04 Aug Global Mobility Pros: How AI Will Reshape Assignees’ Roles

Despite its recent emergence, generative AI is already seeing widespread experimentation and adoption, according to a Mckinsey report, touting 2023 as its breakout year. A recent survey found 79% have some exposure to gen AI for work or personal use. About 22% regularly use it professionally, especially in North America.

With these significant numbers, global mobility professionals will be busy sorting out how one qualifies for jobs based on their exposure to AI, so far.

There’s a new urgency based on how global mobility professionals can figure out who qualifies for AI jobs. The clue lies in following how organizations integrate gen AI, with 1/3 reporting regular use in at least one function.

Of companies using AI, 60% now leverage gen AI tools. About 40% expect gen AI to increase overall AI investment. About 28% already have gen AI on their board agenda. Marketing, product development, and customer service lead adoption, aligning with high-value use cases.

Expectations for impact are high: 75% see gen AI causing significant or disruptive industry change in 3 years. Tech and financial services anticipate the most disruption. While all industries will see effects, those relying on knowledge work may experience greater impact.

Previous estimates suggest tech could see 9% revenue value from gen AI applications. Banking, pharmaceuticals, and education follow at 5-4% value. Manufacturing faces lower effects due to gen AI’s language strengths.

In summary, gen AI is already here and rapidly gaining traction across regions, sectors, and roles. Leaders anticipate major implications, though some industries will likely transform more than others.

Gen AI risk preparation

Despite rapid adoption, most organizations seem unprepared to address gen AI risks.

Only 21% of companies using AI have policies for gen AI use at work. 

When asked about risks, inaccuracy topped responses, even over cybersecurity and compliance. But only 32% are mitigating inaccuracy, and 38% address cyber risks with gen AI. This cyber rate is down 19% from last year.

Overall, as in previous surveys, most state their organizations are not tackling AI-related risks. There appears to be a lag between gen AI adoption and governance to ensure responsible use.

While excitement is high, proactive risk mitigation practices are still lacking. To realize benefits while minimizing harms, companies should urgently prioritize gen AI training, policies, and controls ahead of widespread deployment. The time to build ethics-minded governance is now.

Organizations achieving high value from AI – over 20% EBIT attributable to AI – are outpacing others in gen AI adoption.

These AI high performers already use gen AI in more functions than others, especially product development and supply chain risk. Looking at all AI, they also exceed peers in leveraging traditional AI for product features, development optimization, and new AI products.

Other leading uses include risk modeling and HR applications like performance management and workforce optimization.

Charging ahead with new genAI capabilities

Companies with proven AI success are charging ahead with new gen AI capabilities. They are embedding the technologies deeply in key functions to maximize business impact.

For organizations striving for AI leadership, committing to gen AI across operations and offerings appears crucial. Laggards risk widening gaps in competitiveness and performance.

High AI performers take a different approach to gen AI’s business objectives. They are twice as likely to use it for entirely new revenue streams rather than cost reduction. Increasing the value of existing offerings with AI-based features is their top goal.

These leaders invest over 5X more in AI than peers, with over 20% of digital budget toward AI. They also embed AI capabilities more broadly, adopting AI in 4+ functions and leveraging more AI types like knowledge graphs.

The challenges they face reflect advanced maturity – issues like monitoring models in production. Others struggle with AI strategy and resources.

Proven AI leaders are not coasting but leaning into gen AI for transformational growth. They embed AI deeply across the business to uplift offerings and operations. While challenges remain, high performers seem positioned to continue advancing the frontier.

Responsible AI best practices

Even AI leaders have made progress to make responsible AI best practices like MLOps (machine learning operations). Only 35% of high performers report reusing components rather than reinventing the wheel, though far exceeding others at 19%.

MLOps technologies like live monitoring may be critical for safely scaling transformative gen AI. 25% of high performers have full systems monitoring with instant alerts versus just 12% of others.

Pioneers must continue maturing MLOps and governance to fulfill gen AI’s promise. While ahead of peers, even proven performers have ample room for improvement as applications grow more advanced. Prioritizing safety and ethics now can enable cutting-edge innovation while building public trust. (Dennis Clemente)