The CDS Fellows Program connects rising second-year MBA students with alumni, industry leaders, and each other, deepening their understanding of digital strategy and preparing them to lead at the intersection of technology and business.

Welcome to the T’27 class of MBA Fellows!

Maddie Beecher

Maddie is from Marlborough, MA and Madison, CT, and brings an unusually varied background spanning political consulting, nonprofit leadership, philanthropic investing, and presidential campaigns. She is interested in the creator economy, independent film distribution, media financing, and tech for social connection, and most looks forward to joining a community of peers and alumni who bring diverse perspectives to this complex moment in technology. She is really good at Mahjong.

Amos Cariati

Amos is from Providence, Rhode Island, with a background spanning philosophy, computer science, data analytics at Mastercard, and product management at a Series A tech startup. He is interested in the impactful use of AI, the societal impact of deepfake technology, and strategic business model design. As a Fellow, he most looks forward to engaging with peers who are deeply curious about emerging AI trends. He has played the violin since age four.

Hunter Clark

Hunter is from McLean, Virginia, and brings a background in entrepreneurship and digital product-building, having founded a travel tech startup, scaled operations at Indagare, and advised an audio startup on international expansion. At Tuck, she vibe coded a tool for a professor that she was later invited to present to the school. She is interested in AI-enabled problem solving, UX/UI, and startup tech, and most looks forward to connecting with CDS alumni and learning from her fellow Fellows’ projects. She has been to 47 states.

Gabriella Eberth

Gabriella is from Winchester, Massachusetts. Her experience with CDS has been a powerful way to bond over innovation while navigating uncertain times together. She is interested in AI, vibe coding, tech product management, startups, and climate tech, and is most excited to network with peers and alumni. She has a dog named Junie who made the move to Hanover with her.

Joe Guthart

Joe is from Los Altos, California, and brings experience as a project manager leading large cross-functional technology implementations. He is interested in health technology and wearables, the trajectory of AI, and the challenges of technology transformation, and most looks forward to discussions and debates about the latest tech trends with his peers. He plays the drums.

Tari Kandemiri

Tari is from Silver Spring, Maryland, and has been coding since she was eight years old. Before Tuck, she spent seven years at Accenture leading data and cloud transformations, and founded Hama Beauty, a skincare recommendation platform that grew to serve users in more than 55 countries. She is interested in digital transformation, consumer technology platforms, and AI, and is most excited to help her Tuck peers translate digital strategy into tangible, deployable applications. She was born in Harare, Zimbabwe.

Ariadne Kaylor

Ariadne is from Seattle, Washington, and comes to Tuck from a neuropsychiatric genetics lab at Massachusetts General Hospital, where she managed large-scale research projects involving complex patient health data. She is interested in how people understand and adopt AI, the ethical implications of AI development, and applications of emerging technology in healthcare. As a Fellow, she most looks forward to a space for critical, curious conversation about how technology is evolving in real time. She once entered a meatball eating competition and came in last place.

Adah Lindquist

Adah is from Wayne, Pennsylvania, and has enjoyed her time as a CDS Associate engaging in bi-weekly discussions that have challenged her thinking on business and emerging technology. She is interested in how organizations evolve their operating models and cultures in response to rapid technological change, and is excited to explore emerging AI capabilities through hands-on learning and collaboration.

Samrat Patel

Samrat is from North Stonington, Connecticut, and comes to Tuck from L.E.K. Consulting, where he advised PE clients on growth strategy and M&A with a focus on digital and consumer. He is interested in AI and consumer behavior, digital transformation, and platform strategy, and is especially excited to engage with the alumni network and help shape the 2026 Dartmouth AI Conference, celebrating 70 Years of AI at Dartmouth. While studying abroad, he sold watches online for a startup out of London’s oldest meatpacking district.

Cecily Power

Cecily is from Newton, Massachusetts. During her first year at Tuck, CDS gave her a space to connect with peers passionate about technology, sharpen her AI skillset, and learn from alumni leaders. She is interested in agentic AI, digital marketing, and product management, and is most excited to collaborate with fellow Fellows and execute her CDS Fellow service project. She is a twin.

Jen Spadaro

Jen is from Stonington, Connecticut. As a CDS Associate, she has had the opportunity to network with alumni, learn from industry professionals, and engage with classmates through the program. She is highly interested in how AI will impact publishing, from copyright to the creative process, and is especially excited to celebrate the 70th Anniversary of AI at Dartmouth at this fall’s AI Conference. A former ballerina, Jen now spends her personal time reading.

Seokmin (Alex) Sohn

Alex is from Seoul, South Korea, and brings experience in data center procurement and service as a South Korean Army officer. He is interested in AI and its implications for everyday productivity for both companies and individuals, and most looks forward to the Dartmouth AI Conference and being part of the CDS community. Before high school, he aspired to be a professional e-sports player, and was very good at it.

Simone Wasbin

Simone is from San Clemente, California, and comes to Tuck from Everlaw, a legal tech company, where she built AI-powered software for lawyers and launched a novel GenAI product. She is interested in agentic AI, tech policy, robotics, and enterprise transformation, and is most excited to get to know the other CDS Fellows and hear what they are each most passionate about. She set a record at her high school for juggling a soccer ball for nearly two hours straight.

This talented and diverse group is united by their passion for technology, eagerness to learn, and commitment to driving digital innovation. Their unique backgrounds and the mentorship of the CDS alumni network promise an enriching environment for shaping the future of digital strategies. We are excited to welcome our newest Fellows and witness and support their impact at Tuck and beyond!

About the CDS Fellows Program:

In 2000, thanks to the generosity of Ed Glassmeyer, Roger McNamee and others, the Center for Digital Strategies opened its doors to MBA students at the Tuck School of Business. The mission of the center has always focused on educating future business leaders on the enabling role of technology in modern businesses.

This year marks the 20th Anniversary of the CDS Fellows Program, a milestone that reflects two decades of students who have gone on to shape the digital strategies of organizations around the world.

Since our founding, more than 200 MBA students have benefited from the program, which has evolved to incorporate in-depth research and service projects, experiential learning trips, corporate research partnerships, case writing for Tuck professors, and customized learning opportunities tailored to each student’s interests.

The MBA Fellows program for second-year students works to expand upon students’ understanding of digital strategies and the impact of technology on the future of general management. Students are challenged to grow via hands-on learning opportunities, and are encouraged to build their networks and share their knowledge with the CDS community.

The program is highly competitive, with selection open to rising second-year students. Applications for the CDS MBA Fellows program are accepted in the Winter Term for the forthcoming academic year.

The post Introducing the T’27 CDS MBA Fellows appeared first on Center For Digital Strategies.

In mid-March, we traveled to San Jose for NVIDIA GTC 2026, the annual conference that has become one of the most important gatherings in AI. Over 30,000 attendees packed the San Jose Convention Center and SAP Center for four days of keynotes, panels, demos, and hallway conversations about where AI is headed next. As representatives of the Center for Digital Strategies at Tuck School of Business, we went to learn and came back with more questions than answers, which is exactly what a good conference should do.

Here are five themes that stuck with us.

1. The AI industry is pivoting from training to inference — and that changes everything

CEO Jensen Huang’s two-hour keynote drove home a point that should matter to every business leader: the AI industry is shifting from a world focused on training models to one focused on inference, actually running them at scale. NVIDIA now frames its data centers as “AI token factories,” industrial-scale operations optimized for producing outputs rather than just building models.

Why does this matter outside of a server room? Because inference is where AI meets the real world. It’s the moment a customer gets a response, a doctor gets a recommendation, or an agent completes a task. As this shift accelerates, the companies that figure out how to deploy AI effectively, not just build it, will have the advantage.

2. The bottleneck isn’t intelligence, it’s infrastructure

One of the most grounding conversations at GTC was a fireside chat between Google’s Jeff Dean and NVIDIA’s Bill Dally. Their core message: networking is the real constraint in scaling AI systems right now. It’s not that models aren’t smart enough. It’s that the pipes connecting everything together, the physical infrastructure, can’t keep up.

This extends beyond hardware. A recurring theme across multiple sessions was that most enterprise tools were designed for human speeds, but AI agents operate roughly 50 times faster. When an agent can process information in milliseconds but has to wait on a tool built for someone clicking through a menu, the tool becomes the bottleneck. This is a massive opportunity for software companies and a wake-up call for organizations deploying agentic AI: your infrastructure has to be rebuilt for machine speed, not human speed.

3. OpenClaw was everywhere at GTC, and agentic AI is just getting started.

If there was a single technology that defined the energy at GTC, it was OpenClaw — the open-source agentic AI framework that has exploded in popularity since its launch earlier this year. NVIDIA went all in, dedicating an entire bootcamp and maker space to OpenClaw throughout the week, and introducing NemoClaw, an enterprise-grade reference stack designed to make agentic AI safer for corporate deployment.

