Entrepreneurs are often coming up with innovative ideas that help solve huge problems in numerous fields, such as finance and healthcare. This is especially true when it comes to artificial intelligence (AI), which is growing quickly. A lot of business owners are interested in AI because it’s a new technology that might be useful in many ways. But there are a lot of things that could go wrong that would stop an idea from becoming a great business. Seven significant principles that successful AI firm founders have learned from their own experiences, industry reports, and academic research are listed in this article. These lessons are based on real-life events and will help you cope with the challenges that come up when you establish, expand, and run a business that uses AI. This is true whether you want to be an executive in charge of your company’s AI strategy, create a startup, or invest in new businesses.
1. The first thing you need to do is fix a problem in the real world. After that, make the technology better.
“We didn’t start with the technology; we started with the problem.” — Demis Hassabis, CEO and Co-Founder of DeepMind
Important Point: The best AI companies don’t merely utilize sophisticated algorithms to find ways to use their technology. Instead, they look for a huge, critical problem that has to be fixed and then utilize their AI solutions to achieve it.
For example, DeepMind. Since it launched in 2010, DeepMind has always been interested in challenging challenges like how to win games and how proteins fold. The team made sure that the things they worked on were essential to both people and science. This enabled them hire the best personnel and make the most money.
Not using technology just because it’s available. According to a McKinsey study, over 70% of AI projects fail because the goals and return on investment (ROI) aren’t clear. Companies that know how much they are worth from the outset do better than those that are continually trying to find new ways to make money.
Before you start building code, talk to your clients to find out what they require. The problem needs to be significant enough, serious enough, and long-lasting enough to make the cost of development worth it.
2. Get additional information about the topic so you can understand how to use it.
“You need to know how to use technology.” Domain knowledge helps you find even better answers. Ali Ghodsi is the CEO and co-founder of Databricks.
AI isn’t a cure-all; it only helps you learn more about something. Experts in product development help successful companies make sure that their products function with how people use them and the laws they have to follow.
A case study about Databricks. The AMPLab at UC Berkeley started Databricks as a research project. Ali Ghodsi, Ion Stoica, and Matei Zaharia were the ones who started it. They all understood a lot about distributed systems and gained ideas from teams that worked with enterprise data. By 2024, this domain-driven strategy had made Databricks worth $38 billion.
Issues with rules and morals. For example, Rich Ho (PathAI) and other healthcare AI companies work with doctors and nurses to make sure they respect HIPAA rules and gain clinical approval.
Advice: Make sure that at least three well-known people in your field are on your advisory board. Ask them to quarterly reviews to look at the pilots and the plans for new products.
3. Make adjustments right away using the Lean Startup Method.
“Speed wins in AI: ship early, learn fast.” ― Clement Delangue, Hugging Face’s CEO and co-founder
Important Point: In AI, which is continually changing, short development cycles are better than long ones. By leveraging rapid prototyping, A/B testing, and agile feedback loops, startups may quickly identify the optimal product-market fit.
One example is Hugging Face. Hugging Face used to build chatbots, but in 2019 they stopped and started making open-source transformer models based on what people said. More than 10,000 models are currently in their “model hub,” which gets more than 2 million views every month.
Minimum Viable Model (MVM). A minimum viable model (MVM) is when you give a small group of people a basic model to work with, monitor how they use it, and then improve it based on what you learn. A research by Stanford’s CSAIL demonstrated that iterative AI development can save up to 40% on costs when they go over budget.
Every two weeks, start a sprint cycle. This is a good bit of advise. To see how far you’ve come, look into critical metrics like model consistency, inference latency, and user engagement.
4. Make sure your team has people from diverse areas and backgrounds.
“Meeting people from different points of view gives you new ideas,”Sta. Sam Altman is the CEO and co-founder of OpenAI.
Important point: To produce AI solutions, data scientists, developers, product managers, UX designers, and business strategists all need to work together. Founders want their teams to have a wide range of talents, backgrounds, and experiences so they can think of innovative ways to solve challenges.
One example is OpenAI. OpenAI’s leaders, such Ilya Sutskever, Greg Brockman, and Jack Clark, blended researchers, engineers, and policy experts on purpose to find a balance between safety and creativity. The company began out as a non-profit in 2015. It is now a firm that can only make so much money.
Things go better when there is a lot of change. A BCG study indicated that organizations with diverse management teams make 19% more money because they come up with innovative ideas.
Putting people from diverse departments into teams that are in responsible of getting features done from start to end is a smart concept. Every six to nine months, switch around the people on your team so they can communicate what they know more easily.
5. Put ethical AI first and build trust.
“People need to trust AI.” — Dario Amodei, CEO and Co-Founder of Anthropic
Important Point: People are more worried about bias, lack of transparency, and misuse when AI systems are utilized in more private settings. Founders’ brands stand out and acquire the trust of customers and regulators for a long time when they adopt ethical standards from the outset.
Anthropic: An Example. People who used to work at OpenAI formed Anthropic in 2021. The startup is all about “constitutional AI,” which is a means to use the law to make sure models act right and keep people safe.
