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.

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.