Talent shortage key barrier to AI says Andrew Ng

Talent shortage key barrier to AI says Andrew Ng

Artificial intelligence and machine learning are the buzzwords of the day, but how fast are organizations warming up to AI technology? A recent estimate from consulting major McKinsey predicts that by 2030, an additional economic output of US$13 trillion will be generated through AI alone. This figure is going to push up annual global GDP by 1.2%. No enterprise of repute, or no industry domain for that matter, will remain unaffected by artificial intelligence.

This is where AI guru Andrew Ng offers a few words of caution. Let us not assume that all organisations are equally eager to jump on the AI bandwagon, he warns. Co-founder of Coursera and an Adjunct Professor of Computer Science at Stanford University, Andrew Ng identifies some significant barriers that stand in the way of a smooth AI transformation for many enterprises.

  • Extreme shortage of AI talent – One obvious way to address this is hiring, but the fact is AI experts are still in short supply across the market. Organisations that will parallelly focus on upskilling internal talent through training, would overcome this challenge better.
  • Identifying the right projects – AI projects should not be chosen based on the novelty factor. Only two deciding factors should matter: strengthening the business, and feasibility. Any project that does not meet these two criteria are worth not venturing at all. This requires a combination of business acumen and profound technical insight.
  • CEO-level support – Planning and implementing an AI strategy to effectively impact the entire organization will never be possible without adequate CEO and board-level commitment.
  • Setting up AI-specific processes – Structuring effective workflows and processes are key to embracing any new technology. A chief AI officer or similar AI leader can serve as a point of contact, establish new workflows, and help integrate technology from external sources.
  • Adequate data infrastructure – In house legacy data is valuable but leveraging this data requires deep judgment. A scalable strategy for collecting, labeling, and improving data sets will aid smooth AI transformation.
  • Regulatory hurdles –Enterprises working in highly regulated industries (such as self-driving cars, healthcare, etc.) face industry-specific compliance challenges. Measured, monitored roll-outs can be the only effective solution.
  • Lack of awareness – Educating the entire workforce about AI is necessary because companies that fully harness AI advantages are structured and expanded through strategies radically different from traditional companies. Spreading awareness will help leaders within the company to drive necessary changes to their familiar landscape.
  • Fear of change – This is nothing new, because any change is fraught with the fear of unknown. It can be minimised through planned and sustained communication. Employees should be made to see the benefits of the proposed transformation. However, if the company refrains from implementing an AI strategy fearing employee resistance – that might be suicidal.

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