But the bigger conversation was about what agents mean for how companies operate. During an open-source panel that included Jensen Huang, one founder offered a striking framework: in any modern enterprise, there are three critical capabilities: executing code, accessing sensitive data, and communicating with external audiences. No single person should do all three, except the CEO. That’s a useful mental model for thinking about how to govern AI agents, which can theoretically do all three simultaneously and at scale.

The example of prior authorization from healthcare made this concrete. Today, when a doctor’s request gets denied by an insurer, someone has to manually gather data and file an appeal. This process eats enormous amounts of time and drives physician burnout. An AI agent can handle this workflow automatically, collecting the right data and aligning the appeal to the specific rejection reason. These aren’t hypothetical use cases. They’re in development now.

4. “LLM” might not be the buzzword for long: World models are the next frontier

Just as “LLM” entered mainstream vocabulary over the past two years, “world model” is poised to follow. Multiple panels and demos explored how AI is moving beyond language into models that understand and simulate the physical world — critical for robotics, autonomous vehicles, industrial applications, and creative tools.

Runway, which started as a video generation company, is a telling example. Anastasis Germanidis, Runway Co-Founder and CTO, gave a talk on Runway’s GWM-1 world model and why they believe video diffusion-based world models are the most direct path to general-purpose simulation. When a video generation startup pivots to world modeling, it tells you where the center of gravity is moving.

For business leaders, the implication is that AI’s impact is about to expand well beyond text-based knowledge work. The physical world is next.

5. Creativity is not being replaced, it’s being redistributed

Cara’s background in entertainment drew her to the panels on media, entertainment, and creativity. The conversation at GTC wasn’t about AI replacing creative professionals, it was about democratization and responsible use. Panelists discussed how these tools can lower barriers to entry for independent creators while enhancing the capabilities of established production teams.

The tone was striking. This wasn’t techno-utopianism or doom. It was working professionals in entertainment talking honestly about how to integrate powerful new tools without losing what makes creative work meaningful. For anyone navigating a career that sits at the intersection of content and technology, that conversation is essential to follow.

The real conference happened between sessions

Some of our most valuable moments at GTC happened not in keynote halls but on the exhibitor floor talking with founders, watching demos, and hearing how people across industries are thinking about AI in their own work. The energy was unmistakable: while the big companies dominated the headlines, it was the individual conversations about what this technology is and how people want to use it that will ultimately shape how AI shows up in everyday life.

GTC 2026 showed us an industry that is simultaneously maturing and wide open. Enterprise best practices are forming, world models are gaining momentum, and agentic AI is moving from concept to deployment. But companies are still very much figuring things out in real time, and the founders building the future are learning as they go. That combination of ambition and humility,  of progress and uncertainty, is what makes this moment so compelling to study, to teach, and to be part of.

Patrick Wheeler is Executive Director of the Center for Digital Strategies at Tuck School of Business, Dartmouth. Cara Harrington is a CDS Fellow conducting original research on AI adoption in professional settings.

The post Five Things We Took Away from NVIDIA GTC 2026 appeared first on Center For Digital Strategies.

The instinct, especially in business education, is to read this as a story about Ramp. A unique culture. A specific tech stack. An anomaly. That reading misses the point. The deeper insight in Charles’s playbook is that access is not adoption. Most companies that hand their employees an AI license and call it a day end up with a fraction of the leverage Ramp is generating, and they’re confused about why.

What Ramp built is the clearest demonstration to date of a shift I’ve been arguing for in my classes for the past two years: the move from analyst to builder. And the implication for how we educate the next generation of business leaders is more urgent than most of us are acting on.

The shift, restated

For most of the modern business era, the dominant professional skill has been analysis. Read the brief. Synthesize the inputs. Frame the recommendation. Hand it off to someone who can execute. MBAs are trained for this. Consulting is built on it. Most of corporate America runs on it.

AI breaks that model. Not because analysis stops mattering (it does), but because the marginal cost of analysis is collapsing toward zero. When a non-engineer at Ramp can stand up a working contract reviewer in an afternoon, the bottleneck is no longer “who can think clearly about this problem.” The bottleneck is “who can get something working in front of a customer or colleague by Friday.”

That’s the analyst-to-builder shift. And Ramp is living proof that when you remove the friction, building isn’t reserved for engineers. It’s available to anyone willing to develop a different set of habits.

Why the Ramp story validates our meta-skills framework

In the Bridge Program and across our MBA work, I teach three meta-skills as the core of what it takes to thrive in an AI-augmented economy:

• Deep Curiosity. Not the surface kind that knows AI is important. The kind that asks what question is actually worth pursuing — and then keeps pulling on the thread when the first answer is unsatisfying.
• Comfort with Ambiguity. The willingness to start a project without knowing the full solution, to sit with a half-built thing, to let the work teach you what it should become.
• Bias to Execution. The instinct to move from analysis to building, fast. To prefer a working v1 over a polished plan.

Source: Geoff Charles [@geoffintech]. “How to get your company AI pilled .” X, April 8, 2026, 6:12pm, https://x.com/geoffintech/status/2042002590758572377?s=20

Read Charles’s playbook with those three in mind and the alignment is striking. His proficiency ladder — L0 through L3 — is essentially a map of how those meta-skills compound over time. L0 is the absence of all three. L1 is curiosity without execution. L2 is the moment someone develops enough comfort with ambiguity to ship something that works. L3 is curiosity, ambiguity tolerance, and execution operating as a system.

The Ramp employees doing remarkable work aren’t the ones who took the best training. They’re the ones who got to what Charles calls the “aha moment” fastest, installed the tool, built something small, and felt the leverage on day one. That’s the threshold that matters. If a tool doesn’t deliver a real result in the first ten minutes of use, most people abandon it, regardless of how much potential it has. Everything that compounds at Ramp compounds from that first concrete win.

What this means for business education

For decades, MBAs carried a reputation as the people who analyzed the problem and recommended the action — and then handed it off. That reputation isn’t entirely fair, and it’s becoming less accurate by the semester. At Tuck, professors like Scott Anthony in Strategy now give students the option to write a traditional final paper or build a working prototype, app, or agent. The number of students choosing to build has grown sharply, and that’s worth celebrating. Our students want to build. The Center for Digital Strategies is leaning hard into that shift, and we’re not the only ones.

Analysis still matters. The ability to frame a problem clearly, weigh trade-offs, and reason from first principles is more valuable in the age of AI, not less. The shift isn’t analysis versus building. It’s analysis as the floor, with building as the new expectation on top.

The companies struggling right now are the ones that didn’t make this transition. A lot of the workforce reductions getting attributed to AI aren’t really about AI doing the work. They’re about organizations carrying too many people who only analyze — who frame problems and recommend solutions but never own execution. Ramp’s playbook is the inverse of that org: everyone is expected to build, and the people who do are compounding their leverage week over week. That gap, between organizations like Ramp and organizations still optimized for hand-offs, is going to define the next decade of careers.

Stanford research is already showing AI’s disproportionate impact on entry-level knowledge workers. The 22-year-old who can analyze but not build is the most exposed worker in the economy. The 45-year-old executive who delegates all building to others is closer to that risk than they realize.

A few specific things this should change about how we educate:

Hiring screens for AI proficiency aren’t optional anymore. Charles describes Ramp’s PM interview: build me a product, walk me through how you built it. Not a slide deck. An actual prototype. Tuck and every peer institution should be asking whether our students can pass that interview before they leave campus, because the companies they’re targeting increasingly will.

The harness matters more than the model. This is Charles’s most important insight, and it’s the one most educators are getting wrong. We spend a lot of time teaching frontier model capabilities and almost no time teaching people how to set up an environment where those capabilities are actually accessible. The MBA student who can quote benchmark scores but can’t get past npm install is going to plateau at L1 forever. The Claude setup mini-lesson I now run before my AI prototyping workshop is, in retrospect, the most leveraged 30 minutes I teach.

The L0 floor is rising fast. A year ago, occasional ChatGPT use put you somewhere in the middle of the workforce distribution. Today it puts you near the bottom. A year from now it will be disqualifying. Curricula built on last year’s baseline are already obsolete.

Building has to be in the room. You don’t develop bias to execution by reading about other people’s execution. The most important pedagogical move I’ve made in the past year is shrinking the time between concept and prototype to the smallest interval the room can tolerate. Forty-five minutes is enough. Two hours is plenty. The mistake is thinking students need a semester to be ready. None of this dismisses the legitimate questions faculty are working through about academic integrity and when AI use supports learning versus shortcuts it. Those questions matter, and the answer isn’t to keep building out of the classroom until they’re resolved. It’s to bring building in and let the pedagogy evolve alongside it.

