2012 was a great year for technology. 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. SaaS was a foundation for entrepreneurs and innovators to reinvent businesses. Investors also saw its potential in many different sectors.
The year 2024 will be a very exciting one for technology. Generative artificial is now a part of enterprise technology, and the travel industry hosts many of its most compelling applications. GenAI is a foundation for entrepreneurs and innovators to reinvent their businesses. Investors, too, are attracted by the potential of GenAI across many industries.
GenAI today and SaaS back then have many similarities, as well as some differences.
Uneven and unbalanced playing fields
GenAI is not the only SaaS business that has been created. Some of the SaaS pioneers have matured and are well-established today. Others took the money but failed, and some never got started. AI is following the same path with a very similar outlook. GenAI startups are on the same trajectory as GenAI today.
To secure an investment, we had to be disciplined and have a clearly-defined strategy with realistic and quantitative goals.
AI startups are everywhere, but one of their biggest challenges is to cut through the noise.
Focus on the solution, not your tech
In 2012, investors were convinced that hoteliers worldwide needed a solution which would allow for them to sell online rooms, either directly to travelers or through the numerous online travel agencies that were emerging at the time, while managing their own pricing and availability, bookings, and guests. We were solving a very specific business issue, SaaS just happened to be the delivery method.
AI-driven startups should not lose sight of the problem they are solving. They should focus their pitches on the business and the use case, rather than the technical specs.
Spend time finding the right kind of investor
AI startups have more options for funding today than they did in the past. Generalists are more comfortable with B2C, and they use the same metrics for every business. This leads them to overlook the nuances in a particular sector.
Investors in specific B2B verticals can assess the viability and growth potential of an AI startup by assessing their industry knowledge, their awareness of competitors, their knowledge of target markets, and their scale-up potential.
A boutique investor who has a fintech or AdTech interest might also be interested in an AI travel startup. AI is a hot topic for high-net-worth individuals and super-angels. Sovereign wealth funds are also interested in AI.
Investors may see a company’s branding as an “AI Startup” as a warning sign if. There is funding available, but startups need to fight harder to prove themselves. This brings us back to the point we made earlier about focusing on use cases and business outcomes.
Adaptability is becoming a standard as the pace of change accelerates
SaaS developed more slowly than AI. Innovations took a while to gain traction. Not because they were not valuable, but because technology adoption was generally low. As adoption increased, so did the innovation cycle.
GenAI is advancing at a speed that is unheard of for enterprise technology. Startups have to be prepared for this rapid pace of change. Investors are also becoming more aware of this when evaluating businesses.
In practice, because of the rapid pace of change, a startup that has a plan that is based on ChatGPT4 must ensure that it still works after ChatGPT5. ChatGPT5 can learn from all the things that have been implemented with ChatGPT4, and what was unique, becomes commonplace.
You can see the problem when you consider the other generative AI products on the market or in the pipeline. AI startups must consider the viability of their proposition in light of the rapid pace of change.
Focus on the solution, not the technology. Some GenAI start ups give the impression that they own the IP and have invented the algorithms, but they have only taken an API. Most investors will see through it.
Data is the differentiator in the AI industry
SaaS enabled many businesses to become data-driven, preempting the current need for data on which GenAI can train.
GenAI startups may find it difficult to deliver on their promise of differentiation without data. Anonymized data from travel companies and banks are widely available. Startups must create something new (and investable) to the market that is different than what other startups are pitching using the exact same data.
Takeaway
In an environment where GenAI startups are oversupplied, differentiation and solving real-world problems is the key to getting to the front of a queue.
