How do organizations build future-ready AI-fluent teams?

In the rapidly evolving landscape of IT solutions and services, businesses are increasingly grappling with the implications of artificial intelligence (AI) on their revenue streams, cost structures, and risk profiles. While technology forms the bedrock of digital transformations, it is the people who truly drive change. According to the future of jobs report released by the World Economic Forum (WEF), 23% of jobs are expected to change in the next 5 years, while 44% of worker’s core skills will be disrupted by 2027. In our experience of supporting clients through technological evolution, we have seen innovation pave the way for new jobs with novel skill sets.

In my role as leading the delivery of applied AI platform and implementation services, I have a front-seat view of how roles are changing as AI becomes part of regular workflows. For example, in a recent implementation, we used generative AI-powered HR chatbots to dynamically access the latest information from HR documents to reduce the coordination tasks for our HR Business Partners, allowing them to focus on personable employee interactions and building team connections.

With AI taking over highly structured or repetitive tasks, uniquely human expertise and skills such as creative problem-solving, strategic thinking, and emotional intelligence become more valuable. This will change the future workforce, creating new job roles that meet the changing demands of AI technology, ethical norms, and human oversight.

Building a multi-skilled workforce to unlock AI’s full potential

The emergence of AI Foundation models in the last couple of years has consumerized the technology beyond AI experts and data scientists, to consumers and business leaders. While these skills remain crucial, our experience of implementing large scale AI projects suggests that the future is not only about AI algorithms and data, but also about people from different disciplines coming together to apply the technology to solve complex problems.

As you embed AI in applications to provide a more personalized experience to your customers, sure, the data scientists can design the right algorithms and analyze large amounts of data, but to create even better customer experiences, wouldn’t it be helpful to have an ethics expert ensure transparency from the start or a user experience specialist help customers consume information seamlessly? This means having small agile teams with diverse skills for complex AI implementations, and not just traditional technical skill sets.

Emerging job roles beyond algorithms and data

The ability to interact with AI models through effective prompting and openness to incorporating AI into regular workflows are becoming must-have skills. While specific job titles might differ from company to company, I see emergence of new pivotal roles with expertise in the areas below to ensure organizations build and use AI responsibly.

Ethics: With the growing demand for AI assistants in critical areas such as healthcare diagnostics, loan approvals, and insurance claims, how do you ensure there is fairness, accountability, and transparency in the process? This requires consultants to guide AI development and deployment to ensure responsible AI principles are embedded into the design process rather than mere afterthoughts.

Integration: Connecting the latest AI technology with realistic business use cases requires experts with deep knowledge of an organization's operations, data environment and enterprise applications.

Transparency: How many times have you questioned suggestions from an AI model? Finding the right balance between accuracy and transparency in selecting suitable AI models and explaining the rationale behind AI outcomes requires skills missing in many AI implementations today.

User Experience: Do you have to rely only on chat-based interaction with AI models to integrate AI into your applications? AI Foundation models can handle multiple modes of data, such as text, images, audio, and video, which can overwhelm users with too much information. Expertise in designing intuitive interfaces that seamlessly incorporate AI in the workflow would increase user adoption.

Bridging the AI talent gap through ROI-led business use cases

As my colleague Jennifer Mecherippady shared in her blog, “Taking a page from the Agile playbook to advance Artificial Intelligence,” one of the best ways to transform the workforce into embracing AI is to draw on transformational approaches that worked in the past. With the current gap between the demand and supply of talent with AI skills, combining comprehensive reskilling programs for employees with talent acquisition in strategic areas has worked well for CGI and in our client implementations.

Our reskilling strategy has always been identifying and adapting essential base skills to meet future demands. With AI too, we are adopting a targeted reskilling approach for our employees rather than a one-size-fits-all AI training program. Based on our experience, organizations must take the following steps to build an AI-fluent workforce:

1. Conduct AI use case identification workshops to uncover high-impact opportunities.
2. Prioritize use cases based on business value, feasibility, and goals.
3. Invest in persona-based AI training programs tailored to prioritized use cases.
4. Leverage partnerships and specialized AI talent pools to augment internal capabilities.

CGI’s four AI imperatives for action (Envision, Explore, Engineer, and Expand) helps organizations build future-fit and adaptive foundations and teams. Integrating AI into business operations is not only about implementing new technologies but also about cultivating a workforce that can harness these innovations effectively. The journey towards responsible AI adoption is an ongoing process, where we continually innovate, collaborate, and share insights with our clients to deliver trusted ROI-led outcomes.

As you prepare your organization for AI transformation, what other core skills and multi-disciplinary expertise will be crucial? If you are interested in talking more about the insights I have shared, let’s have a conversation.