Artificial intelligence is poised to unleash the next wave of digital disruption, and companies should prepare for it now.
That’s the key takeaway from a new 80-page discussion paper from McKinsey Global Institute titled Artificial Intelligence: The Next Digital Frontier.
The report argues AI is finally starting to deliver real-life benefits and will be a powerful force for disruption.
“AI has the potential to accelerate shifts in market share, revenue, and profit pools—all hallmarks of digitally disrupted sectors,” the authors write.
“Early adopters and early case studies demonstrate AI’s potential to transform business processes, shake up entire sectors, increase profits, and create new sources of value. AI applications are starting to reach maturity, and companies with serious, proactive adoption strategies stand to gain significant competitive advantages.”
AI investment is growing and is dominated by digital giants such as Amazon, Apple, Google and Baidu. Globally, McKinsey estimates tech giants spent $20 billion to $30 billion on AI in 2016, with 90 per cent of this spent on R&D and deployment, and 10 per cent on AI acquisitions. VC and PE financing, grants, and seed investments also grew rapidly, albeit from a small base, to a combined total of $6 billion to $9 billion, the report states.
Despite the growing investment, broad commercial deployment of AI is still nascent.
Based on McKinsey’s survey of more than 3,000 senior executives on the use of AI technologies, few firms have deployed it at scale. Only 20 per cent said they currently use any AI-related technology at scale or in a core part of their businesses.
The survey also found business leaders are uncertain about what exactly AI can do for them, where to obtain AI-powered applications, how to integrate them into their companies, and how to assess the return on an investment in the technology.
“AI is more than the sum of its parts: for truly impressive gains, companies are building their AI capability across the value chain, integrating it into core processes, and using it to enable their employees to be more productive,” the report states.
See through the hype to build a business case
To build a business case for AI, organisations must separate “the hype and buzz around AI from its actual capabilities in a specific, real-world context.”
To ensure a focus on the most valuable use cases, AI initiatives should be assessed and co-led by both business and technical leaders and, relate to a firm’s strategy.
“It includes a realistic view of AI’s capabilities and an honest accounting of its limitations, which requires at least a high-level grasp of how AI works and how it differs from conventional technological approaches,” the authors write.
In the immediate term businesses should focus on AI use cases where there are proven technology solutions today that can be adopted at scale, such as robotic process automation and some applications of machine learning.
In the mid-term, the report recommends companies identify use cases where a technology is emerging but not yet proven at scale.
And over the longterm businesses should pick one or two high-impact but unproven use cases and partner with academia or other third parties to innovate, gaining a potential first-mover advantage in the future.
To aid adoption AI providers must focus on building AI solutions for real-world business problems, the report argues.
Laying the foundations
Building a data ecosystem is vital to deploying AI.
“Data is at the heart of the disruptions occurring across economies and is recognised as an increasingly critical corporate asset. Without data, getting the AI engine started is impossible,” the authors write.
Aside from the need to reskill workers to collaborate with machines, specific talent is required to develop AI use cases for individual businesses.
“Roles that companies often have to filll are “translators” and data scientists (“quants”). Translators bridge the gap between the techniques available to data scientists and the real-world problems of management. Quants design, develop, deploy, and train AI technologies,” the authors explain.