Episode 174: Integrating AI & Transforming Workflows with Anthony Maggio, VP & Head of Product at Airtable
I recently sat down with Anthony Maggio, Head of Product and VP at Airtable to discuss combating challenges within the digital supply chain, addressing strategy drift, and how Anthony is spearheading Airtable’s newest: Airtable AI.
Airtable is all about connecting people and data, and connecting with Anthony to hear his clarity of thought and collaborative ethos, it was clear to see why he’s become such a trusted figure in this space. You can read about some of the fascinating thoughts he shared with me on AI integration below.
You’ll hear them talk about:
05:39 - Airtable has gone from a product that is flexible and extendable to a wide variety of companies into a more vertical focused company that has successfully integrated AI into its products as well. Anthony shares Air Table’s approach to allow the people closest to work are also closest to the software, to allow them to optimize their workflows. It’s fast, easy to deploy, and fully customizable. The next stage is focusing on helping larger companies adapt and build custom software using the platform and becoming the fastest and the easiest way for companies to bring AI into business processes.
13:52 - Vertical companies can share a fear of becoming too narrow and limit themselves to which markets they can reach. Anthony speaks about how Airtable is able to move past this worry as it first began as a flexible platform. He shares how Airtable is built on a flexible foundation, which allows them to introduce new capabilities while still maintaining a horizontal approach to meet the needs of a larger market.
22:16 - AI is a significantly disruptive technology that is transforming across all industries. Anthony shares how AI will transform the role of product managers, and it will start by raising their expectations. AI is becoming much better at intelligent strategy and is already being used to proactively monitor and engage with market and customer insights. Anthony highlights the importance of product operations in identifying high-impact opportunities for AI transformation, and using AI to surface actionable information for product teams.
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Intro - 00:00:01: Creating great products isn't just about product managers and their day-to-day interactions with developers. It's about how an organization supports products as a whole. The systems, the processes, and cultures in place that help companies deliver value to their customers. With the help of some boundary-pushing guests and inspiration from your most pressing product questions, we'll dive into this system from every angle and help you think like a great product leader. This is the Product Thinking Podcast. Here's your host, Melissa Perri.
Melissa - 00:00:36: Hello, and welcome to another episode of the Product Thinking Podcast. Today, we're joined by Anthony Maggio, the VP of Product Management at Airtable, the platform trusted by big businesses around the world to connect their people and data and achieve their most important goals. Today, we're going to talk about combating challenges within the digital supply chain, addressing strategy drift, and, as you may have seen in the news, their new product, Airtable AI. But before we dive into that, it's time for Dear Melissa. And this is a segment of the show where you can ask me any of your burning product management questions. Let's see what the question is today.
Dear Melissa, I work in a B2B2C organization leading our product function. Our customers ultimately care most about retaining students at their institution, but their purchasing behavior doesn't align to that. We lost out on deals as a result of not having a robust amount of features as our competitors, despite those features largely being inconsequential in driving student retention. This has resulted in our executive leadership team developing a mindset where our product strategy should be highly indexing on the requests and demands of our customers and under-indexing on the needs of our consumers. I'd love to hear your thoughts on how to balance the needs of the B and the C in your product strategy.
All right, so B2B2C is always a very challenging proposition here. Ultimately, you want to satisfy the consumers at the end of it, but you've got to get that deal first. So there is no cut and dry way to actually do this. But I have seen it where a lot of that B part, you know, your customers who are actually purchasing these things do request certain stuff. So you have to understand what their goals are and what they're looking for and how they're being measured for success. So when they're evaluating your product, they're usually going up against competitors. And sometimes in that evaluation piece as well, they might just be doing a parity analysis. If they're doing a parity analysis and they're being judged for getting something that's the most robust feature set, they're going to go with your competitors. They're not. They're really going to be looking at what the outcomes are. Now, the way that I've seen people get around this is by having extremely strong value propositions and showing data and reports on how their products drive better retention with their features instead of their competitor features. So could you do that, right? Could you help show them and demonstrate why you're better than the competitors, even though you might not have the same features? At the end of the day, though, you have to remember that those customers are usually the ones who are paying your bills. In a B2B scenario, those are the people who are actually paying you and then they're taking their product and then putting it out there. So for example, if you're selling something that is what it sounds like to me is that is going to impact students at the end of the day, but you're selling it to universities, you have to understand what your customers need, right? And what they're being judged for success on. That is an ultimate goal, right?
