Even the administrative tasks so often discussed in the context of automation shouldn’t be viewed as a given. Are we at a point where a procurement negotiation or contract agreement could be handled AI agent to AI agent? Yes, probably. But would you want to give these processes over to technology? That depends on your firm’s partner ecosystem, risk tolerance and overall AI maturity level.
When to build, when to buy
The next critical consideration is timing. As major software vendors embed new capabilities into their platforms, some functions may soon come pre-equipped with AI. Companies must determine whether to invest in developing their own AI tools today — or wait and assume these will be built in tomorrow.
The challenge for leaders, then, is to identify where the business opportunity is so big that it justifies instant investment in creating digital agents — and where it makes more sense to wait for out-of-the box capabilities. A good idea is to establish a cross-functional team that can deconstruct all of the opportunities for immediate AI transformation and then place strategic bets based on your specific operating environment.
For example, should companies integrate AI agents into their finance processes now or wait until their enterprise resource planning (ERP) platforms offer plug-and-play agent capabilities in a few years? In the latter case, an offshore GCC may therefore be a smarter short-term option. On the other side, how long will you need to wait for these capabilities and how customizable will they be for your operations? The answer will be different for every firm and every capability area; the key is to be asking the question.
The rise of physical AI
Beyond software, another frontier is emerging — physical AI. From humanoids and quadrupeds to drones and autonomous vehicles, what were once futuristic prototypes are rapidly becoming viable business tools. This, in turn, opens up fresh opportunities for automation, particularly in sectors like manufacturing, energy and life sciences.
But it brings challenges too. Few companies possess the necessary skills to harness physical AI in-house. Demand is therefore growing for embedded systems engineers, robotics engineers and computer vision professionals. These are not capabilities you can simply upskill. They represent entirely new disciplines requiring specialized training and expertise.
Organizations must therefore decide whether to build those capabilities through their recruitment and retention programs or rent them by engaging boutique firms or professional services vendors. Either way, a future-proofed talent strategy should now include physical AI readiness as a factor in workforce planning.
Don’t wait — upskill now
There is a more pressing skills mandate too: the current workforce. While the type of knowledge and capabilities employees need will undoubtedly shift in the long term, the most effective workers in the short term won’t necessarily be new hires.
Instead, they will be existing team members who already know your culture, systems and clients — and learn how to deploy AI to augment their performance. Companies must therefore empower employees to integrate AI into their workflows today, boosting productivity and competitiveness straightaway.
How they do so is up to them — and a divide is emerging between the carrot and the stick. Some organizations are incentivizing workers to embrace and innovate with AI through rewards, recognition and career advancement. Others are more hard-line. One chief digital officer described reducing their finance team by 20% before redistributing tasks among the remaining 80% who are expected to deliver the same outcomes by leveraging AI. Other organizations have made headlines by mandating that no new hires will be approved unless it can be demonstrated that the tasks cannot be performed by AI.
Both approaches have their pros and cons — and again, it’s up to leaders to decide what best suits their business strategy and culture. Whichever path they take, though, it’s clear that the integration of AI into everyday work requires a broader rethinking of performance management. Whether now or in the future, building AI usage into KPIs and promotion criteria will be key to career growth. Tracking who’s using AI — and how effectively — will become standard practice.
