Tom Purves – Product Management, Fintech & AI

1. leaner teams – Generative AI is pretty great, at a lot of generalized PM tasks. Anything that’s boilerplate or commonly used across organizations: product requirement outlines, commonly used frameworks, agendas, role descriptions, typical project plans. PMs often struggle to find enough time to write content and copy, or internal or external audiences. GPT can absolutely help with first drafts of user documentation or help content. It can also help you spin up, more personalized copy, implementation guides or docs in greater breadth or depth than you could otherwise ever do unaided. GPT and others are about 80% great at the basic stuff. It’s also pretty helpful (like in the sense of Wikipedia or google searches in the old days) for quickly learning, at least superficially, a fair bit about nearly any well-established technical, business or industry topic. And, having to know/learn a little bit about everything, is half the job product management. Generating basic code can be handy too, esp if you are a little rusty as a PM. Code interpreter can help with generating SQL or Excel VBA scripts to pull or scrub data. Up to and including kicking the tires your own or 3rd party apis with simple prototyping without fully distracting the engineering team.
   
   Of course, LLMs are still (and I can’t stress this enough) terrible at creating ‘new’ knowledge. Generative won’t help you much with anything that was never in it’s training set. To be sure, Sam Altman’s stated goal is to create an AI that can truly invent new ideas and new knowledge. But don’t expect that to change in 2024. You still need humans to bring true insight, to separate the salient signals from the noise and to actually innovate. In theory though, today’s AI should be allowing you a little more time in your PM’s oft-harried days to actually do that.

2. New tooling – I love tools like Otter.ai and Gong that can do remarkable jobs of transcribing and annotating live internal or customer sales/service conversations, and then turning those into collaborative assets for actionable insights and roadmap ideas. New design tools like visily.ai make it possible to import screenshots to figma. A couple weeks ago, I need a way to better visualize and idea for a client. So I navigated to an archetypal customer website and screen-shotted their checkout flow into an AI-generated figma. And in the space of one cross-country flight, mocked up multiple ways my client’s embedded fintech product could transform that category of checkout experiences.

If you are looking for more, Product school also maintains a moderately updated list AI-driven PM tools.

3. Mix Shift to Hard skills – Now lets talk about building AI, not just using AI. For organizations investing in proprietary AI tech, I am seeing engineering and data science leads driving the roadmap and product strategy. Somewhat less, for now, is the primacy of the UI/experience focused PM. It used to be, the riskiest assumption in tech was, if we build it, will they come? With AI though, the riskier, more expensive feedback loop is going to be can we build it? From data sourcing to training, tuning, retrieval augmentation and scalable inference – good AI is not trivial to build. And even when you can get good results can you manage for safety, compliance, un-good edge cases? AI than some of the past waves of web and digital tech. More of your engineering backlog is going to be consumed by engineering challenges, technical features, as well as more platform engineering and  data science. We’re also likely to see more product teams led by engineering or technical leaders.

Can AI replace your product team 2024? Well hardly, but I’d hope it’s helping them work at least 10-20% smarter and more efficiently as well as helping them be stronger generalists.

Between  the above effects of efficiency and mix shifts I do see likely change for the size, process and composition of product orgs. You may see a higher working ratio of engineers and data science to traditional PM roles. Some PMs will start to feel a little un-tethered if their skill sets are less well matched now, or more of the roadmap is increasingly out of their hands. It’s a good time now to have those career conversations with your team. Change can be hard, but everything is learnable, and the future is perennially something we’ve barely started to invent yet.

Signing big customers is hard enough, but it’s only half the battle. There’s an adage in fintech startups – banks can take longer than you can stay solvent. Especially in the current market. I’ve built SaaS products that could take one customer just weeks to go live with, and another customer literally 18 months from signing. Expecting regular project meetings, integration support from you all along the way. 

When a big customer lags on going live, it’s not just lost revenue opportunity. It’s also likely a significant cost in distraction and resources supporting that customer in their integration project. Resources that could have been better spent in getting after the next customer and the one after that.

