are shifting at warp speed, which means those of us who bring the “outside-in” must continue to evolve to meet corporate partners where they are at…with more than billable hours and advice.

How should we do that? Why do I think this way? Strap in…

(Easter egg at the bottom for music fans)

State of the advice business

Last weekend, I found myself with a rare hour alone (I have two little girls), engrossed in a Wall Street Journal article on the current state of management consulting. The headline: we are seeing a seismic shift in the industry. For the first time, leading firms are not just downsizing their workforce but are also parting ways with partners, a previously unheard-of practice. A 2022, a report cited that the management consulting market had grown to $973.67 billion in spend globally, reaching a fever pitch during the pandemic. So what changed in the last two years?

Rising interest rates, fears of looming recession, and an uncertain geopolitical climate has caused global enterprises to scale back investment in external “for-hire” expertise and change management. The environment is now very different from the record-setting years of early pandemic when large enterprises poured billions into consultant coffers to get help imagining transformation in an all-of-a-sudden remotely working, masked, terrified and economically dislocated world. Now, more companies are taking the reins themselves, increasingly choosing to internalize the monumental tasks of envisioning their futures and driving transformation.

This shift reaffirms a long-held belief of mine that the management consulting role and business model are rooted in a bygone era: One in which the pace of change in business was slower. Before software began “eating the world” and the information revolution brought us not just the world’s information in our pockets (thanks internet, qualcomm, apple, google) but now the means to critically analyze that information in seconds (thanks AI).

Management consulting’s core value proposition historically was about professionalizing ‘management’ itself: 1) helping good leaders consistently make great decisions in service of shareholder value and 2) managing complexity through rigorous research, analysis, and benchmarking to well-understood standards. The best do it so very well. And truly, some of the brightest and most successful executives I’ve encountered in business are former consultants.

The consultants aren't the problem. The model is.

The management consulting model is at its best when change can be easily dimensioned and predicted, based on historical precedent. But the accelerating pace of disruption over the last 30 years has demonstrated that exponential change from novel sources is impossible to accurately predict from the safety of your desk. Advice based on models does not cut it. You have to get your hands dirty building the future to really understand the future.

💡When access to expertise and critical analysis was hard to come by, establishing and standardizing frameworks that could then be packaged with advisory was brisk business.

💡So was becoming the trusted partner for optimization and financial engineering that signaled professional management, efficiency and fiscal responsibility. The street loved it all.

However, as the world adjusts to a pace of change that only continues to accelerate, the traditional consulting model of dispassionate, research-based opinion falls short. Why?

Because at some point providing opinions without having "skin in the game" or direct accountability for execution and the long-term success of the organization, leaves the "client" short.

In a breakfast of bacon and eggs, the chicken is involved but the pig is committed.

Tell me something I don't know

A former colleague at a large company once said to me that consultants are paid millions “to take your watch and tell you the time.” More generously IMO, management consultants often serve as highly effective tiebreakers, lending their reputation and brand weight to confirming what clients may already suspect, rather than challenging clients to explore less familiar, riskier, unventured paths.

This all hit home for me and our work at Co-Created when a corporate partner, who genuinely valued our collaboration, referred to us as "the best consultants" they had ever worked with. Great vibes to be sure, and yet I found myself internally stuck on the label. Consultants. Who, me? Us?

That moment led me to explore what truly differentiates a partner from a consultant. We at Co-Created consider ourselves “partners,” and “operators,” not consultants. Why? Because partnership is not about delivering a presentation and walking away; it's about ownership of outcomes, about being as invested in the success of an initiative as our clients are. It's about rolling up sleeves and doing the doing—opportunity identifying, venture building, growth fostering, co-creating—activities that go beyond advice and counsel. It’s getting in the trenches at all levels of the organization; digging in with the business leads, testing with marketers, building alongside the PMs, not just behind closed doors in the boardroom. We embrace risk, we innovate, we deliver—not just once, but repeatedly, iterating beyond the initial idea to truly meet the market and its real-world complexities arm-in-arm with our corporate partners.

