Tricia Maia is Head of Product at TED Conferences. Before joining TED, Tricia held key positions at AlphaSights and Verizon, where she led product management and digital strategy initiatives.
In our conversation, Tricia discusses how defining strategy differs between nonprofit and for-profit companies, as well as those in different industries and at various stages of their life cycles. She emphasizes a few key universal strategy principles that apply to any organization, and also shares insights into how trends like AI are impacting the nonprofit, media, and edtech sectors.
I head Product here at TED. We’re technically a nonprofit but also function as a media and event company, which influences our strategy development. I’m responsible for product management, design, analytics, support, and localization across all of TED’s digital experiences — from our website, apps, and consumer experiences, such as how people watch TED Talks, to our event technology powering our conferences and supporting our attendees and event staff.
On the backend, I oversee our media, internal tooling, and data platforms, including the analytics and insights behind all of our operations. For example, how do we produce a live talk into multi-media assets? What tools do our internal teams need to edit that media and distribute it to our different publishing systems? How do we measure success across all of our different products and business units and help our teams draw key insights from the data?
In my career, I define myself as more of a generalist versus a specialist in a particular domain. Before TED, I worked at various industries, products, and companies, including growth-stage startups and larger corporations.
TED is interesting because we’re a mix of everything. We’re a nonprofit, but we’re also an events and media company with strong philanthropic arms. We have enterprise, B2B, and educational divisions, which support the organization financially and drive revenue to power our overall mission. We have to balance revenue and growth, but our mission is the impetus and purpose behind everything. We grow revenue to fund our mission to have a greater impact for our many communities, not to reward shareholders.
When working on strategy, I think about it across a few dimensions. Wherever you are, you need to look at the goals of your organization, the audiences or users you serve, resources, and compliance. First and foremost, any strong strategy needs to start from a keen understanding of the goals and the user base. From there, you can dream up how to serve those goals in invaluable and differentiated ways. That’s how I define the strategy.
For nonprofits, the focus is always on the mission. At TED, our goal is to discover and champion ideas that motivate, inspire, and educate. So, our stakeholders are our beneficiaries, people who consume our content, volunteer communities, donors, and the TED community at large. Our strategies focus on maximizing impact for the people we serve and fostering a sense of community and purpose in a financially viable way.
For-profit companies can still have a strong mission, but the primary objectives at the end of the day are more focused on user and revenue growth, scalability, and profitability. Their stakeholders are generally potential customers, current customers, investors, or shareholders, not donors or beneficiaries. So their strategies focus on maximizing those metrics and serving those groups above all.
One of the things we’re working on currently is what we call our learning product. It’s still a work in progress, but we’re trying to transform the passive act of watching a talk into something a little more codified and concrete that our viewers can take and run with.
For example, if you’ve watched several talks on a particular topic, like AI or psychology, your user profile will reflect your learning journey across different topics. We’ll show the key takeaways from your viewing history distilled in a bite-sized, written format. This will help you retain insights from many long talks over the years, making your learning more permanent and actionable.
This year, we’ve also launched an AI dubbing feature as part of a strategic push to better serve our global communities. Most of our audience is international, and many don’t speak English as a first language. They’ve had to rely on transcripts and subtitles to understand the content of the talks. Now, we’re using AI technology like voice cloning and lip-syncing to publish TED Talks in different languages as realistically as possible. If someone gives a talk in English, we can translate it into several different languages (with the speaker’s permission, of course), and it will seem like the speaker is delivering the talk fluently in these languages.
Much of what I’ve done over the years is related to managing stakeholder expectations. I start by working to understand where the organization is and its current digital maturity level, including understanding existing organizational structures. For example, does the product team report to the chief marketing officer? This tells me about the bent of the company and its values. What are the digital capabilities, the skill sets of the employees, the tech infrastructure, etc.? This gives me a sense of the overall readiness for a digital transformation or product-driven thinking.
Being “product-driven” is a controversial and often misunderstood term. It doesn’t mean that the product team runs the show; it’s more a way to describe how the best tech-powered companies tend to work. Marty Cagan uses the similar term “product operating model” — it’s ultimately about cross-functional teams focusing on delivering outcomes rather than outputs. These empowered teams work toward high-level goals with the autonomy to determine the best ways to achieve them. The opposite of this is a feature-driven team.
Once I understand where the organization sits on the spectrum, I adapt my communication style to their level of digital fluency. In less mature organizations, I explain the basic concepts and benefits of digital initiatives. In more advanced settings, I emphasize strategic growth opportunities and competitive advantages. The key is to sell the vision first — once that’s bought into, the “how” becomes easier because there’s less internal resistance.
I also try to quickly scope out, identify, and empower internal champions who understand and value this transformation and already have the respect of the team. They’re the ones who can help me advocate for the strategy within the org and expedite the impact immensely.
From there, I take into consideration thoughts, feelings, and context from as many people as possible to inform the strategy. Leading with an empathetic, flexible, listen-first mentality is important. A lot of people refer to this as a listening tour. I’ve seen transformation initiatives fail when leaders come in with a brilliant idea but try to do a massive overhaul right out of the gate without taking that first step of, simply, listening.
The one variable in this process is how much time an organization needs before they’re open to change. So, having some emotional intelligence to understand how quickly you can move also helps.