What I’m taking away from Ramp’s AI approach

A few specific moves from Charles’s playbook that I’m thinking about adapting at CDS.

The skills marketplace concept — Ramp’s “Dojo,” with 350+ shared workflows — is a model for how a CDS Fellows cohort could compound their learning rather than each Fellow figuring it out alone. The first person to crack a workflow shouldn’t be the only one who benefits.

The discovery-through-emulation dynamic Charles describes is one we’re already building this spring. Lily McCarthy T’26 and Clara Delgado T’26, two of our CDS MBA Fellows, are leading a showcase of projects that Tuck students have built this year with the help of AI. The point isn’t to rank anyone. It’s to solve the blank-page problem — the moment a student opens a fresh AI tool with no shared examples, no peer workflows, and no sense of what’s actually possible from where they sit. Making the building visible reframes the question from “do I have to do this” to “what can I build.” Lily and Clara are doing exactly the kind of student-led work that compounds across a community, and I’m grateful for their leadership on it.

Shared context is the other half of the blank-page problem. I recently wrote about a four-part framework for how organizations should build the context documents that make AI genuinely useful inside their walls; the same logic applies to a school. We’re working this term on a baseline context document workshop for Tuck students that captures the frameworks they’re learning, the resources they have access to, and the way we talk about problems in the Center for Digital Strategies. The goal isn’t to hand students a finished artifact. It’s to give them a starting point and then teach them to extend it for their own work. Building a context document is itself a builder skill: it forces you to make tacit knowledge explicit, which is half the battle in any serious project. A student who can articulate what an AI needs to know to be useful in a strategy case has already done the hardest part of the strategy work.

The token budget question is an important question for educators and one I wouldn’t dismiss out of hand. Tuck doesn’t have an unlimited budget, and neither does any university or business school I know of — full token-maxing in the Ramp sense would bankrupt us many times over. What we can do is allocate meaningful token access to students doing serious AI work, treat it as an investment in their development rather than a cost to be minimized, and watch the spend carefully as use scales. The reframe Charles offers, that token consumption is rounding error against the value of a more capable person, is the right principle. The execution has to fit the institution.

The simplest lesson: just get started!

Charles ends his playbook with what he calls the simplest lesson: just get started. That’s the right note to end on here too.

Ramp didn’t have a master plan. They had a culture that valued speed, leadership willing to back bold bets, and a relentless focus on getting people to their first real result. The compounding did the rest.

Business education has the same opportunity. We don’t need a perfect curriculum. We need to stop teaching analysis as the terminal skill and start treating building as the baseline. The students and executives who develop those three meta-skills: deep curiosity, comfort with ambiguity, and bias to execution, will be the ones writing the next decade’s playbook.

The rest will be reading about it.

Patrick Wheeler is Executive Director of the Center for Digital Strategies at the Tuck School of Business at Dartmouth.

The post What Ramp’s 99.5% AI Adoption Means for How We Educate appeared first on Center For Digital Strategies.

Each year, the CDS Fellows tackle ambitious, self-directed projects that sit at the intersection of technology, business, and society. The T’26 Fellows are diving especially deep into artificial intelligence, product building, cybersecurity, creative technologies, and the future of human connection. What makes this year exceptional is the breadth of experimentation: students are not just studying technology, they are building with it, testing it, and exploring its implications in real time.

Below is an overview of this year’s projects, organized into thematic groups. Each project is listed with the student leading it, and reflects the kind of inquiry, creativity, and leadership that define the CDS Fellows experience.

Why These Projects Matter

Together, the T’26 Fellows are examining not just what AI can do today, but how it will alter the way we work, create, connect, secure systems, and build community. Their work positions Tuck at the forefront of exploring how technology reshapes business, with MBAs acting as both researchers and builders.

A Call to Action for Alumni CDS Fellows and Tuck Alumni 

Our Fellows thrive when they can learn directly from the experiences of Tuck alumni. If you work in these areas, or simply have relevant experience, you can make an immediate difference by advising or supporting a Fellow.

Areas Where Alumni Expertise Is Most Helpful:

• AI product development, prototyping, or engineering
• Cybersecurity, privacy, penetration testing, or AI safety
• Sales, RevOps, CRM systems, and GTM automation
• Generative AI, 3D modeling, XR, gaming, or media production
• Fintech, personal finance, or women’s financial empowerment
• Consumer apps, social products, or community engagement
• Startup building, MVP scoping, or early-stage user research

How to Get Involved

If you’d like to share your knowledge, mentor a student, or learn more about any project:

Email Patrick Wheeler (Executive Director, CDS)
patrick.s.wheeler@tuck.dartmouth.edu

We’ll match you with the Fellow whose project aligns with your expertise or interests. Your guidance strengthens the CDS community and helps shape the next generation of Tuck leaders working at the forefront of digital strategy.

T’26 Projects:

Sruthi Bayapureddy

AI Prototyping an Alternative Checkout Systems for Small Businesses

This project explores the feasibility of a QR-based pay-by-bank checkout system designed to help small businesses bypass high credit-card processing fees; an approach that may be especially well-suited to South Africa’s rapidly evolving fintech ecosystem and lighter regulatory environment. The student will conduct a comparative analysis of the U.S. and South African fintech landscapes, build a “vibe-coded” and partially functional MVP of the QR-based checkout experience, and perform customer research with South African small business owners to understand needs, adoption drivers, and friction points. Deliverables include a comprehensive market map, a product requirements document, the prototype itself, and a synthesis of product, market, and regulatory insights. The project will culminate in a candid overview of the student’s end-to-end experience—offering practical lessons for future builders navigating AI-assisted development, global market scoping, and early-stage product design.

Aerin Brown

AI Prototyping a Social Music Discovery Platform to Fight Recommendation Fatigue

Aerin’s project aims to build a community-driven music discovery platform that counters the “recommendation fatigue” many users experience on major streaming services and instead fosters shared exploration modeled after platforms like Strava and Letterboxd. She hypothesizes that listeners frustrated by repetitive or commercially biased algorithmic suggestions will embrace a social space where they can rate songs and albums, comment on what they’re hearing, follow friends, and receive more transparent, community-informed recommendations. 

Using AI tools such as Cursor and Claude, the project focuses on rapidly translating a concept into a functional MVP while examining the strengths and limitations of AI in code generation, feature design, and product development. Alongside the app, the student will distill lessons learned into a practical guide for non-technical builders, highlighting where human judgment, user research, and product intuition remain essential. 

The outcome is both a working prototype and a roadmap that demystifies AI-assisted entrepreneurship for the broader Tuck community.

Brenna Forristal

The Future of AI-enabled Sales and Revenue Operations

This project investigates how AI is transforming sales and revenue operations by interviewing Tuck alumni who actively use AI sales tools to understand current applications and future potential. Through these conversations, the student will identify how automation, predictive analytics, and conversational AI are reshaping sales strategies, team dynamics, and customer engagement. In parallel, the project will provide the Tuck community with a clear overview of Revenue Operations (RevOps) and the modern tech stack that supports it, clarifying how data integration, CRM systems, and AI platforms combine to drive growth and efficiency. The ultimate goal is to demystify both AI in sales and the broader RevOps landscape, translating industry insights into actionable lessons for future business leaders.

Sasha Greer

Myst: Demystifying Personal Finance for Women

Sasha’s project focuses on building Myst, a personal finance tool designed to help early-career and post-MBA women navigate major financial decisions with clarity and confidence by transforming traditionally intimidating topics, taxes, investing, 401(k)s, and life events like marriage or parenthood, into visual, interactive, and vibe-coded learning experiences. 

Grounded in the belief that financial planning can be a form of self-discovery, the project aims to simplify complex concepts, guide users through personalized financial pathways, and create an empowering space that normalizes financial confidence. 

The student will conduct user interviews, define an MVP centered on a single high-impact scenario, and build and test a prototype to gather feedback and refine the user experience. Ultimately, Myst seeks to make financial literacy both accessible and emotionally resonant, helping women replace uncertainty with confidence as they build wealth and articulate their own definition of success.

Cara Harrington

Critical AI Skills Evaluation

Cara’s project seeks to answer the question “What AI skills are most critical for Tuck MBA students and alumni in the workplace, and how can Tuck best prepare students to master those skills?” 

Through a combination of primary research, including a questionnaire and one-on-one interviews with Tuck students and alumni, and secondary research on AI education and workplace adoption, the project seeks to map current usage patterns, barriers to effective AI use, and learning needs. 

The goal is to develop targeted, actionable recommendations for curricular or extracurricular experiences that can strengthen AI learning at Tuck.

