Artificial intelligence (AI) has become a popular topic in today’s world that influences the digital revolution. Businesses are keen on adopting AI as a part of their digital transformation journey. AI enables enterprises to be agile, resilient, innovative, and competitive. But it is not so easy to incorporate AI into business processes and get the desired outcome. Several organizations fail to adopt AI for different reasons and at varying levels of integration.
According to a global survey report by International Data Corporation (IDC), a considerable number of organizations reported failures in their AI projects. Though different organizations follow different methodologies and approaches to AI adoption, some common road blockers exist. Let’s learn about the most reasons for AI adoption failure observed by adopters across industries:
- Lack of a data strategy and structured data
Data is the most crucial component of any AI initiative. According to AWS, “a data strategy is a long-term plan that defines the technology, processes, people, and rules required to manage an organization’s information assets”. Unfortunately, a considerable percentage of organizations fail to implement the foundation of all your data practices. So, in most cases, they get incomplete and unorganized data. A Forbes survey of 700 global C-suite executives shows that a minimal percentage (only 12%) of companies have built, and are executing, a company-wide data strategy. As a result, most enterprises have unstructured data in almost every aspect, including text files, emails, social media messages, and employee complaints. It becomes extremely difficult for organizations to accumulate and consolidate such unstructured data from different systems of records and silos. It decreases the visibility and accessibility of data across departments; Organizations face challenges in addressing data management aspects like data quality, data security, etc.
If you plan to adopt AI, first, you need to think about data, develop a data strategy and implement that throughout the organizations and gather structured and usable data. AI solutions are built on a cohesive, well-structured data strategy. It will not only provide you with the power to observe and understand customers, their behavior, and their changing demands but also be able to predict trends in the market.
- No linkage to business outcomes
Another primary reason behind the underperformance of AI for certain businesses is that they consider artificial intelligence as “ a high-tech, superpower thing” without proper planning on how to use the AI results. Being isolated from actual business goals, AI implementation fails to produce desired outcomes. When one adopts AI without knowing how the organization will get and use the AI results, the whole purpose of AI adoption fails. In most cases of AI adoption failure, organizations seem to concentrate more on the production of AI, such as systems, processes, setting up tools, etc.
On the other hand, the most successful AI adopters start their AI adoption process by identifying their business pain points and challenges and trying to align AI solutions with them. Business owners also involve process owners and AI experts in planning to ensure they adopt AI solutions effectively. A well-defined AI strategy is needed to align technology and business together to kick-start the adoption process and get a successful outcome. Every concerned person in the organization must be aware of what business problems need to be solved, what is the purpose of incorporating AI into their workflows, what are their expectation on completion of the AI adoption and what success will look like. - Insufficiency of the right talents
With the continuous improvement of data-driven technologies like AI, the demand for skilled people like data scientists has increased enormously (more than 650%). But the supply of competent practitioners seems insufficient as per the market needs. It makes the situation more challenging for businesses that want to adopt AI. The insufficiency of skilled professionals and AI talents in the market causes enterprises to upskill their existing employees. But sometimes, this initiative of upskilling existing employees doesn’t reflect the appropriate outcome.
How can MSPs help businesses to adopt AI?
Partnering with a managed IT service provider(MSP) can benefit businesses to adopt AI solutions efficiently. An MSP can help their corporate customers’ AI adoption by improving speed, quality and efficiency, filling the talent gaps, and reducing business risks.
- Reduced risk of failure
According to a Gartner report, about 42% of survey respondents do not completely understand the benefits of AI — leading to unsuccessful AI incorporation. Partnering with an MSP can reduce this risk of brand failure by developing an effective AI implementation plan according to the business needs. Since experienced MSPs have better knowledge about the pitfalls and have tried-and-tested processes and tools for effective AI implementation with minimal risks. - Improved efficiency
Developing and implementing an AI model needs to collect and annotate data; build, test, and deploy the model; and monitor it frequently. Partnering with an MSP can help organizations to automate and create pipelines for these processes. System automation will improve efficiency without the need to expend resources. Partnering with an MSP allows companies to remain competitive with faster turnaround and scaling rates.
When a company tries to source data on its own for AI initiatives, the process can be complicated and time-consuming. The initiative can even end up creating biased datasets. These consequences can lead to limited or inefficient AI models that fail to offer valuable insights and desired outcomes.
An MSP offers modern quality controls and efficient data management tools, which can bring better accuracy and consistency to AI models.
- Meets talent gaps
While reskilling or hiring new staff in-house can help businesses in their AI initiatives, these are expensive and not always sufficient. On the other hand, seeking expertise from an MSP can be a more effective and affordable way to fill the talent gap.
Conclusion
Adopting AI models can be a challenging process for businesses. This complex process requires high costs, specialized skills, efficient pipelines, and tons of resources. So, partnering with managed service providers is wise to help businesses in their AI adoption and workflows. Partnering with a suitable MSP could result in a higher return on investment (ROI) as well. MSPs are valuable solutions for organizations to launch AI models and enhance operational efficiency.
