Why Do AI Startups Fail?

Manish Prasad Thapliyal
May 6 · 2 min read
Photo by Mario Gogh on Unsplash

There are many reasons why AI startup might fail. Primary reasons is that AI is still generally not well understood except by a relatively small number of people.

Few executives and managers truly understand what AI really is,

  • the current state of AI and its capabilities,
  • the value it represents
  • the difference between AI hype and reality,
  • the differences and unique benefits of AI as compared to alternate forms of analytics,
  • the differences between AI and machine learning, and much more.

AI can have tremendous benefits for:

  • Companies,
  • customers,
  • users,

but it’s not always obvious how, nor is it obvious what data, techniques, time, cost, and tradeoffs are required. It’s also not always obvious how to measure the success of AI solutions once built.

Companies also may not have the right data and advanced analytics leadership, organizational structure, or talent in place.

AI is an extremely technical subject area and requires translators between management and advanced analytics experts.

When considering investments in technology, executives are rightfully concerned with understanding final outcomes, costs, time to value, return on investment (ROI), risk mitigation and management (e.g., bias, lack of inclusion, lack of consumer trust, data privacy and security), and whether to build or buy.

Unlike traditional technology investments associated with undergoing a digital transformation,

e.g., building a mobile app or data warehouse, AI is better characterized as scientific innovation, a concept that implies an inherent amount of uncertainty in a way similar to that associated with research and development (R&D).

AI is a field based in statistics and probability and is rapidly advancing in both state-of-the-art and potential applications. It may be impossible to avoid some amount of appreciable uncertainty with AI.

Not understanding this or incorrectly setting expectations is another potential cause of failure.

Lastly, building successful AI solutions that benefit both people in addition to business requires a basic understanding of what people need and want, and also what the ingredients are for making great products and user experiences, as many of these ingredients will apply to make great AI solutions as well.

Fundamentally, people use products and services that are useful, better than the alternatives, result in a good experience. AI solutions that deliver all of these will succeed, while those that miss on just one ingredient may fail.

MLRecipies

Helping Beginner’s in their journey in Machine Learning

Manish Prasad Thapliyal

Written by

Data scientist, Machine Learning Engineer, Natural Language Processing and Chatbots/Voicebots

MLRecipies

Helping Beginner’s in their journey in Machine Learning

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