Scaling AI remains a challenge for most organizations
Although artificial intelligence is already present in most organizations, few companies have managed to scale it in a way that generates sustainable economic results.
The challenge for companies is to integrate AI with processes, data, talent and specific business objectives.
Nearly 92% of organizations already use artificial intelligence in some of their functions, but only 8% have managed to scale these initiatives to generate real and sustainable economic impact, according to data from McKinsey & Company.
AI adoption does not in itself guarantee a competitive advantage. For Servinformación, the value of these technologies depends on companies’ ability to integrate them with specific business needs.
Identifying use cases and measuring results.
One of the first steps is to identify use cases directly related to corporate objectives. AI can help optimize costs and time, improve the customer experience, anticipate behaviors, support decision-making, develop new services and generate growth opportunities.
The next step is measuring the value generated. Defining indicators before implementing a solution makes it possible to determine whether an initiative produces results and which ones have the potential to scale.
Productivity, revenue, costs, quality, response times and customer satisfaction are some of the variables that can be part of this evaluation.
“The conversation about artificial intelligence should move from how many solutions a company has to what results it achieves with them. An organization gains a competitive advantage when it identifies processes where AI can generate a specific impact, establishes indicators to measure it, and has the technological and organizational conditions to scale it,” says Juan Manuel Urrego, Regional Innovation Manager at Servinformación.
Creating the conditions to scale AI.
The ability to scale these solutions into real processes also requires reviewing data, infrastructure, security, talent and AI governance, as well as adapting workflows when the technology changes the way people perform their functions.
“A proof of concept can demonstrate that a technology works, but an AI strategy must demonstrate that it generates value for the business. The move toward broader adoption requires reliable data, infrastructure, specialized talent, security and a roadmap that connects each initiative with a business priority,” Urrego adds.
In summary, the shift from experimentation to strategic adoption involves turning artificial intelligence into a capability that generates measurable results, can be scaled and responds to the priorities of each organization.
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