Why "Partner" mindset?

We believe our role is to de-risk the future for our partners, transforming the first good idea into an even better reality through rapid experimentation and testing in the market. Our work with leading organizations across industries goes beyond advisory to actively participating in the creation and operationalization of real-world offerings;  often paving the way for these solutions to be internalized, built, and launched at enterprise scale by our partners.

Co-Created is composed of founders, operators, and builders who understand the gravity of taking risks for long-term growth and are willing to build that future alongside our partners. Our commitment is to not just predict but to actively shape the outcomes alongside our partners. In this rapidly evolving business landscape, our mission is to help companies boldly build better futures - delivering value beyond this quarter’s earnings call.

We don’t simply sell advice or time. We de-risk. We develop. We deliver. Beyond the deck. We value learning through experimentation, focusing on the right problems and moving fast because we know the only way to navigate uncertainty is to do.

So for those of you who maybe aren't familiar with how we use venture building to unlock impactful new growth and then double down, reach out. Always happy to chat.

For now let me quote the inimitable Jay Z:

Allow me to reintroduce myself.

My name is Ron J Williams, partner at Co-Created, and we are not consultants.

Reach out to start a conversation.

I came across a fantastic twitter thread about fully automating personal finances leveraging GenAi and I paused. While applying AI to to finances may seem like a simple extension of long building fintech trends, I see some bigger implications: specifically three intersecting themes that may permanently change the relationship between tech-enabled customers and the companies seeking to serve them.

Better-informed customer, with nearly free access to limitless analytical power (not just search but *summary and synthesis*) and the ability to automate previously un-automatable tasks (like drafting legal-sounding documents to get money back from providers) is a game changer. Customers will know more, expect more, and be able to seamlessly change providers.

We’ve never before seen this level of customer empowerment in the history of capitalism and companies are not ready.

So how should businesses think about what's coming?

📈 1. Rise of the informed customer: Faster & Smarter

Your customers know more today than they did yesterday. More about their fitness, their relationships, their screen time, their money, and even that new bop on Spotify (thanks AI DJ X!); and they’re putting this knowledge to use. What’s more, anything they don't understand is only a google search, social post, swipe on their For You Page or ChatGPT prompt away.

This means even before they’re paying customers, consumers are also getting better at understanding how effective your business is at serving them and their community. They're beginning to see how you (and your competitors) make, spend and invest money plus how much value you pass on to them; which in turn tells them whether or not they should be (or continue to be) your customer.

Companies will have a harder time hiding behind fees and recurring subscriptions they hope people forget about. In fact consider this: "Hidden" penalties & fees will no longer be able to hide. Hard-to-understand fine print with unfair terms will be dragged into the daylight, dissected on hashtag#finTOK and amplified into the ether.

🧠 2. Decision Support for all: a.k.a. Kayak and Credit Karma for everything

The internet + AI leveraging Large Language Models to distill the world's knowledge into well-summarized, synthesized information = highly actionable, super-optimized recommendations for all of us.

Remember what it was like to buy plane tickets in ye days of olde when there was no incentive for carriers to share details on pricing anywhere except in channels they controlled?

Remember trying to figure out different ways to fly somewhere while staying within a budget? Kayak changed that. Not only for the customer but also, by looping search and purchase intent back for carriers and hospitality; creating new ways to compete more dynamically for customer dollars. In so doing, they changed expectations around the table and ushered in a new era of customer choice.

We've watched something similar begin to happen in consumer finance with personal finance management apps ("PFMs") entering the market en masse to assist with everything from budgets to tracking subscriptions to automating savings via "Round Ups".

Certainly better than being on your own with no plan but still too much work. Like paper maps.

