“By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it”.- Eliezer Yudkowsky
The global AI market size totaled USD 51.08 billion in 2020 and is projected to reach USD 641.30 billion by 2028. Artificial intelligence is everywhere. Some businesses are using it, some are planning to, and others have an opinion of it.
Consequently, we can sometimes be misguided by the misconceptions and fascinations of AI resulting from its image created by science fiction and pop culture.
With advancement at the current rate, there’s little doubt that AI and data analytics can reach great potential. However, the optimists among us may miss the many challenges lined up, while the pessimists don’t even touch the technology for fear of failure.
Before assessing why AI hype exceeds its implementation, let’s look at the hype.
The Reality of AI Hype
According to a 2021 study by KPMG, three-quarters of surveyed executives agree that AI is more hype and less reality. Half of the respondents also agreed that AI is moving too fast in their industry, and that they wished to move faster.
The hype of AI leads companies to get into the game with a false perception of what AI can help them achieve. Without a clear understanding of what the technology can and can’t accomplish today, there is a lot of risk in getting involved.
Another significant fact is that 92% of said respondents are implementing the technology and believe in its ability to deliver value in terms of high efficiency. While there is confidence in the technology, there is a hype that understates the challenges involved.
The overpromise of AI led to the coining of the term ‘AI Winter’ to describe the period of boom and bust in the 90s when the technology faded for a while. As it did, people left training programs stranded. So today, the huge market demand for AI doesn’t match the availability of proficient professionals.
Consequently, the advice and implementation a lot of companies receive is experimentative.
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Why Do AI Projects Fail?
87% of data science projects never go into the production stage. And 70% of companies report minimal or zero impact from AI.
Mixing Marketing with Implementation
Often AI vendors will engage in overpromises in their marketing, giving their clients a false sense of expectations. Companies often engage with technology vendors based on marketing alone and forget to look closely at previous implementations and results of the same sort.
Often, a vendor may also convince a business that their AI implementation does not need a particular component when it does- just that the vendor doesn’t provide it and the company doesn’t know.
Artificial intelligence can be a powerful strategy once you know what you expect from it. Having a well-defined business problem with business goals can help achieve success.
Besides, measuring outcomes from an AI implementation can be tricky as it involves building and training an AI model and experimenting with long-term trial-and-error before seeing results. A well-defined business goal can determine whether the direction of implementation is correct.
Business owners can embark on an AI project with high expectations. Self-driving cars, autonomous drones and facial recognition systems are wonderful use cases of AI. Still, a startup owner needs to look at the core business value they’re trying to achieve with AI instead of the new shiny objects.
High expectations around what AI can do for you often lead to disappointment when business owners conveniently underestimate the challenges and misinterpret the reality of AI.
Lack of Access to Talent
There are opportunities galore and not enough experts in the AI industry who can steer the ship and take AI projects to the finish line. Hiring for an AI team can mean huge investments, and working with a vendor needs a careful vetting process.
The lack of easy access to talent (and a lot of marketing) makes it hard for companies to distinguish genuine professionals from others. Getting into it with the wrong people can mean huge irreversible losses that may have been better off invested somewhere else.
What You Can Do to Succeed
The implementation part of AI comes later on the roadmap. First, you need a strategy that has been thought out internally after looking at various options and alternatives. This way, you are not dependent on a technology vendor to build your strategy. Instead, you can take a strategy to them and directly talk about implementation.
Instead of looking at AI as a business opportunity, companies can do better by leveraging it to improve business outcomes first. Begin by analyzing decision points and asking how improving this decision by 1% impacts the bottom line.
Starting small can set you up for success with AI by giving you insight into how it works. After one small victory, you can incrementally move on to more significant objectives.
Plan for Organizational Change
Artificial intelligence projects can change how business happens in your company. Are you and your team ready for it? Spend time getting executive buy-in and getting all employees on board. Explain why this project would mean a lot to your company.
A big part of implementing AI is dealing with the resistance from within. It’s natural for us to feel a little threatened by AI if we don’t believe that AI is here to augment humans and not replace them.
We created an in-depth report for startups exploring AI as a realistic possibility beyond the hype.
Download now: The Practical Guide to Implementing Artificial Intelligence for Startups