Laurent - stock.adobe.com
AI laggards will lose out as rivals make efficiency gains
McKinsey expects artificial intelligence to benefit the economy overall, and early adopters may be able to double their cashflow
Artificial intelligence (AI) could boost GDP by about 0.5% by 2030, by creating 5-10% value in digital data flows, says a report from McKinsey. But businesses that fail to use AI effectively could fall behind their rivals.
According to McKinsey’s Modeling the impact of AI on the world economy, published in September 2018, AI technologies could lead to a performance gap between front-runners in AI and slow adopters and non-adopters.
The report’s authors said companies that fully absorb AI tools across their enterprises over the next five to seven years are likely to benefit disproportionately compared with those that adopt AI more slowly.
By 2030, the competitive advantage of using AI could mean that front-runner companies double their cashflow, according to McKinsey. The report suggested that AI front-runners will tend to have a strong starting digital base, a higher propensity to invest in AI, and positive views of the business case for the technology.
McKinsey warned that AI laggards may experience a decline of about 20% in their cashflow from today’s levels, assuming the same cost and revenue model. “One important driver of this profit pressure is the existence of strong competitive dynamics among firms, which could shift market share from laggards to front-runners and may prompt debate on the unequal distribution of the benefits of AI,” the report said.
Read more about AI benefits
- In IT, a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain.
- Robotic automation is changing the infrastructure management game, helping IT departments to make fewer mistakes and boost their productivity.
McKinsey’s research involved more than 400 cases in which companies and organisations could potentially use AI. It found that AI is already relatively applicable to real business problems and can have significant impact in areas such as marketing and sales, supply chain management, and manufacturing.
According to McKinsey, feed forward neural networks, recurrent neural networks and convolutional neural networks could together enable the creation of between $3.5tn and $5.8tn in value each year across nine business functions in 19 countries.