Fernando Herrera

AI’s Impact on Dating, Courtship, and Companionship 

Fernando’s project explores how generative AI is reshaping human connection by examining two emerging tensions in modern dating: 

1. Whether AI will remain a tool that augments authentic human courtship or increasingly automate the act of courting itself
2. Whether AI should focus on improving compatibility between people or evolve into a substitute companion

The project hypothesizes that users’ growing preference for efficiency, reduced rejection, and higher match quality will push dating platforms toward automated initiation and AI-mediated interaction, just as declining social networks and rising loneliness may accelerate AI companionship from fringe novelty to mainstream emotional support. Through surveys, interviews, behavioral data, and analysis of market and regulatory signals, the research tests how user value hierarchies, platform incentives, cultural resistance, mental-health impacts, and investment trends collectively shape this shift. Ultimately, the project aims to understand not just how AI is changing dating, but whether it is quietly redefining intimacy, companionship, and the future of human relationships.

Etaine Lamy

Beyond the Name Tag: How Tech Communities Design for Connection

Etaine’s project examines how the design of tech community events can create the conditions for genuine relationship-building. It will cover employee resource group gatherings, community organizations’ meetups, and conference networking sessions.

This project will explore how design elements shape the depth and quality of human connection, and help attendees go beyond the superficial. For example:

• Curation: role of identity / pre-selection
• Format: roundtables, small dinners, collaborative activity-based sessions vs drinks reception
• Facilitation: the role of hosts, prompt design, micro-structure within events

Etaine will interview group leaders, community organizers, and conference planners from the tech and venture ecosystem, complemented by light secondary research on community design. 

The outcome will be a concise best-practice guide outlining key design principles to help Tuck students and partners build more intentional community networks in tech.

Lily McCarthy

AI Prototyping and Portfolio Platform Development

This project embraces a “learn by doing” approach to AI by building and launching a series of AI-enabled applications that solve niche problems while providing hands-on experience with modern AI tools and product development. Along the way, the student will document insights, challenges, and best practices, ultimately transforming individual experimentation into shared knowledge for the Tuck community. The project culminates in creating a platform where Tuck students can showcase what they’ve built, learn from one another, and contribute to a growing culture of curiosity and maker-mindset on campus, positioning AI not just as a technical skill, but as a community-driven discipline of exploration and problem-solving.

Brian Zhao

Generative AI and 3D Modeling Impacts on the Future of Entertainment

This project examines how generative AI and 3D modeling are converging to reshape the future of entertainment—from film and television to gaming and immersive XR experiences. Focusing on the technical and creative frontiers, the student will investigate whether GenAI can reliably generate and iterate on high-quality 3D assets, whether it can do so fast enough for real-time or procedural environments, and whether these assets can be optimized for performance-intensive mediums like AR/VR, where rendering demands are exceptionally high. By exploring these challenges and emphasizing edge cases in XR the project aims to identify the “ideal use cases” and conditions under which 3D generative AI can succeed. The outcome will be a forward-looking point of view on what 3D generation might enable in 1, 5, and 10 years, and the industry shifts that could follow as AI becomes increasingly central to content creation.

Will Zimmerman

AI Prototyping, Vibe Coding, and Cybersecurity

Will’s project explores what it takes to design, launch, and secure a novel AI-powered application while sharing those lessons with the broader Tuck community. He will build an original “vibe-coded” app, release it online to gather real user feedback, and present both the product and build process in a final demo. 

In parallel, he will investigate the cybersecurity implications of AI-native applications by either hiring a penetration tester or using AI tools to attempt to breach their own system, documenting the vulnerabilities uncovered and what they reveal about emerging risks in tech. 

The project culminates in a CDS Demo Day, where fellows will present their MVPs, development journeys, and key takeaways—turning individual experimentation into a shared learning experience about how AI products are built, used, and secured in practice.

The post Introducing the T’26 Center for Digital Strategies Fellows Projects appeared first on Center For Digital Strategies.

The 2025–2026 CDS Associates represent a remarkably diverse group of leaders whose experiences span cloud infrastructure, analytics, robotics, AI-powered product development, national economic policy, hospitality innovation, social impact, and entrepreneurial ventures. This year’s cohort includes individuals who have helped design sovereign digital bond programs, launched patented robotics technologies for warehouse automation, built and scaled data-driven startups, and led product teams creating cutting-edge AI features used by thousands of customers. Others have advised Fortune 50 companies on digital strategy, strengthened inclusion in financial services, modernized government technology, and driven transformation across consumer, travel, healthcare, and energy industries.

They bring perspectives shaped by remarkable experiences: advising governments on financial policy, designing patented robotics solutions, leading large-scale hospitality teams, building AI-enabled products, driving civic engagement initiatives, contributing to military operations and leadership development, launching consumer startups, and even competing professionally in e-sports. Together, they reflect the multidisciplinary perspective that defines digital strategy today: a blend of technical fluency, creativity, operational excellence, and human-centered thinking.

Each year’s cohort is selected through a competitive application process that evaluates students across several dimensions central to the mission of the Center for Digital Strategies. Applicants are assessed on their demonstrated interest in technology and digital transformation, their professional and academic experiences, and their ability to contribute meaningfully to center programming and discussions. The selection committee also looks for evidence of thoughtful leadership, curiosity about the strategic implications of emerging technologies, and a commitment to strengthening the Tuck community. This intentional process ensures that each Associate brings both depth in their own domain and the mindset needed to engage with complex digital challenges alongside peers from diverse backgrounds.

We invite you to explore the profiles below and get to know each of our 27 Associates who will help shape CDS in the year ahead.

Mir Baquri

• Hometown: London, England 
• Favorite place in the Upper Valley: Ledyard Canoe Club
• Fun fact: Mir is nerdy about science.
• Digital essential: Microsoft To-Do
• Study snack / caffeine fix: Espresso
• Soundtrack for recruiting/finals: Tannhauser Overture by Wagner (conducted by Barenboim)
• Before Tuck: Advisor to the Economics Minister – His Majesty’s Treasury (UK)

Guyanna Bedington

• Hometown: San Diego, California 
• Favorite place in the Upper Valley: Woodstock
• Fun fact: Guyanna can walk on her hands.
• Digital essential: Chat GPT
• Study snack / caffeine fix: Black coffee and a Cliff bar.
• Before Tuck: Investment Analyst, Sellwood Investment Partners

Maddie Beecher

• Hometown: Marlborough, Massachusetts / Madison, Connecticut
• Favorite place in the Upper Valley: Hanover River Trail (great swimming area for my dog, Shadow!)
• Fun fact: Maddie is really good at Mahjong.
• Digital essential: Notion to organize my career goals, project portfolio, networking tracker, and company dream list
• Study snack / caffeine fix: Cold brew from Dirt Cowboy
• Soundtrack for recruiting/finals: Charli XCX’s Brat album
• Before Tuck: Deputy Program Development Director, Kamala Harris for President

Vivek Bhagat

• Hometown: Pune, India
• Favorite place in the Upper Valley: Campion Ice Rink
• Fun fact: Vivek loves solving puzzles (sudoku, kakuro, mathdoku, or anything!)
• Digital essential: Kindle
• Study snack / caffeine fix: Caramel macchiato
• Soundtrack for recruiting/finals: Wanderer by Ensiferum
• Before Tuck: Director, Founder’s Office, Rightbot Technologies

Amos Cariati

• Hometown: Providence, Rhode Island
• Favorite place in the Upper Valley: Baker-Berry Library – Tower Room looking out over The Green
• Fun fact: Amos has played the violin since he was 4.
• Digital essential: Wireless, noise-cancelling, over-the-ear headphones
• Study snack / caffeine fix: Gummy bears
• Soundtrack for recruiting/finals: My ~40hr long Country music playlist
• Before Tuck: Senior Customer Success Manager, Dover

Abdulmalik Carim

• Hometown: Lagos, Nigeria
• Favorite place in the Upper Valley: Mt Moosilauke
• Fun fact: Abdulmalik used to be in a choir.
• Digital essential: Apple watch
• Study snack / caffeine fix: Trail mix
• Before Tuck: Product Engineering Specialist, Atmus Filtration Technologies

Nate Chou

• Hometown: Cerritos, California
• Favorite place in the Upper Valley: Joe Hall’s office
• Fun fact: Nate spent the last 2 years before Tuck backpacking the world and has visited 44 countries in total.
• Digital essential: Password manager
• Study snack / caffeine fix: Black coffee 
• Before Tuck: Cofounder & CEO, VulnFree

Hunter Clark

• Hometown: McLean, Virginia
• Favorite place in the Upper Valley: A frozen Lake Morey
• Fun fact: Hunter’s aunt was a teacher at Hanover High and taught Dean Slaughter’s son.
• Study snack / caffeine fix: Sour gummy worms
• Before Tuck: Operations Manager, Group Trips, Indagare Travel

