As of 2022, AI adoption continues to be a rising trend among businesses with a growing number of practical use cases across industries. However, the reality of AI Adoption seems to come with both hits and misses. A lot of businesses struggle to realize the technology’s full potential due to various AI adoption challenges.

Several frequently-seen bottlenecks in AI adoption lead to this situation. In this article, GEM guides you through each of them and suggests how they can be overcome. 

many ai adoption challenges can lead to wasted investments and slow down a business' innovation

A survey conducted in 2021 by O’Reilley pointed out the most commonly seen AI adoption challenges – with the biggest troubles being: a shortage of AI talent, poor data quality, and failure to identify business challenges. Another noteworthy barrier involves the company’s culture and willingness to change. 

Challenge #1: Lack of AI talent and expertise

AI expertise is a core element of any successful AI adoption strategy. AI-savvy personnel help businesses define the correct approach to adoption based on current business challenges. They will be in charge of monitoring the adoption project, adjusting the pace and execution promptly. 

Therefore, not having the right people to execute the strategy is a serious AI adoption challenge. The biggest skill gaps exist in areas such as ML modeling and data science and data engineering. The shortage of AI experts is quite an urgent matter, as it significantly hinders the pace and effectiveness of AI adoption plans. 

upskilling current employees to overcome the skill gap - one of the most common AI adoption challenges

 It is also interesting to note that the need for staff responsible for managing and maintaining the computing infrastructure is rather low – standing at 24%. O’Reilley sees this as a hint that companies are entrusting cloud platforms to host and resolve their infrastructure requirements.

There are several ways businesses can bridge this gap and confidently take on this AI adoption challenge:

  • Upskilling current employees via online and offline courses on AI specialties 
  • Leveraging tools for non-AI experts to automate machine learning development
  • Using open-source software 
  • Outsourcing the development project to tap into a broad pool of AI talents 

Challenge #2: Inefficient data quality

The quality and availability of data is a major technical AI adoption challenge.

A powerful and functional AI system is fueled by sufficient, reliable, and relevant data. However, in reality, a lot of companies still use legacy systems and have a silo approach to data processing – be it data collecting, labeling, or storing. 

a man working to ensure data quality

Organizations and businesses, hence, need a clear data strategy to align their databases with business goals and objectives. It also ensures effective scaling of the system and regulating data security. 

How can your business create a data strategy that leads to AI readiness? Here is a step-to-step summary:

  • Pinpointing business goals and objectives
  • Identifying the needed data sources
  • Assessing data quality 
  • Designing data architecture 
  • Creating a data analytics plan
  • Picking the right AI tools and algorithms

Challenge #3: Failure to identify appropriate use cases

49% of respondents of the O’Reilly survey noted “understanding business use cases” as an urgent AI adoption challenge, since this is one of the aspects in which expertise shortage is most acutely felt.

To overcome this challenge, two things are required:

  • Deep understanding of AI (their potential powers and limitations)
  • A thorough assessment of the company’s current performance and challenges
a person holding documents to identify use cases for ai adoption

These two factors, when combined, help senior executives and decision-makers pinpoint the field in which AI-driven values can be expected – its specific use cases. In addition, they can outline what objectives to set, what KPIs to use, and how to calculate the ROI of the project. A plan to adopt a new technology like AI should be characterized by business alignment, rather than assumptions.

Challenge #4: Reluctance to change

Another significant AI adoption challenge is resistance to change. It could result from a general fear of AI-caused job loss or a closed-minded view that fails to acknowledge how much value can be gained from AI investment.

the company culture must be open to large-scale changes resulting from AI adoption

Therefore, the need for staff buy-in is clear. The three tips to overcome employees’ resistance to change are:

  • Keeping them informed via training sessions and workshops to show AI adoption’s benefits and how it can be integrated into their workflow
  • Starting small – with specific tasks of one department, for example – and gradually scaling up afterward
  • Addressing their concerns about job security

Closing remark

Many businesses and organizations still struggle with navigating their AI adoption journey due to their unfamiliarity with the technology and the large-scale changes needed. 

To overcome AI adoption challenges, centralized and extensive efforts are required. An objective-led strategy with consideration of ethical, technical, and human resources factors will help businesses unleash the potential of AI and achieve long-term success. 

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