Succeeding with AI

Francesca Lukes at Wanstor explains what organisations need to do to make a success of Artificial Intelligence

AI is quickly carving a multifaceted role across businesses everywhere, from enhancing data-driven decision-making to redefining customer experiences and employee productivity. The list of potential applications is practically endless, leading to a huge amount of speculation about what journey organisations are on.

For instance, there has been much concern raised about the extent to which AI will replace human workers. While the technology clearly has the potential to make a massive impact on traditional job roles across the globe, recent research has revealed that for many organisations, workforce reduction is not currently an AI-driven priority.

So what is? At present, the primary purpose identified for AI is to deliver improvements in areas such as data analysis and insights, with 56% focusing on its role in data-driven decision-making.

Of equal importance is using AI to boost operational efficiency, with automation and better customer experiences also high on the list of AI implementation priorities.

In contrast, just 15% of organisations see workforce reduction as their primary purpose for implementing AI solutions today. The extent to which this changes in the years ahead is clearly open to debate, but using AI as a ‘plug and play’ alternative to human labour has not yet arrived at any appreciable scale.

The generation game

The successful adoption of AI in the workplace is, however, subject to a range of important challenges and influencing factors. These apply differently across various employee demographics, revealing that, at present, there exists a complex set of attitudes and concerns relating to acceptance or resistance to AI within the contemporary workplace.

For example, Millennials and Baby Boomers exhibit different concerns regarding AI. For Millennials, job security is their key concern (34%), surpassing even the Baby Boomers, who are traditionally seen as more resistant to technological change.

There is also an interesting range of attitudes towards AI across the C-suite and employees. While 83% of the research respondents believe their teams are open to AI adoption, the top barrier seen in the C-suite is resistance to change (36%) and implementation costs (36%), indicating a potential disconnect between leadership and employee perspectives on AI.

This suggests that while there are some important hurdles to overcome, the general attitude towards AI within teams is largely positive, potentially paving the way for smoother adoption processes.

Organisations can further enhance their chances of success by tailoring AI training programs to address the specific concerns and learning styles preferred by different age groups in the workforce. This can help ensure that everyone is comfortable and proficient with AI technologies.

In addition, fostering open communication channels between the C-suite and employees can help align perspectives on AI adoption, address resistance to change, and emphasise AI’s benefits for each organisation.

These are important considerations, particularly because AI has enormous potential to reshape the skills landscape in the near future. For instance, nearly three-quarters of organisations think that using AI effectively will become a critical skill for employees in the next two years, with only 6% believing the opposite.

This situation is reminiscent of the evolution in computer skills decades ago, with nearly 80% agreeing that AI proficiency will be as necessary in the future as computer skills were in the 1980s.

The devil’s in the data

None of these issues will remain relevant, however, unless organisations can successfully implement advanced AI solutions that meet their business needs and are accurate, secure and reliable. The main issue here is around the data being used as the basis for training AI systems and the subsequent decisions being made.

For example, half of organisations have not yet worked to ensure that the data they use to power their AI tools is reliable enough and of sufficient integrity to produce effective outputs.

What’s more, less than half have currently established clear data security protocols to protect sensitive information, while only 50 per cent regularly monitor and update their data sources to maintain the relevance and accuracy of AI output.

These considerations become more important as the sophistication of AI tools and the size of the datasets they use continue to increase. Without effective preparation, data quality and security will pose a serious risk to the realisation of AI’s full potential, possibly leading to inefficiencies and wasteful technology investments.

At the extreme end of the scale, a lack of data preparedness could mean the difference between the success and failure of an AI strategy, with an equally damaging knock-on effect on the business as a whole.

So, while organisations and their leaders work out how big an impact AI will have on the future of work, one issue they can’t afford to debate is the importance of reliable and secure data sources.

Get this right and the way is clear to focus on delivering practically endless benefits. Get it wrong, however, and organisations could be risking their entire future.

Francesca Lukes is CEO of Wanstor 

Main image courtesy of iStockPhoto.com and ookawa