And then over the past couple of years we started to see upgrades. Tools sitting across multiple accounts, aggregating and analyzing everything together at a high level; updating you in real time for changing conditions (i.e. changes in income, daily spend, etc). Definitely better than paper maps to help you get to the location you specified…but nothing helping you figure out if you’ve even picked the right destination and then making a plan.

In 5 more years I suspect we’ll look back at those very same personal finance apps and think of their aggregation and clunky tagging of expenses as quaint. Moreover, we’ll realize they weren’t doing the real job: protecting us from bad decisions and helping make better plans. We’ll chuckle at the fact that even with all those all tools consumers still used to blindly sign credit card, rental car and lease agreements without understanding the fine print; or reflecting on how the average customer used to negotiate with businesses and fight through customer support all on their own.

Being able to look across millions of documents, billions of clauses, and thousands of transactions in order to optimize for specific personal goals (and ways to achieve them) is not something the average customer can do. In contrast, AI-enhanced bionic customers will be able to see the Matrix of possibilities and easily answer questions like:

• "Which subscriptions should I keep given my usage and overall expenses?"
• "When and what should I sell if I need a down payment in 18 months?"
• “What percentage of my assets should I hold in cash and stock for the next 12 months based on our current household spend and how should I spread it around to maximize for safety?”
• “Should we stay in the city or move to the ‘burbs given the age of kids and the current job market??”

They will make better choices all day, every day. And many businesses will lose customers in droves, until they adapt.

Understanding patterns at scale to map optimal strategic personal choices will be a game changer in the power dynamic between customers and providers.

The result: For financial institutions, insurance providers and any industry with very little differentiation at the product level (i.e. a checking accounts is a checking account) companies will be auditioning for their roles every single day, 24 hours a day. Understanding, aligning, and evolving with your customers will become your best competitive advantage. This will look like differentiated services and experiences that haven't even been imagined yet.

🤖 3. Time for some action: Data + Optimized choices + Automation = 🤯

If "Decision Support for all" is about helping customers understand and make complex choices, this last trend is about overcoming the Action Gap -  the distance between what you should do and what you actually do to follow through.

Action Gap = the distance between what you should do and what you actually do to follow through

Today a customer may intend to dump you for another company with better rates, or spread their deposits around based on something they heard or read about FDIC. But the amount of friction involved in fully researching competitors, calling customer service to switch accounts, or retaining counsel to claw back an erroneous charge might just feel like too much. So they forget about it and move on.

Decision support services will take the next big jump beyond showing you your choices to acting on those choices on your behalf. When they do, that action gap will go to zero...for everyone.

Slowly at first but as regulatory pressure on big banks and big tech increases requirements for making customer data portable AND usable, there will be a big shift in not only what AI is allowed to read (i.e. analyze) but also write to (i.e. take actions like moving money).

AI-enabled “analysis and action” automation will guide customers through their interactions with most providers in near real-time overseeing basic functions like where to keep money across all accounts; asking for input only when faced with higher stakes or unfamiliar patterns.

It’ll make day-to-day financial management blissfully boring for most people…and be a huge challenge for institutions stuck in the slow-moving past of dissatisfied customers staying put because of inertia.

The result will be like a Level 5 self-driving EV that can sense when you need to take over before you do...but for your money.

What might that look like? Maybe Wealthfront. Maybe SpendFriend.AI.

In a world where AI knows stuff like:

• Literally ALL of the very best rates across checking, savings, CDs and other short term debt instruments
• Your cashflow patterns and risk tolerance
• Your desire to buy a home within the next 24 months (but lack of a plan)

……your money will drive itself and tell YOU where to go.

Your deposits will flow seamlessly from one account to another optimizing for the right combo of time-based availability and risk-adjusted returns. You’ll never again let a company that overcharged you keep that $20 just because you're too busy to sit on a call for 30 minutes. You won’t even be the one calling. Your AI will handle it for you.

Most businesses will need to reconsider drivers of long term growth to address this empowered, bionic customers who sees right through them and expects more.

