The Biggest Mistakes First-Time AI Founders Make
Artificial Intelligence is driving one of the largest entrepreneurial opportunities of our time. From smart automation to AI-driven products, entrepreneurs are developing technologies that have the potential to change industries and the way that business is conducted.
But launching an AI venture isn't the same as launching a normal software company. There are many first-time AI entrepreneurs who commit fatal errors which delay their growth, drain valuable resources, and keep promising innovations out of the market.
Some of the mistakes that AI founders need to avoid include:
Developing the Technology without Addressing the Problem
One of the common mistakes is approaching from the technology perspective rather than the problems perspective.
An entrepreneur discovers an interesting AI model or feature and immediately wonders, "How can we leverage this technology?" But the truth about successful AI ventures is that most companies approach this differently:
"What problem needs to be solved?"
AI is just a means to an end, not the end-product itself. Customers do not buy the technology but better results, lower cost, efficiency, etc.
The best AI ventures solve a problem by delivering measurable value using AI technologies.
Not Realizing the Challenges of Data Collection
AI works on data; however, many founders do not appreciate how challenging the process of collecting, cleaning, managing, and applying quality data can be.
Even an outstanding AI model cannot provide anything valuable without good data.
Thus, founders should take into consideration the following questions:
Where can the data come from?
Is the data reliable and accurate?
Can it be continuously updated?
Are there any privacy issues?
In many cases, for AI startups, the true competitive advantage is not just the model – it is the unique data infrastructure underlying it.
The popular myth about AI states that the company with the best model wins.
There are many successful AI companies which are not victorious as they have built the most sophisticated technology. They win as they understand their clients better, fit their workflows, and provide constant business value.
The model is just a single part of the equation.
Disregarding the Importance of Industry Knowledge
An AI product can fail if the founder creates something without knowing the industry it addresses.
There is industrial AI, healthcare AI, financial AI, enterprise AI, etc., which all require deep industry knowledge.
Founders who develop an AI product for manufacturing must know the intricacies of factory production. Founders who develop AI for supply chains must know the challenges of logistics.
Knowledge makes technology powerful when used together.
Creating a Product Without Listening to the Customer
Most founders spend months on developing an AI product before talking to the customers.
It can lead to a risky situation when creating an amazing solution that no one needs.
Customers need to be talked to from day one.
Founders need to constantly ask:
What problem does the customer have?
How do they solve it now?
Why would they switch?
How valuable would the solution be?
The best AI products are developed in collaboration with the customers.
Disregarding Deployment and Adoption
The prototype that works in a demonstration is not the same as the product that works in real life.
Enterprise customers care about:
Reliability
Security
Integrations
Scalability
Ease of use
AI founders must think beyond building a model. They need to build systems that people can actually use.
Attempting to Substitute Humans Rather than Enhance Them
Most early concepts for AI aim at replacing humans altogether.
On the contrary, some of the most successful implementations of artificial intelligence assist human decision-making and improve efficiency.
Collaboration is often what defines the future of AI development.
While the machine deals with repetitive tasks, processes vast amounts of data and gives recommendations, humans are responsible for creativity, judgment, and experience.
Infrastructures related to AI can become rather costly quite soon.
Attracting large teams of people, creating complicated solutions and scaling before product-market fit is found might lead to excessive challenges.
First-time entrepreneurs need to concentrate first on proving:
The customer problem is genuine
The customers are ready to pay
The impact of the solution is evident
Scaling up must happen after the validation, not before.
Lack of Business Strategy
Even a good AI product needs a good business strategy behind it.
Entrepreneurs need to be able to give an answer to the following questions:
Who is the customer?
How does the company make money?
What makes the product hard to replicate?
Why would customers use that particular solution?
Failure to Build for the Long Term
The landscape of artificial intelligence moves fast. There are model upgrades, competition, and novel technologies.
The most successful AI companies have sustainable competitive advantages:
Unique datasets
Relationships with clients
Industry expertise
Strong execution
Insight into market requirements
Founders must resist being obsessed with all of the novel developments in AI and should work on creating something valuable that can be sustained.
It is an amazing chance to start a business in artificial intelligence, but there is much more to success than technical skills.
The victors will not necessarily be those who use the latest AI models. The companies that know how to address existing issues with novel solutions will win.
The greatest takeaway for first-time AI founders is easy enough:
Think of customers first when developing your product.
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