That's a huge part of product management there. It's not just about understanding what your user personas are, but also what your customer personas are. So that's where I think you do have to recalibrate and come back in and make sure that you have very distinct customer personas and you understand who the purchaser is. And then you also have your consumer personas and you're making sure that you're keeping really good data and you're creating great reports to show that your product is helping your students at the end of the day, which is driving results for the university. Well, let's take a minute and actually think about what people are making decisions on when it comes to universities. And I've worked with a couple of companies who do sell to universities. If you are trying to do something that helps retain students, you're probably going to want to show that they are retained more by using your product. You're going to want to show leading indicators for that. They want to measure test scoring. They want to measure how much they're going to class. They want to look at dropout rates. They want to look at classes drop, things like that. You want to make sure that you are in tune with all that information and doing good reporting for your customers on that side so that they can make sure that they're getting the value out of your product and they can compare it. Now, during the sales strategy, during your sales process, there are times where you do have to make some concessions and build something so that you can check a box and say, we have parity if it's really preventing you from being able to sell and enter into that market. And this is where maybe we just do a cursory feature where we do something lightweight just to check the box. It's the nature of just being able to sell things.
But what I'd really do in this case is go back and say, why does that feature matter to you, right? What do you do with that feature that our competitors? What's value is it actually bringing to you? What is that thing going to actually help you with to achieve your goals and get them to explain that in situations where I've had to do that? A lot of times people are not able to explain that value and then they reconsider or there's something that we didn't understand from that customer about why they needed those features or what it was actually achieving for them. And because of that, we were able to build them similar features or we were able to build them something different that actually helped them get to that value. So. I'd really try to do that. But in the same way, if you have a B2B to C company, you need to always be balancing your customer persona with your end user persona there. Because if you ignore the customer, you will go out of business. And sometimes it doesn't make sense what they're requesting. And it's totally okay and within your realm to go through those requests and really poke on them and make sure they're important. But I would be careful not to discount them. Because at the end of the day, while they do want retention as an outcome from those students. They're probably being judged for success on a lot more factors inside the university to keep their job and to be promoted and rated. And you have to deeply understand that because your customer persona is just as important as the end user persona as well. So that's why I would really dig through those requests. And then sometimes you have to make concessions. This is all part of the business, but I would be really careful not to just copy all your competitors. Think about your positioning.
Think about how you explain how you're better than competitors. Think about data you could bring. Think about different ways that you could actually show them why you're better and why that retention is gonna help them at the end of the day. So I hope that helps. If you have a question for me, go to dearmelissa.com and let me know what it is. I will answer it on an upcoming episode. And now it's time to talk to Anthony. Are you eager to dive into the world of angel investing? I was too, but I wasn't sure how to get started. I knew I could evaluate the early stage companies from a product standpoint, but I didn't know much about the financial side. This is why I joined Hustle Fund's Angel Squad. They don't just bring you opportunities to invest in early stage companies, they provide an entire education on how professional investors think about which companies to fund. Product leaders make fantastic angel investors. And if you're interested in joining me at Angel Squad, you can learn more at hustlefund.vc. Find the link in our show notes. Welcome, Anthony. It's great to have you on the podcast.
Anthony - 00:07:27: Thank you so much. Nice to be here.
Melissa - 00:07:29: So you have had a great product leadership career so far. What brought you to Airtable? Can you tell us a little bit about your journey and what you're excited about right now?
