Avoiding pitfalls in AI adoption

Evaluating need

While the transformative potential of AI is clear, the buzz around the technology has generated a fog of hype, in response to which many entities have rushed to adopt AI solutions without fully understanding its applications, limitations and risks. This haste can lead to misaligned expectations and poor investment decisions. For instance, while AI can successfully automate customer interactions, it can also produce inaccurate or irrelevant data outputs if not properly managed. Businesses must critically evaluate their needs to determine if AI is the right solution for their specific challenges.

For example, a client in the hospitality industry deployed an in-house AI tool to generate daily metrics for senior management. However, as it was not trained on hospitality industry data, the metrics it showcased were far too generic to be of much value to the business’s managers. To become useful, the tool had to be retrained and recalibrated with industry-specific knowledge and datasets.

While clearsighted assessment and strategic planning go a long way in helping businesses avoid the pitfalls of hasty adoption, deployment may also be inhibited by other challenges. High implementation costs, human resource limitations, and resistance to change and adoption are often cited as impediments.

Resources

Implementing AI comes with substantial upfront costs including the purchase of advanced software and hiring of skilled personnel as well as ongoing maintenance expenses. These investments are essential to undertake tasks such as data analysis, predictive maintenance and customer service automation.

For companies operating with tight budgets, these financial barriers can be daunting, and smaller businesses in particular often struggle to justify and sustain investment. For these, customer service chatbots or simple data analytics tools, which are more affordable and manageable to implement, can be a good starting point.

Businesses in the region also frequently lack the human resources needed to develop and maintain AI systems. AI projects demand specialised expertise in data science, machine learning and software engineering – skills that are not always available or affordable. The Middle East is still developing its tech talent pool, and for the time being companies may need to import talent from overseas, or employ consultants.

Resistance

Overcoming resistance at management and board levels is paramount, with leaders concerned about job displacement, the accuracy of AI-generated results, and overall return on investment. Leadership may also be sceptical about the promised efficiencies and wary of the changes involved.

Overcoming this scepticism requires clear communication. Demonstrating tangible gains and providing a roadmap for integration can help win management buy-in.

Further down the hierarchy, fear of job loss and concerns about the relevance of human roles often leads to resistance from employees, which may manifest itself as reluctance to engage with new AI systems, decreased productivity and even active pushback against the changes.

Companies should emphasise that AI is a tool that augments rather than replaces the capabilities of staff. Training programmes, possibly offered by external consultants or tech partners, can help alleviate employees’ fears and foster acceptance.

One prominent retailer in the region has shown how it is possible to embrace AI’s potential effectively – in this case by augmenting its accounting and finance function – despite initial employee resistance and data privacy concerns. By collaborating with an AI consultancy and introducing strategic initiatives such as employee training and pilot projects, the business was able to integrate AI into its finance function so that it generated financial reports and compared reports to prior periods to help determine the reasons for fluctuations. Within eight months, the approach had yielded impressive results: a 39% saving in time, greater accuracy and the ability to reallocate three staff members to other crucial functions.

Success

A successful deployment of AI may necessitate a cultural shift to encourage innovation and continuous learning. For companies with established practices and mindsets, this can be particularly challenging.

For finance decision-makers, the first step towards a successful AI journey is to recognise and address the critical issues of high implementation costs and limited resources, change management and adoption resistance, and data security and privacy concerns.

By understanding the challenges and developing strategic plans to overcome them, businesses can leverage AI to drive growth, innovation and competitive advantage. Careful planning, strategic investment and a commitment to overcoming obstacles will smooth the path.