There were several questions posed to the panelists that we ran out of time to answer live, so the presenters all took some time afterwards to answer these questions:
What is the best way for us as startups to position ourselves using the potential benefits of AI without quantifiable data of our own?
James Milin: Some incredible research has recently been published on the benefits of AI. I’d lean into these:
- AI Index 2023, Stanford HAI
- GPTs are GPTs: An early look at the labor market impact potential of large language models, OpenAI
- Generative AI at work, National Bureau of Economic Research
Israel, in the case of hyper-personalization that you mentioned, what is the success rate for this strategy?
Israel Niezen: It depends on many factors, but hyper-personalization, for high-ticket B2B sales at least, is much better than the alternative. The conversion rate for mass non-personalized emails will approach zero, burn your brand, your relationships, and end your domain in spam folders….so better to personalize it and have a few leads convert.
I have two SDRs, they don’t do high volume outreach anymore because I asked them not to, I asked them to personalize. They each get me on about 2-3 calls per week…after a few months of practice and testing of messages, etc. We have a good close rate once I speak to people, so that volume is more than good enough to justify their total OTE (on target earnings) and all-in cost to the company. Each company, with different deal sizes, will have different math to do of course in terms of number of leads, conversion metrics, quotas, etc of course.
Deloitte shared on their State of AI 2022 report that 29% of companies are finding that AI isn’t providing the value they anticipated when adopting, and this is trending up as hype continues. What are some ways that we can get ahead of this and set realistic expectations?
James Milin: The timing of publication indeed matters (e.g. 2022 vs. now) as it provides context for the information, especially considering the rapid advancement of AI technologies like large language models (LLMs). As AI has evolved to include more general-purpose technologies like LLMs, it has become accessible to a broader range of applications, unlike previous reinforcement learning models which were often costly and highly specific.
How can someone without a network get enterprise deals?
Israel: Everyone has a network or can build one. Track down former classmates, friends, friends of friends, chase people at conferences, follow people on LinkedIn, genuinely care about them and their content, engage with them, produce great content of your own, etc. Also, hire people who have networks or make them advisors.
For those that are new in enterprise sales, what books or resources do you recommend?
James Milin: These are among the greatest books in the field:
- The Challenger Sale by Brent Adamson & Matthew Dixon
- Secrets of Question Based Selling by Thomas Freese
- How to Win Friends and Influence People by Dale Carnegie
How do you handle the buy vs. build discussion? I.e: “Why should we pay for your product if we can build our own?”
Israel Niezen: Math. Show them the numbers and why building their own makes no sense. Will building their own generate less revenue? Higher cost? Less efficiency? More distractions?
As a technical founder, how do I find agencies to help with B2B sales?
Israel Niezen: Hard truth = you don’t get anyone else to do the first sales for you, and certainly not an agency. Not if you are expecting to be the CEO. If you are the CEO, even as a technical co-founder, then part of your job is to sell the first X-amount of dollars yourself (old school people like me say first $1M). If you are not the CEO, then be the CTO and find a CEO who will do the first sales. The process of making the initial sales will teach you a LOT about your technology and your product, how to improve it, what matters, what doesn’t, what features to remove or simplify, how to articulate it, etc etc. You cannot and should not outsource this in the early stages.