Anthony - 00:07:39: Absolutely. So I've been a fan of Airtable for quite a long time. I actually started using the product back in 2014 when I was running a small startup in the hospitality technology space. We were building enterprise software for hotels to help with things like guest check-in and messaging and upsells. And we found Airtable early in that journey and it took off pretty immediately within the company. We were about a 30-person company and were using Airtable to run our product road mapping and vendor management and CRM. And that's really how I initially got acquainted with the product and the team as an early power user, someone who gave a lot of feedback to the co-founders of Airtable back in those early days. I went on after selling that company to bring Airtable into InVision where I worked for four years leading the enterprise and core product teams. And again, we were using Airtable there to run our product operations and product road mapping. And so when I decided to leave InVision in 2020, Airtable was actually one of the first places I looked. And the timing worked out well in that they were just starting to build a product management function. Although Airtable had actually been around for about a decade, they never really had a formalized PM function. They had product management, lots of people performing the functions of product management in engineering and in design. But around 2020, as the company started to focus more on Enterprise customer, they found the need for the building out a formalized product function and that's around the time that I joined in there for about three and a half years now. And it's been a great journey.
Melissa - 00:09:18: That's so funny how some of the most successful companies that we look at there sometimes don't start with a product management function. But as they start scaling, it becomes a need, right? And they start gravitating towards it. When you came into Airtable too, I'm curious, building out this product management function, what did it look like? What did you get started with? How was the company shaping up?
Anthony - 00:09:37: We had a really unique opportunity to think about how we wanted to build this function, what we wanted the role of product management to be at Airtable. And like I said, lots of great work happening across engineering and design and I think a very, very product-centric team. But as we approached building the function, we wanted it to be an additive function. We didn't want to just take the roles or the work that other people in engineering or design functions were doing and move it into product management. And so we really had the opportunity to say, what should Product Manager here be responsible for? And to craft this vision around product really. Defining where the business is going and aligning our customer needs to new business opportunities and how Airtable as a platform could be shaped to go after new verticals, new lines of business, new functions within the enterprise. And so that's really how we formulated and encapsulated the role of product management as kind of marrying the customer needs, the market opportunity, and where Airtable's platform was best suited to serve some of those needs to create new lines of business, new business opportunities. So we intentionally set the goal for PMs to be really forward-thinking, to be planning where Airtable's going over the next quarters, not necessarily over the next days or weeks. And it's been a great transition for the company. I think we've seen that having PMs be focused really on that future state and how to create more product market fit for Airtable has been really beneficial for all of the teams that work with Product Managers.
Melissa - 00:11:14: Airtable has been through a really interesting journey because I remember when you started, you were more of a broader platform that a lot of people could come in and shape into whatever they wanted. And then a couple of years down the road, I know product management became a really big piece for Airtable where people started putting product roadmaps in it. You started defining templates for that. And now Airtable's mission is really about empowering organizational workflows, which I think is a really interesting pivot into that. And I've seen a lot more definition and a lot more verticalization coming out of it. And you just recently launched Airtable AI, which I think is a great product. Can you tell us a little bit about first, how are you leveraging AI into Airtable to do all of these things? But what was that journey like from going from a product that kind of almost did everything or could be so flexible and so extendable for everyone into more verticalization and then gradually into AI as well?
Anthony - 00:12:07: It has definitely been a journey, but I think it all comes back to this kind of core premise that we believe that this model, the historical model of buying rigid point solution software or internally building software is broken and that no-code platforms are really going to be the next wave of innovation around how software is built and designed. And when you think about the way that historically software was created in the enterprise, a company either bought or built a piece of software for a particular purpose. People within the organization then started using it, and they very quickly identify ways that it should be improved. They have feature requests, or they have changes to their workflow that could be better supported by software. And the challenge has been that historically, the cost of implementing those changes was really high. They ended up in a backlog for developers of an internal tool, or they got kind of thrown into feedback for a SaaS product that... Yeah. Maybe never saw the light of day. And so Airtable's premise has always been that the people who are closest to the work should really be in the position to define how software works and how it supports their workflow, and that software should be really fast and easy to deploy and customize. So that hasn't changed. That's still very much the case. But what we saw over a decade of building Airtable is that many of the larger companies who came to Airtable did have very similar needs around the workforce, they were looking to transform. And so those tended to be workflows around product operations, running the whole product development lifecycle from end to end, around marketing operations, thinking about the planning and execution and management of campaigns. We see a lot around physical product workflows.