Final Thoughts and a challenge question

How will smart businesses cultivate loyalty in an infinitely optimizing world of bionic customers? I believe they will:

• Start to focus on a new kind of “command center” strategies (i.e. be the starting point in a customer journey)
• Lean into building trust via radical transparency around data and business models
• Prioritize customer success and advocacy for customers (which will require knowing a lot more about what they really need...and committing to actually serving them)

And that's just the beginning...

This may be the biggest shift of power in the customer-provider dynamic we’re going to see in our lifetime.

What are you doing to get ready for the Bionic Customer? What experiments are you running? What are you learning?

Tell me what you think. Push back. Share examples of how this is playing out in your world.

If this is particularly interesting to you, drop me a note. I’ve spent years in this problem space and am always happy to chat about what we’re seeing in our work at Co-Created helping organizations figure out bolder, better futures.

Link to original twitter thread

It’s our job to understand the new and emerging signals we are seeing in the market and to help our clients and partners spot opportunities for growth. As part of our “Field Notes” series, our partners share what they are seeing and hearing at key industry events plus their💡quick thoughts.

Ron J. Williams attended Day 1 of the The Wall Street Journal Future of Everything conference which covered the economy; healthcare; higher education; relationships; the future of work; immersive entertainment, and more. Thanks for the invite Katherine Finnerty!

Below is just a snapshot of what he heard on the ground (and of course, his hot takes) –

Chuck Robbins, the CEO of Cisco, said that the government will have to play a more involved role in AI through regulation and prepare to be adaptable and flexible as the technology advances.

💡Will the body that regulates AI need to depend on AI?

Bayer CEO, Bill Anderson, shared his company’s plans for dynamic shared ownership that will cut layers of bosses, reduce rules and eliminate some jobs in an effort to give employees more autonomy to decide how they spend their time.

💡For this to work, does there have to be outsized upside? (Very similar line of thinking as Netflix’s focus on “talent density”)

The President of Dartmouth, Sian Leah Beilock, shared her thoughts on the need to create “brave spaces” (vs “safe spaces”) for dialogue and dissent

💡Exploring and building the future requires space for risk-taking and discomfort. But how do we decide how much is too much?

John Mackey, former CEO of Whole Foods, doubled down on his belief that profit and purpose are intertwined and that every business starts with value creation for customers.

💡Purpose provides clarity and a long-term orientation.

Last, Tracy B. from Kanbrick shared her approach to long-term investing and partnering with clients to grow their businesses and deliver returns.

💡Partnership over transactional relationships everyday. Her mindset reminded us of the article that Ron published last month – The Partnership Paradigm.

While there were dozens more nuggets of wisdom (kudos for a great Day 1 WSJ!), the ones above particularly resonated  with our playbook for fostering “good growth.”

In a nutshell, resilient organizations are  “getting in reps” on managing  ambiguity, taking risk, reimagining key assumptions, better serving all stakeholders (not just shareholders), and partnering to take action.

Watch this space for more from the field!

If you need help exploring, unlocking and doubling down on the future, reach out to the team!

The Assignment

Blood donations in the US have historically been low, but the situation is getting worse. According to the Red Cross, throughout the last 20 years the number of people donating blood has dropped by 40%. A number of factors are contributing to this: the active blood donor pool shrinks as donors “age out” of the donation process, given that the average donor age is high. The pandemic put an added strain on the blood supply, with blood drives still not reaching their pre-pandemic levels. “Traditional” marketing and tactics that proved effective in the past are getting to be cost prohibitive, resource intensive and have diminishing returns.

The donor experience - from recruitment and engagement, to the technology used, resources shared, or the type and frequency of communications - needed to be entirely rethought. Yet, doing so would prove to be incredibly difficult as blood centers are short-staffed, their technology is constrained and access to data is limited, and these tasks, let alone revamping them, are very time consuming and resource exhaustive.

