Upskilling the IT function to deploy artificial intelligence (AI) is undeniably important, but the modern skilling strategy relating to AI success is not just about technology skills. As every business transforms into an AI-driven company, there is a growing need for a comprehensive and modern skills strategy for long-term success. That strategy needs to be far-reaching, across departments and key functions and demands an emerging skillset that relates to AI approach, impact and implications. Only by fostering this business-led and emerging skillset across the entire organisation can companies truly benefit from the opportunity that AI presents.
An organisation’s AI skills can make or break its ability to effectively harness AI. According to Dell’s (Innovation Catalyst) research, 74% of businesses in Asia Pacific and Japan say they have a shortage of AI talent. But this is not the only factor holding them back, they also cite data privacy and cybersecurity concerns (36%), and the lack of right resources to manage the evolving legislative and regulatory landscape (31%). But skills are a pervasive factor across all of these considerations, they are all inextricably linked.
With so much of the business success linked to AI and related skills, how should organisations consider this link as they build a future as an AI-ready or AI-driven business?
Firstly, is to understand the evolution of what we mean by AI skills. The skill or knowledge that needs to be incorporated into key roles across the business is targeted at building a conceptual understanding of how AI fits within the business strategy. This is building robust working knowledge of what AI is and is not, it may sound simple but leaders should push to dispel any myths and ensure that basic understanding and definition are commonly understood. We have deployed such a learning programme at Dell, for example. For this, you can see why organisations can’t rely solely on acquiring outside talent as a solution; it’s crucial to focus on upskilling and reskilling existing employees.
For some roles, although not all, there should be a basic understanding of the AI stack, where it fits in the organisation’s use of technology. For example, knowing that large language models are a tool to be used, not a means to an end or a ‘fix’ for a business problem.
That is why we also look at the structure of the skills landscape. The future of different departments, functions and levels of seniority will have a different need according to the matrix of experience, skills and knowledge. Some will emerge as innovators – those who can have meaningful conversations about AI-related strategy, implementers – those who will make sure it is deployed in line with business strategy, and influencers – those who need to have a say in the approach.
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A key skill is the mindset to understand what potential risks are at play as the business evolves with AI. They need to have enough understanding to know what relevant compliance or data regulation issues, specific to their function or business unit, could be impacted by AI and its deployment. What is common across these scenarios and needs is that working technical knowledge of AI is not needed, but the understanding of its definition and potential impact is. Then, across certain functions such as legal, compliance, data, the business can establish where on the sliding scale each role and department should fall in terms of depth and breadth of required knowledge.
For roles such as the CIO or IT director, automation and AI have driven knowledge up the stack, which means automation has meant operating IT and indeed AI, will require decreasingly less technical knowledge over time. Even to the level of AI-optimised infrastructure. AI will only intensify this as this trend continues. This means a democratisation of technical skills -- more people will have the knowledge required and technology-focused roles will become more strategic and less technical over time.
Adopting AI also means that the baseline level of knowledge across the workforce will be higher, so organisations need to focus on ensuring that they have the right subject matter experts with the right mix of skillsets to complement and create business value. Business leaders should be mindful of this, because as technology and AI become more and more of a differentiator, their competitors are on this journey too.
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So how can organisations embrace this modern skilling strategy?
- Establish where the business is on the continuum of AI, and therefore the continuum of skills. This means assessing the skill maturity level against the overall business AI maturity level and planning a route from there so that both skills and the business continue to grow.
- Be driven by the overall AI-optimised business approach, not by quick ROI or convenience. To bridge the gap where there is a lack of skills, leaders must avoid the temptation to skip over this with ROI in mind. The mindset should instead be working on business strategy and skills together.
- Be flexible to adapt to new operational models demanded by AI-augmented productivity, and instil a broader culture of being comfortable with change.
Modern skilling strategies should align with modern AI strategy, enabling an economy-wide impact. AI will transform talent needs across entire industries, markets and functions far beyond IT or any individual organisation. To thrive in the AI era, skills strategies must truly drive the future of AI-driven businesses.
Peter Marrs is the president for Asia-Pacific, Japan and Greater China at Dell Technologies
