Once there’s a defined use case in place, the next key step is to stop and consider cultural and social implications — to quote Jurassic Park’s Dr Ian Malcolm:
“Yeah, but your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.”
Depending on the business position, think capabilities on a data maturity model, there may need to be major shifts in existing workflows, skills and capabilities across the company. Business leaders must be crystal clear on how they manage the change, how they communicate the plan and the impacts on the business. Because C-suite executives may be excited by AI, but others, including potential customers, will be fearful if not sceptical.
There must be a clear grasp of how much training or reskilling will be required to make the most of the investment and at what scale. None of this is cheap or fast. Furthermore, it’s essential and the need for it will grow as your AI adoption does. This should be approached with a positive mentality, one which recognises that AI presents exciting new opportunities and a progressive redefinition of jobs.
At the same time, don’t embark on a company-wide initiative. Start with a narrow technical project. This limits the splash damage and enables people to see that, fundamentally, AI can be transformative. It’s a chance for departments to think about future uses and re-organise organically, allowing teams, and associated roles, to align with new workflows, AI or ML.
In instances where multidisciplinary teams need to be hired and built, ensure there’s exposure to MLOps (Machine Learning Operations) skills as well as business domain expertise. Do not get hung up on hiring the perfect skill set — this is all so new that nobody is really well qualified. Don’t let your capabilities limit your ambition, consider contractors or smaller boutique providers that have experience and can facilitate upskilling existing teams.
The above applies even in areas that are distant from the changes — ultimately, I expect all roles to be positively disrupted by generative AI. Invest in your people and the adoption of AI.
Lastly, update policies in line with implemented changes. If the ways of working are being modified then supporting processes, KPIs and incentives must be looked at. Business leaders must also measure the impact these changes have on them. Clear, bidirectional, communication will be a differentiating factor between companies that do well with AI and those that do not.
By taking this strategy, business leaders will adopt AI in a way that leads to happier employees. This is because the technology will be leveraged to make their lives easier, rather than to render them obsolete.