CTOs turn to ‘lean’ AI to overcome implementation challenges
It’s amazing the sway that two letters can have. Investors’ ears prick up. Rivals fear obliteration. Shareholders smell progress. But for all of the outsized value that a simple mention of AI can bring to the boardroom, there are only a few companies really able to maximise its commercial potential.
This is because applying AI effectively requires a large amount of infrastructure, both cultural and material. For advances in machine learning (ML) and other frontier fields to be truly integrated into a business, there needs to be a considerable quantity of data, a clear process in place and skilled practitioners who can put these to good use. Many businesses, even larger ones, do not have those ingredients to hand. For other, smaller enterprises, there simply isn’t the time or money to create an effective internal AI operation.
As a result, specialist companies, often startups, are offering a leaner, outsourced AI operation as a service. In the process, these specialist companies are expanding the applicability and power of AI in all of our lives. The more that businesses are able to tap into these technologies, the more customers they’ll reach.
“By outsourcing AI to highly specialised tech companies, not only individual companies but also the entire industry can benefit as the AI models can reach some level of generalisation,” says Ramakrishna Nanjundaiah, co-founder of Phantasma Labs, which provides AI-based models for automotive companies and smart factories. “Generalisation is hard to achieve if industries do not share insights on the range of use cases and the intended value expected from the AI,” he explains.
By working across industries, such specialist companies are more likely to make real advances in the field, potentially to the benefit of all. By focusing exclusively on AI tasks, these companies are far better equipped to try out new techniques and approaches, and become true experts at applying breakthrough methods to a variety of real-world cases.
This might strike many CTOsas anathema to their modus operandi. Aren’t they, after all, the harbingers of innovation? Outsourcing an AI project requires a level of self-awareness and humility, tapping into a risk-averse mentality and admitting that such quests may be beyond an enterprise’s core capabilities. Many businesses cannot justify expensive outlays that result in failure. And as a frontier technology, AI projects are far from guaranteed to succeed.
A deciding factor will often be the maturity of the company’s existing data capture and management systems. The unthinking accumulation of data for data’s sake is insufficient to foster a competent AI operation. The appropriate data has to be captured and then processed, tagged, stored and updated regularly in order for it to be useful.