So retailers who are designing apparel or designing other physical products are similarly store and location-based operations in retail. And so that was really what led to this approach of starting to verticalize from a go-to-market standpoint to say, you know, we're seeing many of our existing customers transform the same types of workflows with Airtable. Let's start being more intentional about how we go help the next wave of companies who have the same need adapt and build custom software using the platform for those solutions. So that's the background on verticalization. In terms of AI, I think every SaaS company to some degree has gone through this journey over the last year and a half, where in 2022, early 2023, as we saw AI starting to become mainstream and consumers and users becoming familiar with the technology, we spent a lot of time meeting with our customers and understanding where they saw opportunity to bring AI into their businesses and where they were excited to deploy AI within their workflows. And so we launched an AI product in beta early in 2023. We had about 1,200 organizations participate in that beta and generated millions of AI responses through early versions of this product.
And really what became clear to us early on was that the same philosophy that we've brought to custom software development through low-code tools was going to apply to AI, that most companies were actually looking to their own teams and their own departments to identify where are there really interesting or practical use cases, That we can take this technology and apply it to transforming a workflow, to automating steps that are being done manually or to assisting humans in the processes that they work on on a day-to-day basis. So early on, our goal became, how do we become the fastest and the easiest way for companies to start bringing these AI superpowers into their own business processes in the ways that really only they know about? And that may be... In some cases, very unique to their company, to their process. And so we put a ton of focus on making this technology, again, accessible to the people closest to the work so that line of business users could identify opportunities of where AI could impact the way they're doing work, very quickly deploy them, and then test and iterate on improving their process with this technology.
Melissa - 00:16:25: So going back to the ICP work that you did too, before we dive into that, I've seen a lot of companies these days are trying to figure out, do we verticalize or who's our ICP, right? Who's our target ICP? What do you think the role of the product leader is in defining those things? And then also, how did the rest of the product managers or company participate in bubbling up who we should be targeting? Like, were you going out and doing user research and product managers were like, hey, this is what I'm hearing over here? Or how did you tap into different functions to really hone in and be like, there's a bunch of companies out there and they're doing all similar types of things with our product. Maybe we should be targeting them more. Maybe we should be going after them more.
Anthony - 00:17:08: It's a great question. And so I'll answer your first part of that first. What is the role of product leaders in defining this? I think product leaders need to take a very active role in that verticalization strategy because they're uniquely positioned to look at the market, look at the customer needs of those markets, identify where the product can best suit some of those customer needs on a repeatable basis, understand the competition in those markets and who you do or don't want to be focused on competing with. And so that has absolutely been a big part of the role of product leadership at Airtable. It's also a very tight collaboration with go-to-market because ultimately, as you are verticalizing a platform and defining the specific ICPs that you want to go after, you want to make sure that your sales and marketing teams are also really excited to go after those ICPs and that they feel confident selling into those markets. And feel confident about the unique differentiation that the product can bring to customers in those markets. So it was a very tight collaboration to define some of the areas that we really wanted to focus on and the verticals and functions where we've seen the most traction and wanted to invest in further.
In terms of what that looked like, the first phase was really a bit of research to identify where we've been most successful today, who are the companies that are already coming to Airtable and what are the types of solutions that they're looking for. And we did some vertical mapping exercises to look at, okay, the common verticals that we see coming and building on Airtable are tech and retail, hospitality, media and entertainment, financial services, and then within each of those, what are the functions that are typically the largest deployments of Airtable and are really using Airtable to build more end to end process tools across their entire departments rather than just team-based productivity use cases or workflows. And so from there, a few different themes started to emerge. We saw a lot of patterns around product management, around marketing, around retail and store operations and manufacturing. So those became some of the initial target verticals that we began building towards intentionally on the product side in terms of supporting new integrations, new visualizations, new workflow capabilities that are unique to some of those areas. And then also on the go-to-market side in terms of building the customer stories and the messaging and the demos that would really position Airtable to serve some of those types of customers.
