A.I. is changing how startups build a Business Development function—part 1
Photo of a shoreline by Eleni Afiontzi on Unsplash
Once upon a time—by that I mean until 2023—there was a commonly shared wisdom for SaaS startups, on when and how to build a Business Development function (by which I mean Partnerships/Alliances, not a euphemism for Sales).
The "good old days"
From inception to about Series C, it went something like this:
The founders (especially the CEO) focus on founder-led Sales & BD for the first [insert relevant number, e.g. $100k] of recurring revenue. Once the founders prove that this can be sold as a product (and not as a tailored engineering service), the investment in commercial scale is slightly de-risked, and savvy commercial talent will be open to joining.
While the founders stay very much engaged in pipegen, they hire a Sales Leader who's happy to start as an individual contributor while building the team (this was my role at Cloud 66), and young & energetic (mostly inbound) reps.
As inbound interest comes in from potential partners, the Sales Leader executes tactically on some of these opportunities, avoiding large/irreversible mistakes, and trying out potential partnership directions in the process.
The partner segments that shows more potential—whether VARs, SIs/MSPs, public clouds, or other ISVs—is where the founders focus their JD and their hiring. Most companies I've seen go for (once again) a leader who's happy to start as an individual contributor while building the team, processes, and programme (this was my first role at Snyk). The other partner segments remain reactive, but move to the BD Leader's desk.
As the company's strategy evolves and matures, it can give the BD team better direction on which KPIs matter, and the BD team can choose where to scale more aggressively, and what to say no to. Specialisation and focus are key here (like my role at Canonical and my follow-on roles at Snyk), and I've suggested a way to do that intelligently with partner segments in this past article, and how to accelerate GTM with the all-important cloud providers in this one.
The "brave new world"
In the face of AI upending so many aspects of startup reality—what is changing for Business Development roles? Here are some initial observations.
Revenue velocity
On the one hand, many AI startups are seeing vastly accelerated timelines to that $100k ARR (or much higher—follow Ed Sim's Substack for insights on this). This means that the handover of selling from Founders to Sales is looking less like a relay race and more like jets refuelling mid-air!
Likewise, with many fast-moving parts and tectonic plates shifting underneath, the importance of skilled BD talent with experience in strategic alliances rises with the stakes.
On the other hand, we're at the very beginning of this technology's evolution, and there is a lot of tailored engineering masquerading around as product. It's natural, but it just makes it more challenging to onboard the right commercial talent at the right time, and retain them—especially when BD work tends to have a longer horizon.
Software integrations
It used to be that you put some API docs on a microsite, glued them to some co-marketing assets, and voilà: you had a technical partner programme. Everyone wanted to become their industry's centre of gravity (aka the elusive "single pane of glass"), but the main point is that startups had the means to gate access and to control growth.
In the age of AI, the advent of new protocols like Model Context Protocol and Agent-to-Agent means that an AI startup can build integrations within a sales cycle, as Tom Tunguz points out in this post. I would argue that both the objective and the strategy of a technical partner programme need to be reinvented. For example, it may mean a looser-coupled programme, that looks and behaves more like an open source project (whether it is actually open source or not).
GTM partnerships
SaaS challenged many resellers who came from hardware or software-CDs to prove their value to customers by building services, and by cooperating more closely with Cloud Marketplaces on cloud-committed budgets. Similarly, the SaaS shift drove many system integrators to move closer towards a managed services model, with either a resale element or a white label capability. Both these partner segments moved towards each other, as well as towards the largest cloud providers.
I would argue that AI as a still-nascent enterprise technology takes us back to a place where the biggest piece of value for the customer lies in a partner that listens to them, builds an operating model with them, and then sits with them to run that model as the technology rapidly evolves. This may mean a further meshing of VARs and SIs/MSPs, or even some M&A activity to consolidate the two.
Concluding thoughts
I am really excited to see how AI continues to challenge our assumptions and ways of working in startup commercial roles, and the Business Development function in particular.
BUT WAIT! There is much more to discuss.
How should founders adapt?
Where do the cloud providers fit in?
And isn't this all hypothetical given increased levels of geopolitical and economic uncertainty?
I'll address all of these in part 2 of this article.
