It is no surprise that artificial intelligence is the toast of any tech-powered corporate enterprise. Organizations are embracing artificial intelligence (AI) for its revolutionary potential to leverage the value chain. Regardless of how business-friendly AI appears to be, many organizations have had a difficult time implementing it. According to the latest findings from Deloitte’s 2017 State of Cognitive survey, only 6% of businesses are achieving success using AI.
That said, is your firm considering adopting AI?
If so, are you hesitant to take the next step especially after hearing about the high failure rates of such projects? We will look at some reasons why most AI initiatives fail, as well as what your organization can do to avoid such roadblocks. But before that, can we take a look at some examples of failed AI projects?
Let’s dive in.
What are some examples of failed AI projects?
- IBM’s partnership with The University of Texas D. Anderson Cancer Center to build IBM Watson for Oncology to improve cancer care is a well-known example of an AI project failure. Internal IBM records obtained by StatNews demonstrate that Watson frequently provided incorrect cancer treatment suggestions, including prescribing bleeding medications to a patient with severe bleeding. Rather than genuine patient data, Watson is trained on a small amount of fictional cancer patient data. M.D. Anderson’s initiative cost $62 million without an achievement, according to a report by the University of Texas System Administration.
- Element AI, a Canadian software firm, had struggled to get its products to market due to high operating costs and low income. According to a 2019 interview with Element AI CEO Jean-Francois Gagné, some of the causes for these challenges include business partners who don’t use their data properly and a lack of current infrastructure to grow an AI model.
Top reasons why businesses fail in their AI initiatives
When your firm decides to experiment with AI, you will need to put in place a well-thought-out AI strategy. The absence of which may lead to failure. The following are a few of such strategies that are likely to result in AI failure:
Ambiguous business objectives
Artificial intelligence is a powerful tool, but it won’t help you succeed unless you have a well-defined business strategy. Rather than beginning with the solution of an uncertain business problem, companies should first identify business challenges and then see if an AI tool can help to resolve them.
However, AI may not be suitable or beneficial for all business processes. To deal with the scenario at hand, there may be more cost-effective instruments and ways available.
If companies asked these questions, they would be able to see clearly and quantify the costs, benefits, and commercial impact of AI projects.
Poor data governance
Every AI project’s most valuable resource is data. To assure the availability, quality, integrity, and security of the data they will utilize in their project, businesses must design a data governance strategy. Working with data that is obsolete, insufficient, or biased can result in garbage-in-garbage-out problems and a waste of company resources.
Companies should verify that they have sufficient data from a reputable source that represents their company processes, that has correct labels, and is acceptable for the AI tool employed before commencing on an AI project.
Lack of technical expertise
According to a Gartner 2019 survey, the shortage of experienced data science workers is the most significant barrier to firms implementing AI. Because there is a skills shortage, assembling a strong data science team can be costly and time-consuming. Companies should not expect to do much with AI unless they have a well-trained crew.
There are two ways for businesses to put together the ideal team. They have the option of building the full system in-house or outsourcing it to AI companies.
In-house system: Developing the entire system in-house allows you to maintain complete control over the final product while avoiding clashes with other teams over management and contractual issues. This is a scenario for both small and large businesses that wish to incorporate AI into their workflows while also expanding their AI team’s skills.
Outsource it to vendors: Companies can also outsource AI integration to third-party vendors with the appropriate skills and assistance. They have a thorough awareness of the client’s requirements and may create a system that complements the existing one.
Lack of an appropriate environment
AI models will never be completely self-sufficient. They work in conjunction with much larger systems, therefore how a user interacts with the AI model’s procedures must be carefully considered. As just a small percentage of an application’s code is dedicated to AI models, businesses require a well-designed production environment to get the most out of their AI systems. This mindset must be considered by your design team from the beginning, and it must be incorporated into the coding of the new tool’s implementation.
They must determine whether the AI solution can be incorporated directly into a client’s system, as well as how the hardware and software will interact to get the desired result.
The cost factor
AI is a costly investment for a company. While large corporations such as FAMGA (Facebook, Apple, Microsoft, Google, and Amazon) have dedicated budgets for AI implementation, small and mid-sized businesses struggle to integrate AI into their business operations. When compared to the multi-trillion-dollar AI opportunity analyzed by consulting firms, it is clear that AI talents are an expensive investment.
Conclusion
Artificial Intelligence has truly become a huge part of our lives, as well as a big element of many firms’ business workflows around the world. Regardless, getting started with AI can be challenging, due to the high level of uncertainty and the multiple variables that must be considered. Rather than being concerned with why most companies fail in their AI efforts – you should focus on how you can make your digital transformation an exception to the rule.
Always remember that getting it right from the start is crucial, which is why selecting the right AI strategy and partnering with a trustworthy IT firm cannot be overlooked.
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Scalework | AI | Data Science: Why are companies struggling to leverage AI?