Melissa - 00:19:47: So when you do get into verticalization, I've seen a lot of companies get scared that they're going to kind of limit their markets, right? Or they're going to make things too narrow. What do you do strategy-wise to make sure that your product is still extensible and adaptable, but also be able to target markets when you have a product like Airtable that could be used technically by a lot of different types of companies?
Anthony - 00:20:10: I think that is really one of the benefits of Airtable in that we started as a platform. And so we were fairly unique in that way. When you look at a lot of the other enterprise platforms in the market, like Salesforce or ServiceNow, many of them actually started with a specific point solution and then kind of branched into platform from there. And Airtable has been unique in that it actually grew up as a very horizontal, very flexible platform. So that has not been too much of a challenge or problem for us. The core of the product foundation is built in a very flexible way. And each time that we build and Anthonyduce new product capabilities, even if it is born out of one particular vertical, we will look at how that same product feature or platform feature could be used across teams or businesses in other functions and in other verticals so that we can build it in a fairly horizontal way. And we can bring it to market in a way where we understand that even if a feature, we'll say like a Jira integration, might be used predominantly by product management. And by product operations teams, it's actually a lot of teams across many other functions and marketing and operations that will also leverage that same type of integration. And so we use those opportunities to look at what the workflow will look like in its core use case, as well as how it might be utilized by other functions to design it in a way where it will suit and meet the needs of the largest market possible.
Melissa - 00:21:40: What's your process to when you're embarking on this between working with product marketing and sales and product to make sure that you're aligning across those verticals and all kind of going together after one or another or a few?
Anthony - 00:21:53: So we typically will pick two kind of areas of focus for a given year. And that allows us to really put a concerted effort across product, sales, product marketing around where we're all going to be focusing and rowing in the same direction over the course of a particular time period. We kind of think of those as being a unified go-to-market effort that allows us to really collaborate together on the messaging, on the marketing, on the customer story development, on big new kind of product features that will better support one of our target functions or verticals. And the organization only has so much capacity to focus on any kind of number of new functions or verticals at any one time. So we found that picking kind of one to two over the course of a year to really put a lot of emphasis on is a great way to unify the organization. And the way that teams are coming together. I would say also, you know, AI actually has been a motivating force there as well. Like we have seen that there are certain workflows and certain opportunities within particular functions that are really ripe for innovation and for transformation with AI. And that has actually also played a factor, you know, particularly this year in where we've decided to focus. So the product development lifecycle, for example, we just see so many touch points and. So many interesting areas to innovate with AI that we've put a lot of focus there this year to really, you know, think about how can we help the product teams across that whole lifecycle from discovery to planning to executing on their roadmaps to launch management. And so looking for those areas and opportunities where a new technology like AI comes around and where can we marry that into a process that is really ripe for disruption.
Melissa - 00:23:49: Did you know I have a course for product managers that you could take? It's called Product Institute. Over the past seven years, I've been working with individuals, teams, and companies to upscale their product chops through my fully online school. We have an ever-growing list of courses to help you work through your current product dilemma. Visit productinstitute.com and learn to think like a great product manager. Use code THINKING to save $200 at checkout on our premier course, Product Management Foundations. When it comes to AI, there's a lot of companies out there too who are, they're trying to figure out what to do with it, right? They sometimes don't even know what they can do with it or what they would need to do with it. How did you go about working with your clients and talking to them and approaching that topic without just being like, hey, what do you want to use AI for, right? Or what are your tolerance for it? How do you kind of navigate that discussion so you can get ahead of their needs instead of just having them be like, I want AI for this, right? And it makes absolutely no sense.
Anthony - 00:24:47: Exactly. And that has been, I think, you know, a really unique challenge and definitely something we saw a lot of last year during our beta is that you'd go into certain conversations and either the use cases that a customer was coming up with were just not a great fit AI. They were things that, you know, maybe were not at the right kind of problems to solve with that technology. Or on the other end, the ideas that they came up with were sort of so forward-looking that they're not really practical given kind of the state of the technology today. You know, but as a process overall, like we spent some time early on, actually even ahead of our beta, identifying areas where we saw a lot of opportunity for AI to influence the workflow. We actually picked starting with product and marketing to kind of workflows that we mapped out end-to-end. We said, you know, let's look at in an enterprise, what are the common sequences that a product management function will go through? So what are the things that happen in Discovery, where do you get user feedback from? Where do you generate insights from your go-to-market teams? And then in planning, like, you know, how do PMs map their potential backlogs of projects towards company goals? So we went through each step of the life cycle and we ran some brainstorming exercises to say, okay, what problems do we feel high conviction that AI can solve within each of these workflows?
