Reasons why Business are failing in AI?

We’ve seen the transformative effect artificial intelligence (AI) has on customer experience, cost savings, and profitability when working with our clients. Given the benefits and opportunities that AI provides, it’s no surprise that it’s becoming more widely adopted around the world. In 2021, 76 percent of organizations prioritize AI and machine learning (ML) over other IT efforts, according to Algorithmia’s third annual report, 2021 Enterprise Trends in Machine Learning.

However, we’ve seen how AI deployments can encounter roadblocks. Executives begin with high aspirations and expectations, but eventually, struggle to put their models into production or guarantee that the intelligence is being used to drive actions and impact by the end-users. According to a white paper published in 2019 by Pactera Technologies, over 85 percent of AI projects fail at some point.

Companies building a chatbot for Facebook’s Messenger, for instance, discovered a 70% failure rate in fulfilling user requests.

Factors for Failure of Business in AI Technology

1. Data Governance Issues

According to Suman Nambiar, head of the strategy, partner alliances, and services at Mindtree, “data is the overlooked x-factor that prevents organizations from moving from successful experimentation to making AI-led business a way of life.”

“While many companies acknowledge the value of data in training AI models, the vast majority profess to be uninformed of the data infrastructures and architectures needed to ‘industrialize’ AI at scale.”

Data of poor quality has an impact on AI’s accuracy and capability. Even a 1% data inaccuracy can have a significant influence on AI accuracy. AI practitioners must “clean” the data as a matter of course. Having efficient data cleaning procedures to increase data quality is a critical component of developing a scalable AI that is both resilient and scalable.

Most businesses overlook the importance of valuable data in enabling AI implementation success. Few organizations have weak information management and information cleansing procedures, resulting in a material that is suspicious, copied, or referred to by a different name elsewhere.

Therefore, we require a comprehensive, integrative, and all-encompassing data progression to develop transformational AI arrangements.

2. Ignoring Cultural Transform

As the world recovers from the COVID-19 epidemic, business executives created the groundwork for their companies to prosper in a new environment. The pandemic expedited three sorts of transformations in every industry: the adoption of digital technology, the development of new business models, and the deployment of new working practices.

While most businesses appreciate the importance of transformation, far fewer are aware of the critical link between business transformation and culture change.

Companies that focused on culture were five times more likely than those that didn’t achieve breakthrough results in their digital transformation programs, according to a recent study by Boston Consulting Group.

A transformational culture is built on its nature of being adaptive. It aids firms in overcoming cultural dispersion caused by incomplete acquisition integration or a legacy of global expansion.

Leaders must recognize that culture is fluid and that even if they do nothing to influence it, change will occur in their organizations.

3. Not In Sync With The Company’s Goals

Before deploying a business tool or a concept, organizations must let go of the notion that it must be fully baked or have all the bells and whistles.

Businesses may adopt convenient use cases in a rush to jump on the AI bandwagon, regardless of whether or not such use cases are linked with broader company goals. Failure to prioritize projects can lead to inefficient resource allocation, large opportunity costs, and a low return on investment in AI/ML.

AI has the capacity to completely revolutionize a company by increasing efficiency and profitability. Customer life cycle use cases like retention, contentment, and win-back can unleash millions of dollars in net present value for subscription firms. It is, nevertheless, a difficult task that necessitates cross-functional expertise, a clear road map, and alignment with business objectives in order to have a genuine impact.

4. Poor Data Quality

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 biassed can result in garbage-in-garbage-out problems, project failure, and a waste of company resources.

The performance of AI tools and algorithms implemented in response to COVID-19 is a fantastic example of why data quality is so important in AI initiatives.

The majority of the flaws stemmed from data quality issues such as unknown origins and mislabeling:

• As non-COVID cases, many models employed a dataset of healthy children’s chest scans. In the end, the AI trained to recognize children rather than COVID cases.
• The AI used text typefaces that hospitals used to categorize scans as a predictor of COVID risk in some circumstances.
• Models employed chest scans taken while lying down for some patients and standing up for others in other circumstances. The AI learns to anticipate patients’ danger based on their positions because a lying patient is more likely to be sick.

Companies should verify that they have sufficient and relevant data from trusted sources that represent their business activities, has correct labels, and is acceptable for the AI tool employed before commencing on an AI project.

5. Complicated Framework

Different handling designs are used in computer-based intelligence-enabled applications and systems. According to ABI Research’s 54 Technology Trends to Watch, this is likely to alter shortly.

Future AI and ML systems will be multimodal by nature and may require heterogeneous processing assets for their activities, according to ABI Research experts, who predict that major chipmakers will abandon proprietary programming stacks in favor of open Software Development Kits (SDKs) and API-based approaches to dealing with their instruments.

Whatever the case may be, it tends to remain a hindrance till further notice.

6. Lack Of Talent

According to a 2019 survey, the shortage of experienced data science workers is the most significant barrier to firms implementing AI. Due to the skills scarcity, assembling a strong data science team can be costly and time-consuming. Companies should not expect to achieve much with AI unless they have a team with sufficient training and business domain experience.

7. Fear of Missing Out

Now that AI is gaining traction, it’s normal to see multiple business groups, such as operations, technology, and product divisions, each experimenting with their own AI initiatives. With so many use cases to choose from, it’s more crucial than ever to learn to prioritize.

Startups and organizations must be willing to look for new and unusual business demands in order to make AI work for them. The first step in experimenting with AI technology is to examine data from a historical perspective. Are there any underlying or hidden factors for the data’s outcomes? How can the network system track the features or signals?

It might be difficult to tell whether an AI program has delivered definitive results at times. Should you extend the testing period for more iterations or include other datasets? If the cost-benefit analysis shows that maintaining an AI project is not financially viable, you must be willing to halt or cut it. It can become more difficult after the initial assessment is finished and before the method in issue is implemented.

The idea is to stick to a procedure to ensure that AI adds value.

8. Weak Collaboration Between Teams

Working on an AI project with a data science team in solitude is not a recipe for success. Data scientists, data engineers, IT professionals, designers, and line of business professionals must work together to create a successful AI project. Companies might benefit from establishing a collaborative technological environment if they:

• Ensure that the AI project’s output is well-integrated into the company’s broader technological architecture.
• Ensure that the AI development process is uniform.
• Share your knowledge and expertise, and create best practices.
• Deploy AI solutions in a large-scale manner.

To bridge the gap between diverse teams and operationalize AI solutions at scale, there are approaches such as DataOps and MLOps. Furthermore, creating a federated AI Center of Excellence (CoE) where data scientists from various business areas can collaborate will help to increase collaboration.

9. Fear of Losing Job

While AI has the potential to deliver significant improvements and benefits to the organization, it can also do what we do now for those who have no notion. AI technology can handle everything from doing physical tasks to making consistent decisions.

This, in its most advanced stages, could pose a threat to the members of the organization that implements it. All things considered, there may be persons who clog up the AI execution stream in order to keep their jobs.

Failures in AI projects

1. IBM’s partnership with The University of Texas M.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.

According to a report by the University of Texas System Administration, M.D. Anderson’s endeavor cost $62 million without achieving anything.

2. 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.

Conclusion

For businesses, the chasm between AI’s promise and the payoff has become a valley of disappointment. AI must and can improve its response to the difficulties that organizations encounter on a daily basis.

As a result, the current difficulty is cultural rather than technological. Companies will need an effective AI strategy connected with business objectives and novel business models to flourish in the digital age as it advances towards the core.