Reskilling Talent to Shrink Technology Gaps
Leaders must focus on managing the gaps in AI skills and processes within the organization.
The AI & Machine Learning Imperative
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Frustrated with artificial intelligence efforts, the CIO at a large pharma company characterized the products and services from AI vendors as bright but “very young children” requiring tons of effort from internal staff members to reach the maturity to solve practical business problems. The company could buy AI-enabled products and services, but purchasing alone was not enough. Acquiring sophisticated AI technology still left the organization far from achieving strategic goals and increasing business value.
This company’s dilemma isn’t an isolated case. Despite the growing prevalence of AI technology and copious data within companies, getting value from AI isn’t easy. Even with AI technology increasingly easier to acquire, 40% of organizations making significant investments in AI still do not report business gains from AI. As with technology advances in the past, technology alone isn’t the answer to value.
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Instead, getting value from AI requires investment beyond technology, notably in data infrastructure and talent. AI talent can be a particularly difficult limitation. Once armed with technology and infrastructure, many organizations find that they don’t have the AI skills they need.
Technology creates an inevitable gap — a gap between the sophisticated solutions an organization produces with a given technology and what portion of that production the organization can use. Spiffy models don’t help if people in the organization don’t know what the results mean or what they should do differently based on the results. The problem for managers, therefore, is less about managing the technology itself and more about managing the skills and processes needed by people and teams.
To illustrate, consider the relationship between the maturity of an organization with a particular technology and the sophistication of its use of that technology. As an organization matures, the technical sophistication likely improves in general. But this technical sophistication isn’t distributed evenly throughout the organization. Some employees have greater technical skills than others do. Some organizational roles (such as AI and IT teams working on the production and development side of the technology) are likely more technically sophisticated than those of employees who consume those results (such as upper management or customer service teams). Compounding the difficulty, as the organization matures, the skill levels among employee groups develop at different rates.
As organizations put more resources into a general-purpose technology such as AI, they can produce more sophisticated results with the technology.