And then we actually went and tested them internally. So for product, we, you know, within a matter of months, we were able to do a lot of work. We actually completely redesigned our own product development life cycle tooling in Airtable to incorporate AI. We started actually gathering all of our user feedback across every channel. So forms, direct user submissions, sales submissions, integrating Gong calls and recordings from our team and built an AI-powered feedback repository that could extract insights and make suggestions to PMs about areas to focus. So, you know, that was kind of the internal exercise that generated a lot of these types of opportunities and suggested use cases. And then we brought those into beta. And so as we had conversations with the 1,200 organizations who worked with us on the beta of this AI product feature set, we provided those as an initial set of use case inspirations to say, here are some of the workflows where we've already seen a lot of impact from AI and where we think, I think it can be pretty transformative in your workflow and then helped them bring some of those same opportunities into the applications that they were powering.
Melissa - 00:27:24: There's been a lot of talk lately, too, about how AI is going to put product managers out of a role or completely disrupt like what we do. What's your take on how things are going to change for product managers specifically with AI?
Anthony - 00:27:38: There's so much discussion on this. I was just looking at a thread this morning that Lenny started on LinkedIn on this topic with his most recent newsletter. And I do think that it is going to be extremely transformative for the role of PM. I think that the biggest thing in my mind is that I think that AI is really actually going to increase the expectations of the PM function. So when you think about the impact on the role, there's a lot of things that PMs do that AI is actually already quite good at. Taking data and analysis from many different sources and using that to kind of craft strategy and set goals or write PRDs. I think we're not all the way there yet, but you see that with the pace of model development, LLMs are actually becoming quite good at some of those types of activities. So on the strategy side, I do really think it's going to raise the expectations. I think as PMs, we're expected to know the market, know the customer, know the business, all to inform the product strategy. And historically, there have been so many inputs that it is difficult for PMs to stay on top of all of those things proactively, right? What has actually happened in practice is that you probably kind of loosely follow your market and competitive news. You meet with customers, review feedback on some type of basis. But, Now it's actually very quickly becoming possible to monitor and proactively kind of engage in these sources in real time. So for customer feedback, for example, what are the themes that your customers are talking about right now? And how do you have confidence as a PM that the products that you're building or the features that you're developing are actually addressing the top volume of customer needs or the biggest revenue opportunities that you see out of your customers feedback.
Or on the market side, what has changed in your market landscape this week? What are the latest movements of your competitors? Or even actually monitoring and analyzing things like 10-K forms from your customers? What are the commonalities across changes that are impacting your customers' business? I think that on the strategy side, PMs are now going to be expected to be able to speak to these types of inputs in near real time. And so product, and product ops teams, I think are going to play a really, really important role in ensuring that they have systems and toolings to be able to capture and measure this data and use it to inform strategy. And that's kind of one of the areas that I think we'll just see the biggest shift in, in that it will really just become an expectation of the role that you're using this technology across all those different verticals to keep a pulse of your business. So that's the raising expectation side. I also think that, you know, maybe on execution, it will make a lot of things easier. And there's so many parts of the PM role that end up being focused on non-value added work. Actually, one of our customers recently used the term bad admin for this. You know, they said like, our PMs are just stuck in bad admin.
They're spending so much time like writing weekly updates or preparing decks for executive strategy alignment conversations. And on that side, I think that we will see AI significantly reduce that type of bad admin work that PMs end up facing the brunt of in many organizations. And we're already seeing a lot of interesting use cases that our customers are coming up with to use AI to reduce that type of admin work. So I had actually someone just show me last week how they nearly, you know, have automated their sprint reporting and executive reporting using AI work, or feeding all of their last sprint Jira tickets into an AI prompt, as well as everything that's planned for the next sprint and a few bullet points from a PM to kind of add the narrative and auto generating executive status reports using all of that data. So you can see already some ways where some of those more administrative tasks will become automated very quickly.
