How Could Gen AI Impact GTM Strategy?

As every company looks to integrate Gen AI into its product set or workflow, there are meaningful implications for the way GTM strategy, pricing and sales motions are run.

Issue #18

See here for Part 1 of my enterprise sales lessons series illustrating the lessons learnt from scaling a tech business to IPO, here for Part 2 on hiring your first strategic sales leader, here for Part 3 on the emotional aspects of strategic sales and here for Part 4 on how to conduct value based selling.

Every company is looking to integrate Generative AI into it's product set or workflow. For companies that typically follow a strong B2B sales motion such as software companies or service based companies, there is likely to be a big shift when it comes to GTM strategy, the sales motion and approach to pricing. As we were scaling MoneyHero Group, this was a topic I kept thinking about and coming back to.

The game-changing aspect of Generative AI is the potential to put the user and the diversity of user needs at the heart of the platform in a way that has never been possible before.

Let’s take enterprise software - what does the actual user experience currently look like? Software is usually a replacement for a complex series of actions and/or analysis that would need to be cobbled together by human effort and other (not specialized) tools. Typically, workflows need to be created, either by system admins (in order to preserve overall control, access rights and so forth) or directly by the end user by selecting various different options, typically from drop down menus, who then have to manually create groups of users themselves. Creating reports and analysis is typically cumbersome as well; you need to manually create the specifications of the report

Generative AI turns this paradigm on its head. As a user, you can now easily explain what you need and the AI agent will create it for you. Creating customized workflows and having AI agents executing aspects of them will become a breeze in the near future. Data analysis, reports etc can be created automatically and to fit the context. This is currently being tested and refined across multiple industries. An example in the fintech space is Tatch.ai which is trying to replace legacy workflows and human heavy repetitive work with AI agents.

Therefore, at present, there are 2 different paths B2B companies will take with Gen AI:

One is to build products and services from the ground up using Gen AI as a bedrock and the second is to layer on Gen AI driven features on top of their existing solutions.

If you are interested in collaborating directly with me to you prioritize to grow Revenues please…

So what are the implications?

For companies that fall into the first bucket (building products and services from the ground up using Gen AI as a bedrock):

GTM:

• Having a strong understanding of your buyers’ ICPs (ideal customer profiles) becomes even more important, especially in the early stages of monetization. Why? Because the very nature of a Gen AI based product means that the effective usage of the product is reliant on the ability of the users to articulate what they want and how they want the software to behave. If this is not well understood (and similarly, if the foundational models are not trained properly), then it will not lead to effective conversion to paying customers. I am starting to see this in practice.

• This also means that segmentation based on ICPs needs to be done even more carefully.

Pricing:

• Gen AI based products become more powerful the more that they are used, especially if they include autonomous agents that can be used to stitch multiple expected behaviours together. In that sense, there is likely to be a strong propensity for customers to consume more of the product over time and pricing should be geared towards that.

• Feature based pricing does not really make much sense in such a model considering ‘features’ in the traditional software sense won’t exist (and enterprise ready features such as access rights, uptime etc. will be seen as standard anyway). There’s probably going to be a strong emphasis on professional services, especially around integrating the AI product into existing systems, particularly around capturing data from disparate sources and training people to use the system properly (prompt engineering, creating agents).

• A key swing factor in the near term is the marginal cost of delivery. The incremental cost of training models and finding talent that can effectively build is high. That will reduce over time but that will take some time. Given the need to achieve high gross margins, this needs to be factored in carefully.

• It generally makes sense for these kinds of products and services to have some kind of base pricing to give access to the model or specific classes of models and then consumption based pricing through a credits system

Sales motion:

• Account executive (AE) incentive plans will naturally have to gravitate towards a consumption based approach. I also think that there will need to be a certain qualititative aspect to a variable incentive plan that compensates AEs at smaller companies for efforts made that do not necessarily generate revenues in that particular time period (e.g. quarter). This is because there typically aren’t defined post sales roles or in some cases, even strategic customer success teams. Therefore, the account executive needs to do the heavy lifting of educating a customer on how best to use the product and optimize for that.

• An interesting twist is that Gen AI native companies are likely to use Gen AI agents for outbound lead generation (considering their familiarity with the tech) - essentially replacing a lot of the work that sales development representatives do at the top of the funnel. This, combined with the increased importance of AE to drive consumption, could result in a need for better quality AEs and a redistribution of the compensation pool towards AEs.

For companies that build Gen AI features on top of their existing product set:

GTM:

• The main question that companies will wrestle with is whether to charge for the Gen AI features separately or use them as a user engagement tool that can then be monetized through the core product and service offering. Treating the feature separately tries to ensure that the value of the feature is adequately captured but for that, you need to be really sure about that there’s enough standalone value in the proposition. Generally, I don’t see that as a viable approach at the moment as it will take quite a lot of time and resource for non Gen AI native companies to create effective feature value propositions. It is better to use Gen AI featuers to improve the value of the core product and improve montetization through that.

Pricing:

• If the value of Gen AI is to improve the value of the core product, then it makes sense to improve the pricing strategy that captures more engagement from users in the core product as a result of interlinkages with Gen AI features. This needs to be calibrated so that the incremental COGS (cost of good sold) impact of introducing the Gen AI features is covered in a reasonable amount of time. Also, as an aside, financial capacity planning for these new features needs to be done properly considering the (currently) elevated Opex and Capex associated with them too (cost of hiring skilled AI staff).

Sales Motion:

• There is arguably less of an impact on the sales motion as the core aspects of enterprise sales such as qualifying prospect needs, P&L and organizational structure remain the same. The greatest impact will come on the B2B marketing material to adequately position the benefits of Gen AI features and how they intersect with the core feature stack. In essence, generating qualified leads that have the highest propensity to engage most with the entire feature set and derive most value from it will be paramount.

It will be really interesting to see how this evolves going forward; one thing is for sure, it will continue to be an interesting ride!