Joe Guthart

• Hometown: San Francisco, California
• Favorite place in the Upper Valley: Dartmouth Skiway
• Fun fact: Joe loves to backcountry ski.
• Digital essential: Spotify
• Study snack / caffeine fix: French Press Coffee
• Soundtrack for recruiting/finals: Sugar We’re Going Down – Fall Out Boy
• Before Tuck: Project Manager, Finance Business Process Reengineering, Apple

Spencer Hogan

• Hometown: Middletown, New York
• Favorite place in the Upper Valley: Oak Hill XC Ski Center
• Fun fact: Spencer had a stint as a professional photographer.
• Digital essential: Whoop (Fitness Tracker)
• Study snack / caffeine fix: An espresso and some dark chocolate
• Soundtrack for recruiting/finals: The Tragically Hip’s greatest hits! (The greatest 90s Canadian Rock band)
• Before Tuck: Consulting Associate, Kearney

Tari Kandemiri

• Hometown: Silver Spring, Maryland
• Favorite place in the Upper Valley: Lucky’s Coffee Garage
• Fun fact: Tari is helping build the Zimbabwe National Women’s & Men’s Lacrosse teams! #OlympicsBound
• Digital essential: My iPad
• Study snack / caffeine fix: Cappuccino with oat milk and brown sugar
• Soundtrack for recruiting/finals: Any and all jazz playlists on YouTube and Spotify
• Before Tuck: Founder & CEO, Hama Beauty

Sathya Keerthi

• Hometown: Originally from Chennai, India but has been in Boston for the last 5 years.
• Favorite place in the Upper Valley: Norwich!
• Fun fact: Sathya can solve the Rubik’s cube in under 30 seconds!…and he has written a book about Physics!
• Digital essential: Gadget – AirPods, App – Google Maps!
• Study snack / caffeine fix: homemade Chai with Ginger and Cardamom
• Soundtrack for recruiting/finals: Classical Music, anything from Mozart to Paganini!
• Before Tuck: Chef de Cuisine, Boston Harbor Hotel

Adah Lindquist

• Hometown: Wayne, Pennsylvania
• Favorite place in the Upper Valley: Still exploring!
• Fun fact: Adah is a certified yoga instructor.
• Study snack / caffeine fix: Addicted to coffee
• Before Tuck: Data Strategy and Communications Lead, Enterprise Transformation, Liberty Mutual Insurance

Matt Neal

• Hometown: Santa Barbara, California
• Favorite place in the Upper Valley: Dunks 2.0 once it reopens
• Fun fact: Matt can balance a broom on his nose like a seal.
• Digital essential: Chat GPT
• Before Tuck: Consultant, Deloitte Consulting

Margaret Palko

• Hometown: Collegeville, Pennsylvania
• Favorite place in the Upper Valley: POST
• Fun fact: Margaret loves to draw and doodle! She hopes to make her own stickers someday.
• Digital essential: Procreate!
• Study snack / caffeine fix: Matcha covered almonds
• Soundtrack for recruiting/finals: Mess – Noah Kahan
• Before Tuck: Senior Associate, Strategy & Operations, DoorDash

Samrat Patel

• Hometown: North Stonington, Connecticut
• Favorite place in the Upper Valley: Broken Hearts Burger
• Fun fact: Samrat was a press photographer for one of the last major conferences in NY before COVID, Vogue Knitting Live 2020.
• Digital essential: Outlook!! I mainly digest news through newsletters (~20/day)
• Study snack / caffeine fix: Double espresso followed by a black coffee
• Soundtrack for recruiting/finals: Lots of EDM
• Before Tuck: Consultant, L.E.K. Consulting

Alankrita Patil

• Hometown: Mumbai, India
• Favorite place in the Upper Valley: Pine Park, Tuckerbox
• Digital essential: Big Excel fan
• Study snack / caffeine fix: Matcha/lightly salted nuts
• Soundtrack for recruiting/finals: Eye of the tiger!
• Before Tuck: Logistics Planning Advisor, ExxonMobil

Cecily Power

• Hometown: Newton, Massachusetts
• Favorite place in the Upper Valley: Woodstock Farmer’s Market
• Digital essential: New York Times Cooking app
• Study snack / caffeine fix: Cold Brew
• Before Tuck: Product Manager, aPriori Technologies

Calogero Ramirez

• Hometown: Falls Church, Virginia
• Favorite place in the Upper Valley: Base Camp Cafe
• Fun fact: Calogero has built a digital speaker from scratch (procuring pieces from electronics vendors and designing the enclosure).
• Digital essential: Oura Ring
• Study snack / caffeine fix: Blueberry Luna Bar
• Soundtrack for recruiting/finals: Nice to Each Other – Olivia Dean
• Before Tuck: Staff Engineer, Stryker

Julie Sanduski

• Hometown: Pleasantville, New York (lately Seattle, Washington)
• Favorite place in the Upper Valley: Still North
• Fun fact: Julie can be found crabbing on her paddleboard in the summer.
• Digital essential: A good ol’ cast iron skillet
• Study snack / caffeine fix: Too much coffee
• Soundtrack for recruiting/finals: Whatever my discovery weekly is on spotify
• Before Tuck: Senior Product Designer, Pioneer Square Labs

Lars Erik Schönander

• Hometown: Larchmont, New York
• Favorite place in the Upper Valley: Lou’s
• Fun fact: Lars is a triplet! He has two sisters.
• Digital essential: The Apple calendar app. 
• Study snack / caffeine fix: A Nutella sandwich.
• Soundtrack for recruiting/finals: Salvatore Ganacci – Fight Dirty
• Before Tuck: Policy Technologist & Research Fellow, Foundation for American Innovation

Sehaj Seli

• Hometown: Chandigarh, India
• Favorite place in the Upper Valley: Giles Trail
• Fun fact: Sehaj is artistically inclined and enjoys painting. 
• Digital essential: ChatGPT, notebookLM
• Study snack / caffeine fix: Cappuccino
• Soundtrack for recruiting/finals: It’ll all work out
• Before Tuck: Senior Account Director, LinkedIn

Tanya Shah

• Hometown: New Delhi, India
• Favorite place in the Upper Valley: Giles Mountain Trail
• Fun fact: Tanya enjoys singing and playing the guitar!
• Digital essential: Google Maps
• Study snack / caffeine fix: A banana and black coffee; other times a diet coke!
• Before Tuck: Senior Manager, Founder’s Office, ApnaKlub

Alex Seokmin Sohn

• Hometown: Seoul, South Korea
• Favorite place in the Upper Valley: Han Fusion
• Fun fact: Alex used to be a competitive e-sports game player (almost 15 years ago).
• Digital essential: Outlook App
• Study snack / caffeine fix: I always drink Iced Americano even during the cold winter
• Soundtrack for recruiting/finals: Luke Combs playlist
• Before Tuck: Manager, Data Center Hardware Procurement, NAVER Cloud

Jen Spadaro

• Hometown: Mystic, Connecticut 
• Favorite place in the Upper Valley: Putnam’s Vineyard
• Fun fact: Jen was a ballerina growing up.
• Digital essential: My AirPods!
• Study snack / caffeine fix: AWAKE Chocolate (caffeinated chocolate bites!)
• Soundtrack for recruiting/finals: “This Is Taylor Swift” on Spotify
• Before Tuck: Strategy Senior Consultant, Deloitte Consulting

Jack Thompson

• Hometown: Cary, North Carolina
• Favorite place in the Upper Valley: The local wooded walking trails. 
• Fun fact: Jack went skydiving in Hawaii last year. 
• Digital essential: GrapheneOS
• Study snack / caffeine fix: Dark Chocolate >90%
• Soundtrack for recruiting/finals: Frank Sinatra’s Greatest Hits
• Before Tuck: Infantry Officer, United States Marine Corps

Simone Wasbin

• Hometown: San Clemente, California
• Favorite place in the Upper Valley: Quechee
• Fun fact: Simone is currently learning Russian to be able to talk with extended family abroad
• Digital essential: Chat GPT
• Study snack / caffeine fix: Dark chocolate peanut buttercups
• Soundtrack for recruiting/finals: “relaxing classical guitar” playlist on spotify
• Before Tuck: Senior Product Manager, Everlaw

We are grateful to this year’s Associates for the energy, talent, and perspective they bring to the Center for Digital Strategies. We look forward to the impact they will make across Tuck and beyond in the year ahead.

The post Get to Know Our T’27 Associates appeared first on Center For Digital Strategies.