Following the regulations. The AI Act in Europe (2024) specifies that systems that have a major effect must be open and have risk evaluations. These rules assist fledgling firms get ahead of their competitors and avoid making mistakes that cost them a lot of money.
Every year, write an AI Ethics Report that looks for bias, seeks feedback from outside sources, and suggests ways to fix concerns. Allow people who are interested to give input in public.
6. Create ecosystems and form strategic partnerships
“Don’t be alone; use the ecosystem.” — Fei-Fei Li, who used to be in charge of AI at Google and is now a co-founder of AI4ALL
Key Insight: You can get your products to market faster, create trust, and find new methods to sell them by teaming up with schools, big companies, and open-source groups.
One example is Salesforce’s Einstein. With cooperation from research universities and cloud providers, Salesforce added AI to its CRM package. Features enabled by AI generated in more than $1 billion in sales in 2023.
Collaborating on open source projects. Cohere and MosaicML are two new firms that collaborate with colleges and institutions and give back to frameworks like TensorFlow and PyTorch. This makes it easier for them to hire more developers.
Here’s a tip: Set aside 15% of your R&D budget for initiatives and pilots that you perform with other companies. Plan how to split the profits so that people will desire to work together for a long time.
7. Make plans for company concepts that will stay and flourish.
“A great prototype is useless if it can’t get better.” — Alexandr Wang, CEO and co-founder of Scale AI
Important Point: AI companies that perform well plan for expansion from the outset by making their cloud infrastructure, data pipelines, and pricing models as efficient as possible so that they can grow with their clients.
Scale AI is one example. The first thing Scale AI performed was label data for self-driving cars. It is now a full-service data platform that automatically adds notes and charges you based on how often you use it. They were worth $7.3 billion in 2024, but that might change.
The unit’s monetary value. The Harvard Business Review says that organizations should always know how much more their Lifetime Value (LTV) is than their Customer Acquisition Cost (CAC). They should work to make their LTV at least three times bigger than their CAC in order to maintain growing.
You can address spikes by using microservices and containerized deployments like Kubernetes. Use tiered and usage-based pricing to satisfy the needs of your clients based on their size and income.
It’s not enough to have the newest algorithms to run a successful AI firm. The lessons above show AI business owners how to handle the problems that come up when they start a firm. Some of these attributes include recognizing a major problem to solve, teaching your employees about the industry, encouraging ethical behavior, forming strategic relationships, and coming up with company models that can grow. Founders may have a better chance of being able to launch new items and stay successful in a competitive industry if they follow these suggestions. Some of the greatest persons in the field who have taught these ideas are Demis Hassabis (DeepMind), Ali Ghodsi (Databricks), and Sam Altman (OpenAI).
Frequently asked questions (FAQs)
Q1: How much money do most AI companies need to get going?
Getting started costs a number of various amounts. For instance, a bootstrapped business that sells specialized B2B solutions could only need $500,000, whereas a deep-tech startup that needs a lot of research and processing capacity might need more than $5 million. Angel investors who are interested in AI or venture capitalists who want to make a difference generally give seed rounds.
Q2: What is the best way to collect and maintain track of training data?
Use synthetic data augmentation, third-party annotation services, and internal labeling with tight quality checks to make sure the data is of high quality. To make sure that everything can be transferred and fits the standards, use data versioning technologies like DVC and preserve strong lineage records.
Question 3: How do AI businesses find the best people when there are so many of them?
Give your staff stock options, flexible hours, and mental challenges to help them stay focused on the goal. Help colleges set up internships and workshops, and work with other people on open-source projects to make your brand better.
Q4: What legal problems should people who want to create AI enterprises know about?
Talk about who owns the intellectual property, especially when people contribute to open source projects. Also, discuss about the standards for keeping data safe (GDPR and CCPA) and how AI-based decisions could get you sued. As soon as you can, get a lawyer who knows about AI ethics and the terms of service.
Q5: What can AI firms do to prove to clients that they are worth the money and keep track of how much money they make?
Be clear about what you want to do, like saving money, making more money, or spending less money. Use controlled pilots to see how well AI works compared to how well it generally works. Then, let clients use these case studies to see how much money they made from their investment (ROI).
References
1. Nature: “AlphaFold’s Impact on Biology” – https://www.nature.com/articles/d41586-022-03664-1
2. McKinsey & Company: “Global Survey: The State of AI in 2024” – https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2024
3. Crunchbase: Databricks Profile – https://www.crunchbase.com/organization/databricks
4. PathAI: Research Publications – https://www.pathai.com/research-publications
5. Stanford CSAIL: “Iterative Development in AI” – https://ai.stanford.edu/research/iterative-development
6. Hugging Face: Model Hub Statistics – https://huggingface.co/models
7. BCG: “How Diverse Leadership Teams Boost Innovation” – https://www.bcg.com/publications/2020/how-diverse-leadership-teams-boost-innovation
8. OpenAI: About Us – https://openai.com/about
9. Anthropic: Safety Approach – https://www.anthropic.com/safety