Here’s a few of the recommendations I’ve provided to my own advisory clients and portfolio companies when they (inevitably) encounter problems with customer integration delays.

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Pricing Strategy Sticks and Carrots

The goal here is both to create a better sense of urgency, and practical deadlines for your customers. But also to help you de-risk when you can start seeing, at least some, recurring revenue from each new deal. A key insight here is that most customers don’t think their project is going to be the one that goes off the rails and gets delayed, so some of these are easier to negotiate into contracts than you might think.

Start your recurring billing clock at time of signing. Make sure you have a component of your billing (a minimum volume, a monthly/annual license fee) that is recurring, not tied to volume, and that starts either immediately at signing, or within a fixed time from date of signing. 

Charge for integration support. Set a modest but reasonable integration fee. You may waive it, particularly for first-mover customers, but set the value of integration support in the minds of your customer. But then also make it a metered or recurring fee.

Cap integration support. Don’t let difficult customers drain your resources indefinitely. Whether you waive it initially or not, set an expectation that your team will provide a fixed amount (in hours, or for 30 days etc.) of support. If the customer needs more than that (intentionally or otherwise) then let them pay for incremental cycles of support. 

It’s not just about the one-time integration. Set expectations in your contracts clients will have to keep up with your future versions, including any reasonable level of retesting or reintegration over time. You can also build in provisions for ‘extended support’ where your service fees escalate in price if clients can’t keep up with some minimum reasonable upgrade cadence. 

Pricing incentives. Helpful  for unproven early stage products or network-value products. Provide pricing incentives for customers going live early, for participating in beta testing but also contingent on going live.

Air Cover

Arm your champions inside the customer’s organization. Think of your job as to make your main champions inside the customer look like geniuses to their peers and get them promoted. 

Do the customer’s job for them. Give them fill-in the blanks business and ROI models. Sample project plans. Sample test plans. I used to always build a mock bank app (see dogfooding) both as a test harness, UX testing tool and demo my own SaaS products, but ALSO as a reference implementation to share with customers

Support all the stakeholders. This one should have started early in the sales cycle. But it means having resources to help your customer team talk to legal, security, regulatory, ops, finance etc. There will be common questions/concerns everyone has, arm your champions with ready-to-go tools, collateral and answers.

Don’t forget the product as soon as it’s launched. Make sure they don’t forget to actually communicate, promote and actually use this fancy product they just worked so hard to buy and integrate from you (I’ve seen it happen). Help them with marketing, communication examples and templates, post-launch best practices to drive usage, metrics and reporting to provide feedback.  Put marketing clauses and potentially associated budget expectations in contract. As well as your ability to publicize the work/case study it.

Sometimes the contract can wait. When it’s not the technical integration but the commercials/legal, don’t let client legal be a blocking factor. You can start integration without final paper. It’s not the best practice but I’ve actually gotten major banks live in prod with an integrated product before all the final contractual and redlines were sorted out. Which means it can be done!

Design, Dogfood and iterate your Integration Experience

Clean API design. This means following and not breaking (without good reason) standard patterns like Restful API and well formed JSON. Be consistent. Try to make your APIs, data types and validation standards feel like they came from the same author and engineering philosophy. Conway’s law will creep up on you, but try not to make understanding your own organizational structure and history not your customer’s problem.  

Developer Friendly Sandboxes. Inline your sandbox with your developer documentation. Allow developers to test and learn api behaviour within the online docs themselves. Provide samples across multiple common languages/frameworks. Try to deploy all your products to shared sandboxes to better enable developers to test and experiment with creative solutions, potentially composed of multiple products/apis. 

Dogfood and iterate on your integration patterns. Build a mock customer app and integrate your service to it. Document your experience doing that. I’ve gone so far as to hire a third party shop to attempt to integrate an early version of your product, only using your first draft documentation. You will learn a LOT from that experience. 