A troubling pattern has emerged from my conversations with business professionals: employees are hiding their AI usage at work. In a meeting with a new Tuck MBA student, I learned the company they worked for prior to coming to business school still had a generative AI ban in place, forcing this student to either hide their use of AI or take significantly more time to compile and complete analysis in hours that previously took days, or spend significantly more time on the analysis than necessary.  The student noted they were afraid their manager would see the use of AI as “cheating.”

This secrecy isn’t just counterproductive. It’s a symptom of a leadership crisis. As Stanford research shows 22-25 year-olds facing 13% employment declines in AI-exposed roles, managers are leading a workforce transition using performance systems that actively discourage the adaptation their organizations need to survive.

The Performance Crisis AI Exposed

The difficulty of evaluating knowledge work predates ChatGPT. Unable to measure thinking quality or insight generation, managers defaulted to proxy metrics: hours worked, process adherence, deliverable volume. These input-based metrics were always inadequate, but they were tolerable when analytical work was time-intensive.

AI shattered that tolerance. When analysis that required days now takes hours, what are managers actually evaluating? An employee who uses AI to generate insights quickly and then builds prototypes appears unproductive by traditional metrics while delivering more value.

Meanwhile, workplace training spending has remained flat at around $1,280 per employee for over a decade, right when skill requirements are changing fastest.

From Analysis to Insight: The Real Work Begins

Recently, a Tuck alumnus at an AI-native startup shared his CEO’s approach to requests: “No rush, tomorrow’s ok.” The message is clear—with AI, data processing happens quickly. The valuable work is generating insights and making decisions based on analysis.

This reframes knowledge work value. Analysis was never the point; it was a step toward decision-making. But when analysis required significant manual effort, many employees and managers conflated analytical difficulty with importance. AI eliminates that confusion by making analysis commodity work.

The hard work now: synthesizing meaning from data, identifying non-obvious patterns, making judgment calls with incomplete information, translating insights into action. These require human creativity, experience, and wisdom—capabilities AI augments rather than replaces.

Case Study: Airtable’s Structural Solution

Howie Liu, Airtable’s co-founder and CEO, recognized that AI transformation required restructuring work itself. He reorganized the company into two team structures:

Fast Teams: Ship AI features weekly, optimize for iteration speed, embrace experimentation. Success metrics focus on speed of learning and adaptation.

Slow Teams: Focus on infrastructure, deliberate planning, systematic progress. Traditional metrics around stability and reliability apply.

The insight: different work requires different management. Rather than forcing one performance framework, Liu acknowledged some people need fast iteration space while others maintain operational stability.

Critically, Liu models the behavior he expects; he codes daily and is Airtable’s top global AI user. He implements “mandatory AI experimentation time,” telling employees to cancel meetings for a week to explore tools. This creates organizational permission for exploration that traditional productivity metrics discourage. Listen to his recent interview on Lenny’s Podcast to learn more from Howie Liu.

Managing Hybrid Teams: Humans and AI Together

The analyst-to-executor transition is just the beginning. Forward-thinking managers are learning to manage teams that include both humans and AI agents as collaborative members, not just humans using AI tools.

This requires rethinking team composition, workflow design, and task delegation. When AI agents handle analytical tasks as effectively as humans, managers need new frameworks for optimal task allocation, managing interdependencies between human and AI work, and ensuring effective communication across diverse team members.

Workflow orchestration becomes critical. Traditional project management assumes human-paced handoffs. Hybrid teams operate at different speeds because AI processes information instantly while humans need time for synthesis and judgment. Effective managers design workflows leveraging these different capabilities rather than forcing everything into human-paced processes.

A Manager’s Playbook

1. Shift to Outcome-Based Metrics

Evaluate the quality of insights and actions taken, not time spent. Ask: “What decisions were enabled?” rather than “How much time did this require?” Create space for employees to explain their process, including AI usage.

2. Create Psychological Safety

Model curiosity about AI tools. Share your learning process, including failures. When employees mention using AI, respond with interest not suspicion. Establish forums for sharing experiments, both successes and failures.

3. Emphasize Insight Generation

When employees work faster using AI, dig into the quality of their insights. Help them understand time saved should be reinvested in synthesis and strategy. As the AI-native CEO puts it: if analysis can be done by tomorrow, the real work is generating insights and making decisions.

Actively reward insight quality and decision-making over analytical thoroughness. Ask: “What does this tell us we didn’t know?” and “What should we do differently?”

4. Support Experiential Learning

Create low-cost learning opportunities: internal tool-sharing sessions, conference attendance, cross-functional AI projects, dedicated experimentation time. Focus on learning through real work problems, not formal training.

5. Address the Transition Directly

Help analytical employees understand their value lies in what they do with insights. Provide opportunities to develop execution skills: prototype building, stakeholder communication, project management. Recognize employees who successfully transition.

6. Develop Hybrid Team Skills

Think about AI agents as team members with specific capabilities and limitations, not just tools. Practice workflow design combining human judgment with AI processing power. Learn effective AI communication through prompt engineering. Experiment with task allocation considering both human strengths (synthesis, judgment) and AI strengths (processing, pattern recognition).

The Bottom Line

Leading AI transformation requires managers to evolve as much as their employees. Move from supervision to facilitation, from process management to outcome enablement, from individual evaluation to team learning optimization.

The organizations that master this, like Airtable, will develop sustainable advantages through continuous human-AI collaboration improvement. Those measuring AI-age work with pre-AI metrics will manage increasingly frustrated employees who leave for progressive organizations or hide their innovative work.

The choice: evolve your leadership approach to match AI-transformed work, or manage by metrics that discourage the adaptation your organization needs to thrive.

Discussion Question: What’s your biggest challenge in evaluating knowledge work outcomes versus traditional productivity metrics? How are you creating space for AI experimentation on your team?

The post Leading the Analyst-to-Executor Transition: A Manager’s Guide to AI Workforce Transformation appeared first on Center For Digital Strategies.

The answer isn’t what you might expect. New research from Stanford provides a sobering wake-up call, but it also reveals a clear path forward for those willing to adapt. This blog offers that path forward. 

The Employment Reality: AI Displacement is Already Here

Erik Brynjolfsson and his Stanford colleagues just released findings in a report titled, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence,” that should get the attention of every new graduate and MBA student. Using payroll data from 25 million American workers, they documented something many suspected but few had quantified: AI is already displacing workers, and it’s hitting early-career professionals hardest. 

The numbers are stark. Since late 2022, coinciding with ChatGPT’s launch, workers aged 22-25 in AI-exposed occupations like software development and customer service have experienced a 13% relative decline in employment. Meanwhile, older workers in the same roles continue to see employment growth of 6-9%.

This isn’t a future concern. It’s happening now, and it’s accelerating.

The research reveals why: AI excels at replacing codified knowledge, the formal, structured information taught in universities and business schools. But AI struggles with tacit knowledge, the intuitive understanding, contextual judgment, and practical wisdom that comes from experience. Early-career workers, who rely more heavily on codified knowledge, find themselves most vulnerable to displacement.

For MBA students and recent graduates, this creates both urgency and opportunity. The question isn’t whether AI will reshape your career, it’s whether you’ll develop the skills to shape that transformation rather than be shaped by it.

The Builder’s Framework: Three Meta-Skills for the AI Age

The solution isn’t to become more technical (though AI literacy helps). Instead, success requires developing three interconnected meta-skills that amplify human capabilities while leveraging AI’s strengths:

1. Deep Curiosity

This goes beyond intellectual interest. In the AI age, productive curiosity means persistently asking “what’s the next question?” and “how can I dig deeper?” rather than accepting first answers. When AI can provide instant analysis, your value lies in knowing what questions to ask next and which problems are worth solving.

2. Comfort with Ambiguity

Traditional business education rewards structured thinking and clear frameworks. AI thrives in these structured environments. Your competitive advantage emerges in messy, unclear situations where the path forward isn’t obvious. This means being comfortable starting without knowing the full solution and iterating based on feedback.

3. Bias to Execution

Here’s the critical shift: in the AI economy, analysis alone has diminishing value. AI can perform most analytical tasks faster and more accurately than humans. Your edge comes from moving beyond analysis to action; generating insights from data, building solutions from insights, and executing on opportunities.

This last point addresses a fundamental vulnerability I see in many business school graduates. Too many people, especially junior professionals, define their roles as analysts. Those days are ending. Analysis was never the point, it was a necessary step toward decision-making and action. AI can handle the analytical heavy lifting; you need to master what comes next.

The Builder in Action: From Insight to App in 3 Days

Let me illustrate these meta-skills in action through a recent example that perfectly captures what I mean by becoming a “builder.”

Hernan Orvalle T’25 and Thamires Mouta T’25 at the

Hernan Orvalle, an MBA Fellow at Tuck’s Center for Digital Strategies who graduated in June, traveled with me to the Consumer Electronics Show (CES) in Las Vegas. During our visit to Samsung, they demonstrated an AI-powered phone app that analyzes photos of food and provides detailed nutritional information, including calories, macronutrients, dietary insights.