Melissa - 00:31:57: That's really cool. I want to see that too. I think all that stuff, I'm like, it goes so hand in hand too, I think with product operations, which you mentioned before as well. Can you tell us a little bit about like what you've seen out there, since this is a hot topic of mine, with product operations and how you've seen it integrate into product teams and what that looks like when it's doing it well?
Anthony - 00:32:18: I think product ops, especially as it comes to AI, is really carrying the torch here. And I've personally met with so many product operations teams that are so motivated to look at, like, how do we use this new technology to improve our processes? It's actually the number one piece of advice I give to other product leaders when they come to me and say, how should we think about incorporating AI into our process and into the way that we're working? The first thing I always say is, like, go partner with your product ops function because they're going to be really excited to look at these pain points across the organization. They have a unique vantage point to see how teams are working across different pillars, across different groups, and to really identify the highest leverage, highest impact opportunities and where there is so much pain in the current state. So I've seen... I mean, a ton of product ops teams, you know, really kind of leaning in to this change, certainly within Airtable. Our own product ops team has really kind of led the charge around looking at a lot of our own workflows and coming up with really interesting ways of incorporating AI into the way that we are working and, you know, many things that, frankly, like we just hadn't even thought of when this technology became available.
We had a use case that just popped up recently where our product ops team in conjunction with product marketing and customer education actually started auto-generating the scripts for new customer education videos. So they'll take a product roadmap item, the PRD from that product roadmap item, send it into an AI prompt and use it to actually draft the first draft of an education video that explains how people can use this new feature. And so that use case, the video teams said it actually reduced their time for creating a new video from several days down to about eight hours just by being able to quickly synthesize and aggregate all those insights together. So definitely, I think just a huge role and opportunity for product ops to look across actually not even just the product teams, but all of those intersections of where does product interface with customer-facing teams, with marketing teams, with program management functions with release management functions, and to identify where are these high leverage opportunities and high pain in current state opportunities that are ripe for AI business process transformation.
Melissa - 00:34:52: That's really interesting. I like that use case. One of the biggest issues I've seen, I think product ops helps with this a lot as well, is aggregating insights from different parts of the organization back to product teams so that they can make choices off of it. And I know you just mentioned that AI could surface up some of those insights. How do you do that at Airtable? How do you make sure that product managers still can hear what sales is hearing from enterprise customers versus collecting all the user research from around the organization and putting it into a system where you can actually view insights and sift through them?
Anthony - 00:35:24: This was the first use case that we really built internally. And it has been the first use case, I would say, that most of our customers in product ops are most excited about pursuing. So it's such a ripe area for opportunity because you think about in most organizations, the volume of product feedback or customer feedback that you get is just so high that it ended up turning into a black box, right? Like you had requests or feedback coming in directly from your users. You have it coming in from your sales teams. You have tons of feedback that's just trapped in call recordings that no one ever took the time to actually enter into a form or to share in Slack or to make visible to anyone else. And nearly overnight, AI has now solved this problem. And so what we did internally was we built a system to aggregate all of those different sources into an Airtable app. Then to use AI to pull out things like the sentiment, to tag it with different themes and categories that we saw from a particular item of customer feedback. We use it to route the feedback to the most appropriate product team and product area and really kind of turned what was historically a very reactive and underutilized source of data into now a proactive opportunity generating machine where we can actually surface to PMs.
Hey, here are some of the top themes and top sources of feedback that you are seeing in your own product areas. So that's kind of step one is making the information available, actionable, easily accessible to everyone in the org. But we still also do rely on a lot of direct customer interaction from the Product Management team. So the way that I like to think of it is that those insights that you aggregate become the starting point for areas of deeper investigation. You might see a particular product area or product theme coming up with some regularity, and that should really be a trigger for a PM to go start engaging in deeper customer conversations, start engaging with the go-to-market teams to maybe do more competitive research around that area to more deeply evaluate the opportunity. But it has been quite transformative for us and I think for a lot of the organizations that have been part of our beta and have been early adopters of actually bringing AI into their product operations flow. It's certainly where we're seeing the most pull in terms of compelling use cases to focus on.
