How companies tap, train and fine-tune generative AI tools could also have a major impact on both customer and employee trust. IT leaders must demonstrate that AI can be rolled out responsibly – protecting privacy, preserving jobs and producing accurate content – to get people on board.
For their part, employees are interested in learning more. Roughly three in four say they hope their company explores more AI implementation. But organisations need to strike the right balance between innovation and ethics to build enthusiasm about new ways of working. Otherwise, internal resistance to change could hinder meaningful progress.
Here are ways that CIOs can adopt and implement generative AI solutions that will both boost the bottom line and empower employees to drive responsible change.
Develop (and Communicate) a Clear AI Strategy
Generative AI may seem to work like magic, but successful rollouts don’t happen overnight. Getting farther faster with this technology demands a clear vision of what the organisation wants to achieve – whether that’s boosting productivity, increasing customer satisfaction or improving the employee experience. From there, teams can start brainstorming different ways to meet those goals.
However, what they can achieve depends on the quality and quantity of the data AI models have access to. While some out-of-the-box solutions come pre-trained on relevant datasets, most models must be fine-tuned with proprietary data to deliver the most meaningful results. That means CIOs must focus on connecting internal data in a responsible way.
CIOs should also ensure the organisation’s AI strategy keeps scalability top of mind, thinking through how new solutions will integrate with existing processes. The point is to improve results while also staying nimble, adopting technology that can adapt as both the business and AI applications evolve.
While proactive strategic planning is essential to make generative AI investments as effective as possible, that doesn’t mean CIOs need a fully baked plan to get started, said Mohammed Bari, Director, Powered HR, at KPMG.
“You can have a strategy cooking while you're analysing your use cases,” he said. “Don't wait, though. Go ahead, get started. Start thinking, start brainstorming and start experimenting.”
Dip Your Toe in with Specific Use Cases Targeting Pain Points
Your generative AI strategy tells teams where they should be headed. Specific use cases show them which path they should take – and that extra direction can make all the difference.
“What we're seeing with AI is it's a bit more use-case-led,” said Bari “So, I've got a big problem in recruitment. I've got a big problem in redeploying talent. Well, how can AI solve that?”
Take for instance, a company that receives thousands of CVs every day. It’s impossible for an individual employee to sift through all of them – but generative AI can help bubble the best candidates to the top. By focusing on skills – what the business has, what it needs and what different applicants bring to the table – generative AI can quickly find the best fit. With an internal skills marketplace, organisations can also quickly find ideal candidates already in their ranks.
While the details of each use case will vary, focusing on major pain points can help companies get quick wins – and help teams learn how generative AI really works. When people start to apply this technology to their daily work, they’ll identify potential uses that make their jobs easier. And as employees become personally invested in AI rollouts, larger productivity gains promise to appear.
Prioritise Ethics and Governance from the Ground Up
AI models are trained on massive datasets – but that doesn’t mean that data is always accurate. It could be biased, replicating the unconscious bias of its human trainers, or just plain wrong. Plus, there’s the risk that training data has been manipulated by malicious actors, as these types of cyberattacks are becoming more valuable and therefore common.