Most people would have observed the demo, perhaps taken notes, maybe discussed the market implications. Hernan did something different.

Within days of returning to campus, while managing full-time MBA coursework and a young family, he had built a working version of the app himself. His first prompt to ChatGPT? “How do I install Python?”

Think about what happened here:

1. Deep Curiosity: Rather than just understanding what Samsung built, Hernan wondered “How does this actually work?” and “Could I build something similar?”
2. Comfort with Ambiguity: He started from zero technical knowledge, comfortable with not knowing the answers, and worked iteratively with AI to fill knowledge gaps.
3. Bias to Execution: Instead of analyzing the market opportunity or writing a business plan, he built a prototype. The learning came through doing, not theorizing.

This represents a fundamental shift in how we need to think about capability building. We’re all builders now, but that doesn’t mean we need to become technical experts. It means we need to become comfortable working collaboratively with AI to create solutions.

The Analyst-to-Executor Transition

For MBA students, this shift from analyst to executor requires rethinking what “value creation” means in your career. In many traditional roles, producing thorough analysis was the deliverable. In the AI age, that analysis becomes table stakes; something you and AI can generate quickly together.

Your value lies in what happens next:

• Converting analysis into actionable insights
• Building solutions based on those insights
• Executing on opportunities the analysis reveals
• Making judgment calls when data is incomplete or conflicting

This doesn’t mean abandoning analytical rigor. It means treating analysis as input to action rather than output in itself. The human-AI collaboration happens when you use AI to overcome analysis paralysis; moving from data analysis to asking follow-up questions, exploring scenarios, and iterating toward solutions.

Navigating Organizational Reality

My previous blog post highlighted how enterprises are divided between those embracing AI transformation and those stuck in efficiency thinking. As an individual, you need to be strategic about where you build your career.

Some organizations will resist the shift toward execution-oriented roles, preferring traditional analytical deliverables. Others will embrace the builder mentality and provide environments where you can develop these meta-skills. Part of career planning in the AI age means seeking out (or helping create) cultures that support this transition.

This might mean:

• Choosing employers based on their AI maturity, not just brand prestige
• Seeking roles that reward iteration and experimentation
• Working with managers who understand that failure is part of the building process
• Building internal tools and prototypes even when it’s not explicitly in your job description

Building Your Builder Portfolio

Unlike technical skills, these meta-skills resist traditional measurement. You can’t get a certification in “comfort with ambiguity.” Instead, you need to demonstrate these capabilities through what you’ve built.

Start creating a portfolio of projects that showcase your ability to:

• Identify interesting problems worth solving
• Work iteratively with AI to develop solutions
• Execute despite incomplete information
• Learn from failure and iterate quickly

These don’t need to be commercial products. Build internal tools for your current employer, prototype solutions to problems you observe, create proof-of-concepts that demonstrate new approaches. The key is showing that you can move from “what is” to “what could be.”

Practical Exercises for Skill Development

Curiosity Cultivation:

• Weekly “Why?” Sessions: Pick one process in your current role and spend 30 minutes asking “why does this work this way?” Use AI to explore alternative approaches.
• Problem Collection: Maintain a running list of inefficiencies or frustrations you observe. Practice articulating why these problems matter and who they affect.

Ambiguity Tolerance Training:

• Start-Before-You-Know Challenges: Each month, begin a project without knowing how to complete it. Use AI as a collaborative partner to figure out next steps.
• Incomplete Information Decisions: Practice making recommendations with 70% of the information you’d prefer to have. Use AI to help explore scenarios and edge cases.

Execution Bias Development:

• Build Something Weekly: Dedicate time each week to creating something: a simple tool, a prototype, a process improvement. Start with AI assistance and move toward implementation.
• Action Accountability: For every analysis you complete, force yourself to identify and take at least one concrete next step within 48 hours.

The Mindset Shift: From Task Owner to Purpose Owner

Perhaps the most fundamental change required is shifting from owning specific tasks to owning the underlying purpose of your work. Traditional job descriptions focus on activities, such as, “analyze market data,” “prepare reports,” “manage vendor relationships.” In the AI age, you need to think in terms of outcomes and impacts.

Instead of “I analyze market data,” think “I help the company understand market opportunities and make better strategic decisions.” Instead of “I prepare reports,” think “I ensure stakeholders have the insights they need to take action.”

This connects to the first principles thinking I’ve used with undergraduate students. When you understand the fundamental purpose behind your work, you can adapt your methods as tools evolve. When you own just the tasks, you become vulnerable to replacement.

The Path Forward: Choose Building Over Waiting

The Brynjolfsson research is sobering, but it’s not deterministic. The 13% employment decline affects workers who remained in traditional analytical roles. The data doesn’t capture what happens to early-career professionals who develop builder capabilities and migrate to organizations embracing AI transformation.

You have a choice: wait for institutions and employers to catch up to the AI transformation, or start building the skills that will position you to lead it. The MBA students and recent graduates who thrive in the next decade will be those who develop comfort with uncertainty, master human-AI collaboration, and focus on execution rather than just analysis.

Start building. Start now. Your career depends not on what you know, but on what you can create with what you’re learning.

Discussion Question: What’s one project you could start this week to practice moving from analysis to execution? Share your ideas, and what you built from them, in the comments.

This is the second post in a series on AI strategy for business leaders. Read the first post on [Beyond Task Replacement: Why Your AI Strategy Needs First Principles Thinking] and watch for upcoming posts on AI adoption patterns and leadership skills for the AI economy.

The post The Builder’s Advantage: Three Meta-Skills for Thriving in the AI Economy appeared first on Center For Digital Strategies.

One student’s story stood out. Before Tuck, she worked at a global retailer where she was forbidden from using AI. This ban extended not just to consumer tools like ChatGPT, but even to AI features embedded in licensed enterprise software the company was already paying for. Think about that: a firm blocking employees from capabilities built into its own tools.

The contrast with AI-native companies couldn’t be clearer. MBA students are curious but still novices, experimenting at the edges. AI-native startups, meanwhile, are rewiring workflows around intelligent systems. Legacy enterprises, in contrast, are too often stuck in efficiency-focused thinking. They treat AI as a headcount reducer rather than a strategic catalyst. Some are even stifling employee experimentation. That mindset is not just limiting. It risks strategic obsolescence.

The AI Maturity Gap

Recent research from MIT underscores the problem. According to MIT NANDA’s report, The GenAI Divide: State of AI in Business 2025, despite $30–40 billion invested in GenAI, 95% of organizations see no return. Most deployments stop at boosting individual productivity: faster emails, quicker research, smoother presentations. Only 5 percent of enterprise-grade projects reach production.

Here is the divide:

• AI leaders use intelligent systems constantly and assume the rest of the world is catching up
• Legacy enterprises launch endless pilots that rarely scale or produce meaningful returns
• Employees often adopt AI tools informally (over 90% report using them), while only 40% of companies purchase official subscriptions

This “shadow AI economy” reveals a disconnect between individual enthusiasm and organizational transformation, while also revealing a strategy flaw at many legacy companies. 

Efficiency vs. Reinvention: The Amazon and Sears Lesson

We have seen this movie before. When e-commerce emerged, Sears, the retail giant of its era, treated the web as just another catalog channel. Their assumption was that the existing model would hold and their brand reputation and market power were moats around their business.

Amazon took the opposite approach. It built new business architectures: two-sided marketplaces, data-driven logistics, reimagined customer experiences, and eventually cloud computing. Amazon did not digitize Sears. It became something entirely different.

The lesson is clear. The internet was not about efficiency. It was about reinvention. Those who optimized for yesterday’s model lost. Those who reimagined business from first principles thrived.

AI represents the same kind of inflection point. Like the Internet, it will not just make existing businesses faster. It will rewire how they operate. The 5 percent of organizations crossing MIT’s “GenAI Divide” are already building adaptive systems that learn, remember, and evolve. Everyone else is stuck polishing legacy processes, looking for incremental gains. 

Why Incrementalism Is Not Enough

To be fair, efficiency plays are not inherently wrong. For heavily regulated industries such as finance or healthcare, small pilots reduce risk and build trust. But stopping there is a mistake. Incremental adoption does not prepare organizations for competitors designing entirely new models around AI.

Consider what is already happening:

• Walmart uses AI to optimize supply chains and improve in-store experiences
• JPMorgan deploys AI on trading desks and for fraud detection
• Pharma firms are cutting drug development timelines with AI-driven simulations

These are not experiments in efficiency. They are steps toward new operating models. The real risk is mistaking task automation for transformation.