Delcon has enjoyed a proud legacy as a manufacturer of regulated medical devices for the blood industry since their founding in Italy in 1981 and remained focused on bringing the best hardware to the market, both through direct manufacturing and distribution partnerships. Yet for their next phase of growth - and with growing market challenges to confront - they recognized two major opportunities: expanding their business beyond hardware to building new capabilities in software, data, and automation, and growing their presence in the US market. With a bold, ambitious CEO at the helm, they are actively investing in and pursuing both.

Centered on increasing their focus on innovation - including by co-developing new hardware with customers - they had a variety of compelling ideas for the future (and even early concept designs for some), yet outside of hardware didn’t know where to start and what to prioritize.

They partnered with Co-Created to advance their innovation agenda by bringing discipline and a structured process to their work, leveraging seasoned entrepreneurial talent, and gaining an external partner committed to long-term outcomes.

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The Solution

Initially, Delcon’s strategy and hypothesis was centered on the recognizable gaps in their existing market - like helping to attract younger donors - as well as new ideas around engagement  - like a gamified digital platform to give donors badges and rewards for their donations.

While finding some evidence to support those pain points in the existing landscape, Co-Created quickly identified a larger and more pressing problem for the industry by engaging directly with market experts and prospective customers: Converting one-or-two time donors into habitual and recurring donors. Activating blood centers' existing donor base, and using their own data to do so more effectively to increase donor retention and engagement, presented the biggest opportunity to increase overall donations.

The result of a phased discovery, validation, and ultimately prototyping process was the launch of two new digital products (as well as a backlog of other concepts and opportunities and a lot of actionable learnings about customer needs). Co-Created drove the process end-to-end - from conducting initial discovery research and ideation, to bringing onboard designers and developers, to engaging in customer development with blood centers, to deploying and running pilots in the market.

The first product is a mobile-first web application that helps reshape the donor experience and combines a modern booking engine, marketing automation, and analytics dashboards. The new platform makes it easy and  intuitive to book donation appointments on the donor’s own terms. The platform gives donors more control around the communications and reminders they receive before and after donating, a step-change from the status quo today in which donors often feel “spammed” by the blood center and reach the point of communication fatigue. The result is closer, more trusted and personalized relationships between donors and donation centers. Furthermore, this product sets a foundation for future expansion to an end-to-end DRM (donor relationship management) and booking tool for blood centers who don’t have any systems in place today.

The second, OpenChair, is an AI-powered donor recruiting platform that delivers hyper-personalized communication strategies at scale to drive successful donor outcomes. The product was developed within just six months and is now the cornerstone of the business’s 5-year growth strategy. OpenChair combines machine learning, data science, and generative AI to enable blood centers to understand and segment their donors, personalize communications at scale, and get a lot more output (workload and results) with the same size team.

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Outcomes

Tests with several of Delcon’s US partners have yielded remarkable results. Communications sent by one of the largest community blood centers in the US using OpenChair saw a 24% increase in appointments among inactive donors and a 35% increase in donations from frequent donors compared to traditional methods used during the same period. This could potentially result in tens of thousands of additional donations annually for each center.

In addition, OpenChair’s showing at the ADRP industry-leading annual conference generated strong interest from a number of blood centers, with several requesting immediate access to the platform and service to run their own trials.

Importantly, the adoption of OpenChair led to a true inflection point for Delcon. Equipped with a new and tangible capability to offer blood centers, OpenChair empowered them to adopt a truly proactive sales and partnership strategy, knowing and confident that they had a truly additive and compelling proposition to offer centers. This continues to expand their pipeline and relationships across a vast and key market for the company. OpenChair also helped cement Delcon's presence in industry as a true innovation partner to blood centers, laying groundwork to do more co-development on other opportunities in the future.

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You can read more about our efforts in this Byline from Delcon's CEO, Barbara Sala: Boosting Blood Donation Efficiency: Tackling The Blood Shortage With AI - Healthcare Business Today

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How can we help you?

Contact our team to learn more