By Umme Sutarwala - June 07, 2022 4 mins read
Executives will continue to focus on building data-driven businesses and establishing an AI-driven culture that manages data as a company asset in 2022. On the other hand, most firms have gone a long way in developing effective AI adoption plans that enable them to add value to their operations on a daily basis.
The commercial sector is being taken over by Artificial Intelligence (AI). According to McKinsey’s ‘The State of AI in 2020‘ research, 50% of organizations already use AI in at least one function.
If the company’s IT management considers using AI, it’s a good idea to be aware of the potential roadblocks. As a result, IT leaders will be able to ease the way for AI adoption. Here are a few common stumbling blocks that enterprises encounter while using AI.
A substantial amount of high-quality information is required to construct and train successful AI. And the more accurate the data, the better the outcomes.
However, a lack of stakeholder buy-in can lead to organizations underinvesting in the data management systems needed to allow AI, leaving them incompetent to train an algorithm to address their business challenges correctly.
IT leaders may have a data set they may utilize if the organization employs a CRM platform to record consumer demographics, purchasing behavior, or on-site interactions. And in many cases, internet data libraries, including synthetic data, may fill in the gaps. However, if the organization isn’t interested in AI in principle, IT leaders will have no idea what data they will need, let alone how to organize it.
Scarcity of skills
Even when companies decide to go through with AI implementation, the majority of them lack the necessary capabilities to build AI systems. According to “The State of AI in the enterprise, 4th edition” by Deloitte, the skills gap is one of the top three hurdles in AI adoption for 31% of companies.
The issue is that most businesses cannot do it on their own due to a lack of necessary expertise. Some talents are in higher demand than others, but one thing is certain: AI implementation needs a diverse mix of skills.
When enterprises lack the necessary capabilities, the only approach to developing AI systems is to outsource the creation of AI-enabled systems that can be readily linked with the company’s existing tools.
Many businesses rely on legacy infrastructure, software, and devices to run their IT operations. Redesigning everything at once is a huge undertaking. This outdated infrastructure is frequently cited as a barrier to AI and Machine Learning (ML) adoption. Fortunately, cloud computing or, to put it another way, hybrid-cloud computing, has altered that.
Employing AI and ML does not necessitate a complete overhaul of a company’s IT infrastructure. In any event, it anticipates businesses using the cloud for data analytics and AI. Modern ‘Data Lake’ technology performs wonderfully in a hybrid context, combining cloud analytics with on-premises operating frameworks. Cloud analytics frameworks can even send data back into on-premises operational systems, allowing them to guide their operations better.
Finding good vendors to deal with is tough
Despite its rapid rise, AI usage remains modest in most firms. One of the causes for this is that many firms have worked with AI agencies that do not fully comprehend how to apply the technology to generate commercial value.
As a result, many companies have had unpleasant experiences while dabbling in AI development, leaving them hesitant to take the plunge. Whereas if they had started with respected and experienced AI suppliers, the results would have been noticeable.
Even better, if the vendors had first promised to solve a modest business problem, demonstrating the benefit of AI, the stakeholders would have been more willing to accept more ambitious plans later.
Umme Sutarwala is a Global News Correspondent with OnDot Media. She is a media graduate with 2+ years of experience in content creation and management. Previously, she has worked with MNCs in the E-commerce and Finance domain
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