First Principles Thinking as Strategic Advantage

So what should leaders do? Apply first principles thinking: break problems down to fundamentals and rebuild from there. Instead of asking, “How can AI make our operations more efficient?”, ask:

• What is our business truly trying to accomplish?
• If cognitive tasks were nearly free, how would we design differently?
• Which assumptions about value chains, workflows, or structures no longer hold?
• What new customer problems could we solve with unlimited analytical capability?

This mindset forces a reset. Many organizational structures exist because humans have cognitive limits. AI shifts those limits. Entirely new business models are possible when decisions can be made at machine speed and scale.

The MBA Connection

For MBA students, this divide is both a challenge and an opportunity. Business schools teach optimization within existing frameworks. These skills remain important, but they are not sufficient in the AI era. The leaders who will stand out are those who can:

• Think from first principles about business design
• Translate AI-native possibilities into legacy organizations
• Build systems thinking, comfort with ambiguity, and fluency in AI capabilities

In short, you will need to help enterprises imagine new possibilities, not just squeeze more efficiency from old ones.

A Practical Framework: Crossing the GenAI Divide

As a leader, you can use MIT’s research to assess where your organization stands. Here is a simple framework for accomplishing that assessment:

1. Audit your AI approach:

• Are you building custom tools internally (33 percent success) vs. partnering externally (67 percent success)?
• Are you investing mostly in sales and marketing vs. high-ROI back-office operations?
• Are you deploying static tools vs. adaptive systems that learn from feedback?

2. Spot the warning signs:

• Are you on “the wrong side” of AI development: constant prompting, no memory, endless pilots, no production
• Are you on “the right side” of AI development: deep integration into workflows (or developing new workflows entirely), adaptive learning, measurable P&L impact

3. Shift the framing:

• Do not ask: “How can AI make our vendor 20 percent more efficient?”
• Ask: “How can AI eliminate the need for this vendor entirely while improving quality?”

The difference is what separates the 5 percent achieving transformation from the 95 percent stuck in marginal gains.

Final Thought

The MBA students I met last week were both amazed by AI and aware they are just beginning. Legacy enterprises are in the same position. The question is whether leaders will move beyond amazement and incrementalism, or repeat Sears’ mistake and optimize their way into irrelevance.

For AI leaders, the challenge is equally clear. The gap between your AI-native reality and most enterprises is larger than you think. Bridging it is not just good business. It is the only way AI will fulfill its potential to reshape how the world works.

Discussion Question: Which industry do you think is most vulnerable to “Sears thinking” about AI, and what would an “Amazon approach” look like there? 

In case you’re curious, you can listen to the song I created to demonstrate AI capabilities, titled, “The Weight of the Casebooks,” below. The song was created using a free Suno account and is not licensed for commercial use.

The post Beyond Task Replacement: Why Your AI Strategy Needs First Principles Thinking appeared first on Center For Digital Strategies.

Antler is a global early-stage venture capital firm that partners with exceptional founders to build and scale tech startups from the ground up. With investments in over 1,400 companies across 30 cities on six continents—including Nairobi and newly added Lagos—Antler supports entrepreneurs solving meaningful global challenges.

A recent post by one of Antler‘s portfolio companies highlighted the need for more experience sharing on go-to-market strategies for African tech startups. We took on the challenge and mapped out four localized go-to-market strategies to uncover what works and what doesn’t on the continent.

We interviewed six companies operating across Kenya and other African markets. These included fintechs, edtechs, agri-tech platforms, and skincare brands.

Our goal: distill the lessons learned and craft a practical guide to go-to-market strategies for African startups.

What’s Inside the Paper

“Winning Go-to-Market Strategies for Early-Stage African Tech Startups” explores four key go-to-market archetypes:

• Content Strategies: Startups like Uncover and Money254 build brand trust and drive customer acquisition by creating educational content that addresses customer needs before investing in paid advertising.
• Network-Based Strategies: Hello Tractor leverages a decentralized network of booking agents who act as local representatives to facilitate transactions and expand market reach across multiple African countries.
• Partnership-Driven Approaches: Companies like Craydel and MosMos establish credibility and scale by forming strategic alliances with established institutions that not only provide direct access to target customers but also reinforce their mission.
• Direct Sales Tactics: From B2B relationship building to WhatsApp commerce, startups like HoneyCoin and Uncover are reimagining sales models to meet customers where they are.

We also examine cross-cutting success aspects like the imperative of building trust early, adapting communication styles to local cultural norms, and why founders must remain hands-on in go-to-market execution.

We hope this paper becomes a useful compass for founders navigating African markets and to continue to build on the CDS-Antler collaborations to give more students that chance to take the classroom to practice.

Access the full paper on Antler’s website
Co-authored by Marie Nielsen, Kosen Kenta, and Valeria Tiffer T’25

The post From Nairobi to an Antler-CDS Collaboration: Playbook for Winning Go-to-Market Strategies for Early-Stage African Tech Startups appeared first on Center For Digital Strategies.

Sidney Drill

Hometown or home base: 

Rockville, Maryland

What did you do prior to Tuck? 

Before Tuck, I was at Qlik, where I wore several hats—working in account management, solution architecture, and sales enablement to help customers unlock the full potential of Qlik’s data analytics platform. I was also deeply involved in our corporate social responsibility efforts, volunteering on non-profit work, co-founding a company-wide fitness-for-charity initiative, and serving as a co-lead for our Women in Tech ERG.

What are your plans post-Tuck?

After graduation, I’ll be returning to Qlik as a Director of Product Marketing, where I’ll blend my commercial experience with the strategic toolkit I’ve built during my MBA. I’m particularly excited to lead the product marketing strategy for data observability and FinOps, helping customers make smarter decisions about cloud spend and data reliability. With the data world becoming more dynamic, I’m eager to broaden my focus from analytics to the entire data estate.

What made you want to be part of the CDS Fellows program? 

I wanted to keep my toe dipped in the tech world while stretching beyond the niche I came from—there’s so much more out there! Being part of the CDS Fellows program gave me the chance to collaborate with classmates and explore all sorts of tech topics I’d never touched before. It was the perfect mix of staying grounded in what I know and venturing into what I didn’t (yet!).

What other activities were you involved in at Tuck? 

I got involved with a lot at Tuck—maybe too much. I served as a commissioner for the Tripod Hockey League, was a Non-Profit Board Fellow for the Upper Valley Trails Alliance, spent the fall on exchange at Copenhagen Business School, and even had the honor of emceeing Tuck Gives. Along the way, I picked up some brand-new skills like hockey, skiing, pottery, and golf—proof that it’s never too late to start learning (and falling)!

What has been the biggest growth moment at Tuck, where you stepped out of your comfort zone? And what did you learn from that experience? 

Honestly, one of my biggest growth moments at Tuck was when I decided to learn hockey, despite having zero experience. It was tough! The challenge wasn’t just physical; it was learning to adopt a growth mindset and let go of the need for perfection. There were very frustrating moments when things didn’t click, but I learned that growth comes from embracing mistakes and staying persistent. This experience reminded me that learning something new as an adult means showing up, staying open, and understanding that growth takes time – and that I don’t have to be the best hockey player.

What CDS learning opportunities or experiences have you learned the most from during your time at Tuck?

The peer-suggested discussion sessions have been by far my favorite. Occasionally, we have a Fellows meeting where someone suggests a topic they’re curious about, and we dive deep. These open, animated conversations always leave me thinking, “I never would have considered that perspective!” It’s been such a fun (and humbling) reminder of how much I can learn from my classmates’ unique insights.

What class at Tuck pushed your thinking the most?

Moral Reasoning comes to mind most often—it’s completely changed how I approach business problems. I recently caught myself using class frameworks to analyze the decision-making in The Godfather. Sure, Vito’s ethics are…debatable, but you’ve got to admire that he sticks to a very clear moral code!

What books are you reading, podcasts are you listening to, or shows are you watching? 

There’s a lot of reading for class, so now that the term is wrapping up, I’m finally getting back to reading for fun. I’m a proud book nerd and currently making my way (again!) through Brandon Sanderson’s Stormlight Archive—there’s nothing more comforting than epic fantasy.

What advice do you have for Tuck students interested in tech and digitally focused careers?

Be your own advocate, find your people, and don’t be afraid to explore beyond the traditional “tech” bubble. Digital strategy is everywhere—from life sciences to food & ag to energy. During my time at Tuck, I had a blast discovering how my skills could be applied in all sorts of industries, including an internship in the utilities space. While I’m heading back to tech post-Tuck, I’m excited to see how we can bring fresh perspectives and solve big challenges across diverse sectors.

The post CDS Fellows Spotlight: Sidney Drill T’25 appeared first on Center For Digital Strategies.