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Yesterday I attended the Customer Success Collective conference in SF. Between moderating a panel with Daniel Silverstein Elizabeth Blass and Radha Penekelapati, listening to some fantastic presentations (my top two were those of Valerie Jones-Harvey and Mike Merit), networking with a bunch of very thoughtful people and browsing the vendor hall, it was time well spent.

There are multiple values from attending a conference like this, but my key objective in this one was to assess how much reality is there in the AI space. There is a lot of hype surrounding AI recently and I was eager to engage with leaders in the Customer Success field to "SaaS out" what’s real. 

Here are my key take aways:

• Hype > Reality: there is a fair amount of reality (see below), but so far the hype is by far larger. 

• Adoption of AI among small companies > that in large companies: the main reason is that in large companies there are many more rules, processes, checks, and governance that slow innovation (although at the same time, they also slow mistakes, failures, waste and disasters). People in small companies can get-by by just doing what they feel is right and short of public disasters, even if things don’t work out, the penalty is minimal. Large companies can’t afford that, so they take their time. 

• Tactical use cases > formalized processes: most use cases I heard from people involve ad-hoc tasks and one-off innovation. Very little have I heard of incorporation of AI into processes and procedures at scale (example: “all CSM from now on will work this way”).

• Cost Savings > Revenue Generation: most people describe how they use AI to reduce the cost of executing certain tasks, mainly via reduction of time to get tasks done. Some of those tasks are aimed at increasing revenue (including increasing retention and reducing churn), but even those were really focused on the efficiency gains of tasks. 

• A check-mark > actual value: it’s hard to find any tech vendor that does not highlight their use of AI. But, a quick look under the hood reveals that for most of those, especially among large companies, the AI in discussion is either very basic (example: added chatBOT) or very minimalistic in its value. I am sure they will all add more value over time, but for now, it is worth stress-testing some of those vendors on the real value add beyond generic "we use AI" statements. 

• Early Results > At Scale Impact: there are a lot of very very cool new vendors that use AI in ways that fundamentally change the game in their domains. That is: if it really works. The use cases are very interesting and enticing. In my experience, most of those vendors are still very very early in their journey to be viable providers of value to large companies and at scale. What they are able to show are early results, but very few can truly show them at scale. I expect tremendous volatility (multitude of new vendors alongside a lot of carnage) in the next few years before the dust settles on the winners. 

• Capabilities > knowledge: the AI space has reached a tipping point of availability about a year ago and is now exploding with new tools, techniques and capabilities. Those present tremendous opportunities (for productivity, effectiveness of work, efficiency and more) alongside great risks (from errors, mistakes, unintended consequences, fraud and so on). That level of change requires us all to invest in training our people on what to do and how to avoid mistakes. From basic understanding of what is private versus public in AI engines, to how to craft effective prompts in ChatGPT to how to validate results over time: our people can use a lot of training.

• Fear > Opportunity among CSM while Opportunity > Fear among CCO. I sense more fear from AI among junior professionals (including many CSM), alongside higher excitement at the opportunity from AI among senior people. I guess some of it is an outcome of one’s mindset: glass half-full versus glass half-empty. While some of the reason has to be in the level of experience more senior people have towards technology trends and its implications. 

This last point is non-trivial, but very important: most people agree that AI has the potential to both reduce the cost of the work done by CSM and increase its output. Glass half-empty people interpret the above as risk to their jobs: if less CSMs are needed, companies will let go many of them. But, that mindset only looks at CSMs as providing low-level manual work as a cost center, in which case, management should look at ways to minimize this expenditure. If on the other hand, the CSM job is producing high value revenue generating work, and if AI can improve that work, then it behoves on management to increase its investment in that high ROI role. 

Bottom line: What’s the take away? 

A) Learn (as an individual) and train (your team if you’re a leader) how to use AI asap

B) Stay very close to revenue generation

C) Make sure to enjoy the journey, not just the destination...

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