Use generative AI. It’s actually pretty easy to train/embed an LLM on your product and integration documentation. Use that to help you more quickly spin up integration collateral, even highly customised for specific customers and usecases. Use that as an internal tool/refference, or just expose that LLM directly to customers as part of your toolset. GenAI can also be quite useful for generating synthetic test data. If done well, synthetic data helps to solve for privacy, security and compliance problems of how to test against realistic production data – without actually using real data.

Keep as much server-side as you can. Generally, the fewer the lines of code, data or logic the client has to own the better. Lower surface area means, less to build, less that can break, is easier to secure, version control and maintain later. For SDKs in particular, what’s the bare minimum of static code that needs to live client side or as native-ap code vs code can be loaded dynamically at runtime? You can update and improve your server side code continuously, with a big enterprise client, you might be lucky to get them to come back and upgrade your integration more than annually. 

Do the customer’s UX research for them. I’ve mocked up a big banks mobile app and UX tested and optimised the user experience with 3rd party research reports to back it up. Double bonus here, saves your customers time, and mitigates risk of big banks over-complicating or just mucking-up the end-user UX (which you just know they are wont to do)

Pave the elephant paths. For better or worse, your first integrations tend to be all hands on deck affairs. Engineering, product tend to be deeply involved. Bugs are found, key assumptions have to be corrected, gaps in documentation needs to be sorted out, new onboarding processes invented as you go. But your goal should be to make each new customer smoother than the last. Hopefully after just a few, you are handing over a repeatable playbook over to dedicated (and more scalable) support teams.  Make your integration machine continuously better, faster and lighter with each new customer. Track KPIs on integration speed and resources consumed to know if you are making progress. 

What’s your take?

Do you love these suggestions, hate them, have even better ideas I’ve failed to mention? Leave a comment and let me know.

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I’ve gotten versions of this question recently from bankers and investment-analyst clients.

It’s certainly messy. There are multiple countervailing trends in the macro economy that make this a complex picture. Rapid shift in rate environment has changed the mix in usecases for embedded finance, credit and BNPL models are more challenged, while high deposit yields create new opportunities in treasury products. When I talk to platform providers in embedded finance I hear that yes, deals are still happening but there’s definitely been a mix shift in customers and usecases over the last year.

Prominent bank failures in the US and Europe are not helping anyone either. These are further having a further adverse chilling effect on both bank liquidity and regulatory scrutiny. On the risk appetite side, there is a real danger of ‘baby with the bathwater’ as regulators and bank directors may be prone to look at anything creative as a risk rather than as potential innovation. Regulators are going to be leaning into their ‘protect the banking system’ mandate rather than the more open minded, lets stimulate competition and innovation.

US Banks are facing potential capital calls to replenish the FDIC reserve. Traditional deposit liquidity is chasing yields to money markets further pressuring liquidity. The yield curve and the lending market are upside down.

Embedded finance is also dependent on partnerships/customers with fast growing fintech startups, banking-as-a-service platform integrators and the like. But a significant share of fast growing fintechs over the last few years may have grown too fast, over raised, and could be now short of continuing capital. Expect to see a lot of consolidation and thinning out in the BaaS and neo-fintech space through the rest of the year.

And yet… Where there is turmoil there is opportunity. An 50-80% haircut in valuations create opportunities for acquisition, and an enormous about of smart, capable talent on the beach. It’s a buyers market for banks or well-enough capitalized bigger brands in fintech.

And yet… all these headwinds are intrinsically temporary. AI, new payment rails, working open banking, digital-issuance continue to fuel huge new opportunities and usecases across verticals. The overall trend towards embedded finance is a powerful long term cyclical trend. As software continues to transform every industry, payments, capital and finance _needs_ to be more closely embedded in all the SaaS platforms, marketplaces and apps that business (and consumers) now use every day. Banks that take advantage of temporary market downturns to invest in embedded finance stand to benefit enormously through the next economic cycles.