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3 Key Business Challenges in AI Adoption [& Solutions]

It’s safe to say artificial intelligence (AI) has entered mainstream adoption among enterprise and small-and-medium businesses (SMBs) across the globe, with up to 30% of organizations expected to lead their business strategies with generative AI by 2025, according to Gartner.

But with the surge of interest in this new, rapidly evolving field of technology comes several challenges to address, to ensure a successful implementation and long-term success.

In this blog, we’ll cover the three key business challenges in AI adoption today, based on the SparkNav team’s experiences with customers adopting generative AI tools, and the best and most practical solutions for your business to get the most out of your new investment.

AI adoption challenge #1: High implementation costs

According to MicKinsey’s annual The State of AI report (2024 edition), 67% of respondents expect their organizations to invest in more AI over the next three years. But for many SMBs in particular, the high initial costs of AI implementation remain a significant barrier to entry.

The fact is AI projects require substantial investment in technology, infrastructure, and skilled personnel, making it difficult for smaller businesses operating on tight budgets to afford. The IBM Global AI Adoption Index reveals smaller businesses are particularly challenged by these costs, often needing to allocate a larger percentage of budgets compared to bigger companies.

According to Deloitte’s State of AI in the Enterprise report, even multinational organizations with larger budgets struggle to justify the costs associated with AI implementation, which include both the acquisition of generative AI and analytics AI tools, and commitment from key stakeholders to invest in them. That report highlights that 50% of enterprise respondents cite lack of executive commitment as their top challenge in scaling AI within their organizations​.

AI adoption challenge #2: Lack of expertise and skilled workforce

Another challenge for SMBs in AI adoption is the shortage of expertise and skilled workforce. 

Many businesses lack the in-house talent required to develop, deploy, and manage AI systems effectively, owing to the relatively recent surge in availability of AI solutions in the market. According to McKinsey’s AI report, 56% of executives cite a lack of AI talent as a significant barrier to adoption. This skills gap makes it difficult to fully leverage AI’s potential, as SMBs and enterprises alike struggle to understand and implement complex AI algorithms and models​. 

Additionally, training your existing staff to become proficient in AI technologies can be time-consuming and costly, further complicating the AI adoption process​. As Technopedia points out, the hype of AI has also resulted in misleading and disappointing SMBs with unrealistic expectations about the accessibility of AI and its benefits, resulting in some companies investing into the technology and its solutions ahead of their in-house staff being capable of using them.

AI adoption challenge #3: Integration with existing systems

Integrating AI solutions with your existing systems and processes is another big challenge for SMBs. It’s fair to say you may have legacy systems that are not compatible with modern AI technologies, and as such, require a significant undertaking to integrate with newer AI tools. 

A study by Accenture found that 40% of organizations experienced integration issues when implementing AI, which can result in operational disruptions and inefficiencies. For your SMB, these integration challenges can be particularly daunting, as you might not have the technical expertise or resources to navigate the complexities of merging new AI systems with your established infrastructure or business-critical legacy (or proprietary) applications on your own.

Solutions and recommendations for successful AI adoption

These aforementioned challenges are tough, but not impossible to overcome. We recommend your SMB take the following strategic steps when beginning your journey of AI tools adoption:

Opt for scalable AI solutions:

Start with cost-effective, scalable AI tools that can grow with your business. Cloud-based AI services within platforms such as Microsoft Azure, for example, offer flexible pricing models and reduce the need for significant upfront investment​, as they are included as part of your hosting arrangement with the platform.

Partner with AI experts:

Collaborate with external experts or consultants who specialize in AI. This can bridge the skills gap and provide the necessary expertise to implement and manage AI technologies effectively​, and give you peace-of-mind that you’re on the right path.

Invest in training:

Upskill your existing workforce through targeted training programs. This not only builds internal AI capabilities, but also ensures smoother integration and operation of AI systems​. The best external IT partners offer cyber awareness training programs that include AI as part of their up-skilling workshops and webinars.

Utilize managed services:

Above all else, consider engaging the assistance of a managed service provider (MSP) that specializes in AI adoption as part of their broader IT support services and solutions. MSPs can offer comprehensive AI solutions, from initial consultation and implementation to ongoing support and maintenance, ensuring a seamless AI integration process​ while working closely with your team to meet your immediate and long-term business requirements.

3 key business Challenges in AI adoption [& solutions]: Next steps

Are you embarking on your AI adoption journey to enhance your SMB’s IT capabilities? Speak with the SparkNav team today to learn how our generative AI specialists can help ease your transition to sophisticated AI solutions with a targeted, strategic adoption strategy and process.

Robert Griffin
Robert Griffin
As COO, Robert Griffin plays an instrumental role in aligning operational excellence with strategic goals by leveraging his decades of experience in enterprise leadership. With deep knowledge and expertise in security, governance, risk, and compliance (GRC), and AI, his insights are often shared through thought leadership channels, including LinkedIn and blogs. → Follow Robert on LinkedIn.