2012 was an exciting time for tech. The cloud was becoming part of the enterprise technology landscape, with software-as-a-service (SaaS) one of its most compelling use cases at the time. Innovators and entrepreneurs saw SaaS as the foundation for reinventing businesses, with investors also attracted by its transformational potential across many sectors.
2024 is also an exciting time for tech. Generative artificial intelligence is becoming part of the enterprise technology landscape, with the travel industry host to many of its most compelling use cases at this time. Innovators and entrepreneurs see GenAI as the foundation for reinventing businesses, with investors also attracted by its transformational potential across many sectors.
Now we are seeing many similarities (and some differences) between SaaS then and GenAI today.
An uneven and unbalanced playing field
Not all SaaS businesses were created equally and the same is true of GenAI. Some of the early SaaS pioneers are established and mature today, others took the money before falling over, and some never got off the ground. AI is on the same trajectory with a similar outlook. Like GenAI startups today, securing an investment in 2012 required us to be disciplined with potential investors and have a clearly defined strategy with realistic and quantifiable goals.
AI startups are a dime a dozen, and one of the biggest challenges they face is cutting through the noise.
Focus on the problem being solved, not the tech you’re using
In 2012, investors needed convincing that hoteliers around the world needed a system which would allow them to sell rooms online, directly to the traveler or through the many emerging-at-the-time online travel agencies, managing their own pricing, availability, bookings and guests. This was the very specific business problem we were solving, it just so happened that SaaS was only the delivery mechanism.
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Today’s AI-driven start-ups should never lose sight of what it is they are solving and focus their pitches around the business and use case rather than the tech specs.
Devote time to finding the right type of investor
The funding landscape has evolved, and today’s AI startups have more funding options than we had.
Generalists tend to be more comfortable with B2C and use the same metrics to assess every business, which often overlooks the nuances of a specific sector.
Specific B2B vertical investors can assess the viability of an AI startup through their deep industry knowledge, awareness of competition, knowledge of addressable markets and potential to scale is also on their wish-list.
An AI travel startup might also pique the interest of a boutique investor that can see some crossover, say, with its fintech or AdTech interests.
High-net-worth individuals, super-angels, sovereign wealth funds, all are looking at AI, as well as the already-established network of incubators and accelerators.
Investors could see companies branding themselves as an “AI startup” as a red flag if. Funding is out there but startups must fight harder to prove their worth, which brings us back to our initial point of focusing on the use cases and business outcomes.
Adaptability as standard as pace of change speeds up
SaaS developed slowly relative to AI. Innovations took time to gain traction, not because they didn’t add value, but because tech adoption generally was low, so too was the take-up of innovations. Over time, the innovation cycle speeded up as adoption picked up.
Today, GenAI is developing at a pace almost unheard of in enterprise technology. This pace of change is a challenge which must be met head-on by startups. It is also something investors are increasingly aware of when looking at businesses.
In practice, the pace of change means that a start-up which has a plan based on its use of ChatGPT4 needs to make sure that the plan still works when ChatGPT5 comes along. In many instances, ChatGPT5 will learn from everything that has been implemented using ChatGPT4, so what was unique becomes commonplace, almost overnight.
Factor in the other generative AI tools, on the market and in the pipeline, and you see where the problem lies. AI startups need to think about how defensible their proposition is in light of this speed of change.
Focus on the problem being solved, not the tech being used. There are some GenAI start-ups giving the impression that they have invented the algorithms and own the IP, when all they have done is take an API. Most investors would see through this.
AI is the commodity, data is the differentiator
SaaS empowered many businesses to become data driven, pre-empting the need today for data upon which the GenAI can be trained.
GenAI startups will find it hard to deliver on a promise of differentiation if they do not own any data. Anonymized data sets from travel companies, banks, retailers are easily purchased and widely available. The challenge for startups is creating something new-to-market (and investable) that differs from what other startups accessing the exact same data sets are pitching.
Takeaway
Differentiation and problem-solving are key in an investment landscape where there is an over-supply of GenAI startups and solving a real-world business problem is the best way to get to the front of the queue.