Melissa - 00:38:02: From what I'm gathering from this conversation too, I imagine that at Airtable, you do a lot of dogfooding your own product, right? Using it for the same use cases that your customers might. How do you make sure that your PMs are not basically getting swayed by things they might need versus what their customers actually need, right? Something that might be more internal to Airtable because we all want to fix our own problems, obviously. But I've seen that be an issue sometimes with tools where it's like, oh, we built it for ourself, but then now we're selling it to customers. Now we have different types of customers who might not have the same exact needs as us. How do you kind of balance that?
Anthony - 00:38:39: You're right. We do dog food, nearly everything on Airtable. We use Airtable to run most of our internal processes. But I think the antidote for that is and for ensuring that we are listening to customers is partly what I just described. Actually making the voice of the customer really visible internally. That product feedback application that I just described is actually available to everybody in the organization. We can all see what the top themes are, what the top product gaps are that are coming from our customers. The insights that are being pulled from calls and surfaced through our go-to-market teams are all very visible. And then we set the expectation with our PMs that anytime we are building or proposing new product functionality, new features, they're really centered on the customer needs, not our own internal needs. So every one of our Product Requirements Documents are expected to reference, incite specific customers with specific use cases that a new feature or product will support. And that's how we've really built a culture of orienting everything that we do around the needs of customers. So not just in the abstract sense that, oh, we think this feature might help a certain market or certain kind of ICP of customers, but specifically, who are the people and the teams and the workflows within these accounts that are going to leverage this new product capability.
Melissa - 00:40:09: A good trend there that I like that sometimes I don't see in other organizations. I'm curious too, from an AI standpoint, we've been talking about it a lot. For you personally, like, what are you excited about with the future of AI that we can't do today? Like, where do you see it evolving that's not, let's say, so great today that you're really looking forward to?
Anthony - 00:40:31: So I really think we are just scratching the surface on this technology. And the pace of model development and model improvements has been so astounding that it is actually hard to predict where are the most exciting opportunities going to be, not even three years from now, but three months from now. So there's a lot of areas you can probably tell that I get very excited about talking about this. I think to zoom out a bit, Airtable's mission statement was to democratize software creation, meaning our goal is to make it possible for really anyone, any non-technical person to create their own software. And what we're seeing now with the advent of really powerful LLMs is that that is truly becoming a reality faster than we even anticipated. We're working right now on an initiative that uses AI to allow someone to build a fully functional app, kind of end-to-end process application based on a simple prompt. So you could come into Airtable and say, I'm a podcast host. I need a CRM app that's going to track all my upcoming guests and conduct some background research on them, look up their profiles, their prior media appearances, and prepare a first draft of questions that I'm going to ask them. And within a couple seconds we can actually spin up this end-to-end application that can power that whole workflow with all the integrations and views and visualizations and data sources that are needed to actually power that. So that's going to be possible within this year. That's something that I think was hard for us to just imagine that being a possibility even 8 to 12 months ago.
Melissa - 00:42:18: Well, I'm looking forward to that. That could be extremely useful. Love it. Well, I'm excited about this future that we're painting here for AI too. Anthony, thank you so much for being on the podcast today. If people want to learn more about you and about Airtable, where can they go?
Anthony - 00:42:35: They can find us at airtable.com or airtable.com/careers. We're always looking for great product folks to come and join our team.
Melissa - 00:42:44: Great. And we will definitely put those links in our show notes. If you go to productthinkingpodcast.com, you will find them there and you can find more links about Anthony as well. Thank you so much for listening to the Product Thinking Podcast. We'll be back next Wednesday with another amazing guest. And in the meantime, if you have any questions for me on product management, please go to dearmelissa.com and let me know what they are. We'll see you next time.