At Porsche, an AI developer built an algorithm that could distinguish the sound of a cappuccino from an espresso. An entertaining experiment, certainly. Then the capability found another job: helping fine-tune car-door seals against unwanted noise. A coffee machine had supplied a route into automotive design. The useful discovery was that a technique developed around one small problem could travel.
That story captures the appeal of Me, Myself, and AI. You arrive expecting a discussion of artificial intelligence. You leave thinking about the person who noticed a connection, the colleague who understood its value, and the organization willing to try it. The machinery matters. So does the journey from an interesting experiment to something somebody actually uses.
- A free business-AI podcast from MIT Sloan Management Review, launched with BCG.
- Practitioner interviews connect technology decisions with everyday work.
- Start with an episode about your problem. Bring its questions to your team.
A microphone for the implementation gap
The show began in 2020 with a pointed research question. Boston College’s announcement reported that just 11% of companies in the team’s survey succeeded with AI. The podcast’s promotional question rounded the problem to 10%. Those figures belong to the research that motivated the launch; they are not a live scoreboard for every company using AI today.
Sam Ransbotham, an analytics professor at Boston College, and BCG’s Shervin Khodabandeh became its original cohosts. The first two full episodes arrived on October 20. Early guests came from Walmart, Humana, DHL, Porsche, H&M, and the World Economic Forum. The format brought an academic researcher and a consulting practitioner into conversation with people making deployment decisions.
The offering is straightforward: interviews you can hear through major podcast platforms, with transcripts available for many episodes. Its natural audience includes managers, technology leaders, and practitioners trying to connect AI capability with a business problem. For those listeners, the gap between a convincing demonstration and a workable change is often the expensive part.
Interviews, trailers, and bonus episodes counted October 9, 2026. An archive measure, not a listener count.
Think of it as an editorial media product within MIT Sloan Management Review. BCG helped launch and coproduce the series, and maintains an episode archive. Current episode credits identify MIT SMR as producer and Ransbotham as host, with Allison Ryder producing and David Lishansky engineering. The product listeners receive is access to conversations, rather than software or an implementation service.
The first day is a lousy verdict
In an early episode, DHL’s Gina Chung described AI and robotics work around frontline logistics: inspecting and stacking pallets, optimizing delivery routes, and using aircraft more effectively. Her memorable observation concerned time. Initial performance, she explained, improves through human input until people become willing to trust and use the system.
“The first day for AI is the worst day.”Gina Chung · DHL · 2020
The managerial implication is worth sitting with. A pilot’s opening performance can be a poor basis for judging its eventual usefulness. Yet waiting is not a strategy by itself. Chung’s account also emphasizes stakeholders, change management, and designing for the end user. Somebody has to notice the mistakes, supply feedback, and help people understand the changing tool.
For a listener, that changes the next meeting’s agenda. Ask who will collect feedback after launch. Ask whether the people doing the work have helped define success. Ask what happens when a recommendation is wrong. These questions are less glamorous than a demo, but they put the experiment inside the organization that must live with it.
An editorial listening guide, not a promise of performance.
The report nobody wanted to write
By September 2024, the conversation had moved into another familiar corner of office life. Asana CIO Saket Srivastava described AI drafting project status reports using the context already held in the work-management platform. A human could then modify or accept the result. The same discussion considered recommendations about project risks and resources.
This is a useful example because its boundaries are visible. There is an existing workflow, relevant information, a draft, and a person responsible for the decision. Srivastava described AI increasingly acting as a teammate within core work. The hosts explicitly contrasted that idea with earlier conversations centered on prediction and optimization. The vocabulary had changed because the range of tasks had changed.
Listen with a pencil, and a raised eyebrow
Listening costs no subscription fee through the public podcast feed. Applying a lesson consumes time and resources. A sensible exercise is to write down the task, the desired result, and the costs your own organization would carry: integration, staff training, review, and maintenance. Keep the cost of an error on the same page. That exercise is our suggested use of the interviews.
It also explains where the show fits. Broad management interviews, such as HBR IdeaCast or The Economist’s Boss Class, can address a wider range of leadership problems. Me, Myself, and AI repeatedly returns to AI in organizational practice. Its research-linked origin and practitioner access make it useful preparation for a discussion with colleagues who need more than a product announcement.
Success stories still require judgment. A retailer’s data may bear little resemblance to yours. A draft status report and a safety decision deserve different levels of scrutiny. Airbnb’s Naba Banerjee discussed human-machine collaboration while recognizing that neither decision maker is infallible. If your team cannot identify relevant data, accountability, or a way to examine errors, copying a guest’s tool is premature.
The success story gets a skeptic
The 2026 archive adds a productive complication. Economist Daron Acemoglu challenges assumptions about AI and productivity; Erik Brynjolfsson discusses human agency and shared prosperity. Their presence widens the inquiry beyond a successful deployment. What kinds of work should organizations create? Who benefits from the choices they make?
In September, GoFundMe CEO Tim Cadogan brought that question to fundraising, describing AI help with telling stories and mobilizing communities. It is a fitting place to leave this profile. Sometimes the desired outcome is a better decision. Sometimes it is the confidence to ask another person for help. The reason to listen is to get more precise about which outcome you want.
Put an episode to work
Choose a conversation that meets your next work problem.