This raises a question about the essence of an AI-based startup. With the surge in AI solutions, distinguishing between innovation and integration becomes increasingly blurred. AI startups are emerging rapidly, each claiming to revolutionize their field with this disruptive technology. But what truly sets an AI startup apart? Is it merely the adoption of existing AI technologies to enhance traditional processes or products, or the development of novel AI solutions that venture into uncharted territories? This dilemma, highlighted by Michaël Noblot, Deputy Director of Technopole de l'Aube en Champagne, in the 20^th French episode of the AI Experience podcast (not available in English at this time), prompts critical reflections on the nature of innovation in AI and how it integrates into the business models of startups and entrepreneurial projects.

Foundations of AI Startups

What makes a startup qualify as an AItech beyond the hype associated with the letters A and I, which sometimes seem to miraculously turn water into wine? Claiming to utilize AI garners favor from all stakeholders, as AI is perceived as modern and trendy. But the key question is: for what purposes is AI being used?

At the heart of every AI startup lies innovation. Some do not merely adopt AI but push its boundaries by developing new algorithms or discovering unique applications. They do more than solve problems—they redefine the problems themselves. This methodical approach ensures continuous improvement of their offerings and alignment with market needs and expectations.

A major appeal of AI is its ability to handle increasing workloads without compromising performance. AI startups excel in designing solutions and business models that leverage this feature, allowing them to scale at a rapid pace. Scalability is not just a technical capacity but a strategic vision that prepares a business for swift expansion and new opportunities.

Innovate or Integrate: Defining AI's Role in Your Startup

The incorporation of artificial intelligence in startups swings between integrating ready-to-use AI solutions and developing custom AI applications. Each approach has its pros and cons, significantly influencing the startup's trajectory and value proposition.

Integrating Off-the-Shelf AI Solutions

AI-as-a-Service (AIaaS) provides a range of algorithms designed for specific tasks that are immediately usable "out of the box." This approach democratizes AI access, especially for small businesses with limited budgets, by reducing development time and production costs.

Advantages include:

• Financial accessibility: Many AIaaS solutions are subscription-based, making AI expenses predictable and transparent.
• Quick deployment: User-friendly interfaces enable rapid setup without deep technical expertise, facilitating adoption within the organization.
• Trial before commitment: Trial versions allow businesses to assess the solution before making a full investment, maximizing cost savings.

However, disadvantages include:

• Limited customization: Designed for a broad user base, these solutions may not meet specific or unique needs.
• Compatibility issues: Integrating with existing internal systems can be challenging, requiring costly adjustments or compromises on functionalities.
• Data privacy concerns: Using third-party tools developed abroad means data might be stored or processed on servers outside the company's control.

Developing Custom AI Solutions

Creating tailor-made AI solutions offers perfect alignment with a company's specific needs, providing total freedom in terms of functionality and integration. However, this approach demands significant resources and expertise.

Advantages include:

• Adaptability and scalability: Custom solutions can evolve with the company's needs, facilitating process expansion and modification.
• Total control: Owning the source code allows for maximum flexibility in updates, enhancements, and adaptation to market trends.
• Competitive edge: Developing unique, intelligent algorithms for specific business models can significantly differentiate a company from its competitors.

Disadvantages include:

• High development costs and time: Creating a custom AI solution requires substantial initial investment in financial resources and development time.
• Technical expertise needed: Developing and maintaining a custom AI solution demands specialized skills in data science and software engineering, often beyond a startup's internal capabilities.

Outsourcing AI: A Third Path

A startup can be the driving force behind an idea, concept, or vision, while outsourcing the operational aspects to a team of specialists. Just as inbound marketing agencies proliferated with the advent of HubSpot in the 2010s, a similar trend could emerge with AI. However, the entry cost is not the same. Hiring a data scientist is not the same cost as hiring a web content writer. Entrusting an AI project to a third-party agency also raises questions about the startup's genuine commitment to diving into AI. While this approach might work for a proof of concept or a prototype, eventually, it becomes crucial to internalize the management, oversight, and maintenance of AI projects, especially when AI is a core part of the value proposition being sold.

To qualify as an AItech, a startup should offer a service or product where artificial intelligence plays a key role in its positioning and value chain. Merely integrating a ChatGPT-powered chatbot for customer support doesn't make a company an AItech. This situation reminds me of the early days of social media democratization when companies underwent digital transformations, proud to have a Facebook page.

Utilizing AI tools for daily operations doesn't make a company an AItech. However, offering a service that leverages AI to enhance customer experience could. Whether the use of existing AI components qualifies a company as an AItech remains a debate. My response would hinge on the value delivered by the service or product. Whether a SaaS solution powered by AI invents a new use, whether its AI is a 'home-cooked meal by a chef or a reheated frozen dish from the local supermarket, makes little difference for now, as long as it works. However, as an AI startup transitions to an AI scaleup, this question will undoubtedly resurface.