Once I enter the change management stage, I’ve sold the vision, and everyone’s on board. I focus on continuing to connect the digital strategy to business objectives and educating and engaging stakeholders along the way. Leading with as much data and user input as I can, along with demonstrating quick wins early and often, are key to my process of managing stakeholder expectations and ensuring a successful transformation.
I believe that communication is the most important skill of any strong product person. It sounds like a cop-out, but it’s true. To get your message across, you need to be able to adapt it to different people, different skill sets, different understandings.
I have a good example of this from when I was at Verizon, a quintessentially big and complex company. I was tasked with driving mobile app engagement. Despite the slow-moving nature of the company, we were able to demonstrate quick wins by rapidly prototyping new features based on user feedback and data, intentionally going against the grain of how product development was traditionally done there. This way, we could demo the “art of the possible” to our leadership. This early success helped us get buy-in and clear roadblocks, which was key to driving faster change.
At TED, our content can be consumed anywhere — on our site, app, social and podcast platforms, YouTube, etc. YouTube is where a lot of our content is consumed, and that’s great because it has a huge reach and is where many people can discover TED for the first time. Their product and video player has useful features that we will never be able to recreate based on sheer capacity.
Of course, anywhere someone is watching our content is great, but we’re constantly thinking about how to create differentiated experiences in our owned and operated products to establish closer relationships with our most engaged audiences.
In addition to the existing, completely free, benefits of our site and app that people can’t get elsewhere — such as personalized TED recommendations and playlists, dubbed content, newsletters, downloadable content for offline access, and more — we recently ventured into a totally new arena for us in the games space. As we announced earlier this month, games and puzzles have long been a part of TED’s identity, and we’ve extended that thought leadership into the casual game space.
This is an example of how we’re both building upon and improving our world-renowned “TED Talk” as well as innovating to create new content and engagement formats to, again, motivate, inspire, and educate our audiences all across the world in new ways.
Incorporating customer feedback and understanding user behavior and needs are critical elements of any effective product strategy. Pairing the qualitative and the quantitative is the best approach. My process typically starts by assessing the data we already have available.
In consumer environments, there’s generally a higher quantity of data at your disposal. This allows you to draw user behavior patterns and generate hypotheses and questions. Then you can leverage user interviews, surveys, or other qualitative mechanisms to gather deeper insights. Essentially, you begin with data to understand the “what” and then use qualitative research to dig into the “why.”
After identifying data gaps, you can work with the team to address them. However, it’s important not to fall into analysis paralysis — waiting for perfect data isn’t realistic. As Jeff Bezos said, most decisions only need about 70 percent of the desired information. Keeping that in mind and treating reversible versus irreversible decisions accordingly is essential.
Yes. In B2B settings, where you may or may not have vast amounts of quantitative data to start with, regularly prioritizing customer feedback sessions on your schedule becomes more important. The benefit of having a smaller user base is that you can understand individual segments more directly.
When I worked with B2Bs, I frequently shadowed customer success calls to stay in close contact with the customers and end users. For consumer products, on the other hand, due to the sheer size of the user base, you can’t engage with all users directly, but you often have more quantitative data to inform your decisions.
Regardless of the environment, I find that when product teams become too far removed from their customers or users, the result is often a low-quality product.
This is an important question, especially with all of the well-documented environmental concerns around AI. Significant energy consumption and carbon emissions are required to train and run these large-scale models. At TED, sustainability is at the forefront of our ideation. For example, in our AI dubbing project and previous experiments with AI talk companions, quizzes, and video summary tools, we have prioritized approaches that allow content to be generated once and reused.
When we experimented with summarizing talks into written articles, we ran the AI prompt once per talk and made that one summary available to all users rather than generating a new summary with each page load. An approach like this allows us to save substantially on costs as well, which is critical for us, especially as a nonprofit.
One interesting trend is the increased use of AI and data and how that’s being leveraged to create more personalized user experiences for donor engagement. Organizations with smaller budgets can employ these tailored engagement communication and fundraising strategies to improve the efficacy of donor relationships. The cost savings and the increased accessibility of technology through AI will allow nonprofits to do things they couldn’t previously afford.
Data privacy and ethical tech will also continue to be a bigger focus. As data privacy concerns grow, especially amongst donors and stakeholders, nonprofits will need to adopt stricter data protection measures. The bar will get higher to implement stronger data governance policies and the ability to opt in or out easily, and excuses about limited resources won’t hold up. Ultimately, these facets will fundamentally impact donor confidence and the organization’s reputation.
I also foresee a trend around community and social impact integration. Nonprofits of the past could be seen as stoic and stagnant. The digital strategies of these organizations will now need to focus on building vibrant online communities where donors, beneficiaries, and volunteers can interact. It will no longer be a one-to-one relationship — It will be about bringing all constituents and stakeholders supporting the mission together in new and dynamic ways.
Within the edtech space, one of AI’s most exciting potential lies in its applications for educators. When we think about edtech, we think about the students a lot, but teachers and educators are also a super high-leverage audience; they’re a huge value-multiplier. Tools that can help them augment their curriculum and learning materials much faster and easier will be game changers. For students, companies like Khan Academy have built personalized curricula tailored to students’ needs and work with them as 1:1 tutors. So, there’s tons of potential there as well.
In the media industry, the challenge will be the pressure to balance the use of data for personalized content delivery with the need to protect user privacy, as I mentioned. As data privacy concerns grow, media companies must reduce reliance on third-party cookies, enhance data encryption, and give users more control over their data. This balance will be critical for maintaining user trust while still driving engagement.
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