Launching and Scaling Your B2B AI Business in 2024

The AI market is forecast to grow 13x in the next? 7 years.

The AI market size is expected to grow 120% annually.

There has never been a better time to launch an AI Startup.

AI startups are not the same as normal B2B technology businesses, here are seven reasons why launching and scaling AI businesses is different

1. AI Led Founders

The deeper the tech the further the Founders are away from being business or sales-led. Using a racing car analogy, technical AI founders like to focus on building a Formula One engine. The chassis, body, paintwork, upholstery and driving experience are often not front of mind. As the AI markets become flooded with new tools, buyers will buy for the driving experience first, then the paintwork, body and upholstery. The last thing they look at is the engine. A Formula One engine in a Trabant body is a hard sell. Creating focus where it is needed for commercial scale can be challenging in AI-led businesses when all the engineering team wants to do is work on the engine.

2. AI Product Roadmap - Built to Sell

When selling direct to business normal B2B rules apply but turning AI outputs into an easy-to-buy business case is often not straightforward. Business buyers are usually uneducated AI buyers so the process has to look easy and be jargon free. If not team will be referred to the IT or Analyst gatekeepers who may want to build it themselves because the platform vendors have told them ‘AI is easy’. Someone has to believe the marketing.?

AI tools are often B2D (Business to Developer) or B2A (Business to Analyst) sales but these customers are perhaps the most sales-resistant audience there is. They will buy if they can look under the hood and play with the product. In this case a sales-ready product should support trials and have a great UX/UI/UJ to withstand this scrutiny. How you sell and who you sell to will have a significant impact on your product roadmap and what a sales-ready product looks like.

A common problem in the AI buying cycle is that the business value is only apparent when production data is put through the engine. If this is the case build this into the sales process.?

3. Permission to Sell

Is your AI product bought or sold? If it is bought you will have to invest in PLG (product-led growth), where businesses can self-onboard and get up and running quickly. Seeking larger deals means the product will be sold leading to more complex sales cycles. Who will be in the sales meetings with the client? Business people value business credentials, academics value academic credentials, developers respect strong development experience and analysts want to talk to analysts. AI products can open doors to very different customer buying journeys. Permission to Sell comes from ‘Why you’ - what experience qualifies you to solve the problem? The language and experience of the sales team have to resonate strongly with the business buyer.

4. Sales Process - Putting the Pig on the Lipstick

Every business wants AI yet few have AI buying processes in place. Your AI product is innovative and market-changing but this, by definition, means customers have no way to engage. Corporates want innovation as long as it’s not new.

To create uncomplicated short deal cycles the product should look like a product the business has bought before and is comfortable buying. Services are the lowest friction way to sell to any large business so consider a SwaS (software with a service) sale for larger accounts to get started.?

Shorter AI deal cycles mean selling to the sold. If customers don’t have a good level of understanding of what the problem is and what success looks like before engaging you could end up running a free training business as you educate the market. It’s a hype market so AI sales processes have to weed out ‘serious people’ from tyre kickers early on.

5. Platform Fatigue

No business is actively seeking ‘yet another’ platform to log into. On the product build journey of Simplify, Standardise, Modularise, Integrate - Integrate is the step that triggers a sale. In cases where AI buying friction is high consider moving Integration up the product roadmap so the customer thinks 'it’s ‘built for me’. Simple integrations like Google Workplace or Microsoft Teams will help. Deeper integration is better and will increase customer lifetime value but make sure it accelerates not slows the buying journey.

6. Credit Control

Cashflow is everything for startups and AI businesses have a couple of extra challenges to face. Many startups will have access to 100K USD of hosting credits. Giving the development team full control of these credits is like putting a fillet steak in your dog’s bowl and telling him not to eat it. The psychology is that the credits are free so they are used to run multiple experiments. Manage and control your CPU and GPU consumption separately and treat the credits like cash. They will run out so you need a strong credit controller to monitor how and when they are deployed and make sure that when they do end your whole business is not destabilised.?Consider tools like Skypilot and Lambalabs to make credits go further.

AI teams are more expensive, hosting costs are often high before launch, launch cycles are slower and product market fit is more elusive. If normal startups underestimate the cost to launch by 3x, AI startups will underestimate by 6x or more.

7. Time to Market

Thousand of AI products are launching monthly, the market is highly competitive, and securing a footprint beyond your home market is essential. Once in the market, AI businesses should move through the market experimentation, engagement and expansion cycles faster. To do this they should automate their market engagement processes early on, these tools are evolving weekly and creating a centralised virtual digital SDR function is now a real option. They should be circumspect about how much non-core back office functionality to build, and get to market with a merchant-of-record partner that offers instant global market access.

The opportunity AI offers is enormous but it’s a capitalist market so the risks are in line with the rewards. If launching and scaling an AI business offers returns 3x above the normal SaaS market the discipline and structures required for success are 5x higher.