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As I’ve blogged about at length in this space, the US economy won’t see sustained growth unless we can boost productivity. And there are a few different theories out there for why productivity growth has been so sluggish since the mid-2000s. Maybe ideas are becoming harder to find, maybe productivity has increased and we aren’t measuring it correctly, or maybe productivity growth is here but it’s just not evenly distributed yet.
If that last theory is correct, and there’s some reason to think it is (per a Commerce Department study, the digital sector has grown at an average annual rate of 5.6% over the last decade, compared to 1.5% overall), then the relevant question for policymakers is how to get these innovations to spread throughout the rest of the economy. That’s where the new McKinsey report “Notes from the AI Frontier” comes in. “Artificial intelligence (AI) stands out as a transformational technology of our digital age,” they write, and after studying 400 different use cases across 19 different industries, they estimate AI can “potentially enable the creation of between $3.5 trillion and $5.8 trillion in value annually” — if its use is broadly adopted.
So, a good deal of value-add. A few charts from the report show where AI is poised to make the most impact:
I would caution patience though for those who think an AI-driven productivity boom is right around the corner. Here’s McKinsey on the many obstacles to the broad diffusion of AI technology:
Organizational challenges around technology, processes, and people can slow or impede AI adoption
Organizations planning to adopt significant deep learning efforts will need to consider a spectrum of options about how to do so. The range of options includes building a complete in-house AI capability either gradually in an organic way or more rapidly through acquisitions, outsourcing these capabilities, or leveraging AI-as-a-service offerings.
Given the importance of data, it is vital for organizations to develop strategies for the creation and/or acquisition of training data. But the effective application of AI also requires organizations to address other key data challenges, including establishing effective data governance, defining ontologies, data engineering around the “pipes” from data sources, managing models over time, building the data pipes from AI insights to either human or machine actions, and managing regulatory constraints.
Given the significant computational requirements of deep learning, some organizations will maintain their own data centers, because of regulations or security concerns, but the capital expenditures could be considerable, particularly when using specialized hardware. Cloud vendors offer another option.
Process can also become an impediment to successful adoption unless organizations are digitally mature. On the technical side, organizations will have to develop robust data maintenance and governance processes, and implement modern software disciplines such as Agile and DevOps. Even more challenging, in terms of scale, is overcoming the “last mile” problem of making sure the superior insights provided by AI are instantiated in the behavior of the people and processes of an enterprise.
On the people front, much of the construction and optimization of deep neural networks remains something of an art requiring real experts to deliver step-change performance increases. Demand for these skills far outstrips supply at present; according to some estimates fewer than 10,000 people have the skills necessary to tackle serious AI problems and competition for them is fierce amongst the tech giants. Companies wanting to build their own AI solutions will need to consider whether they have the capacity to attract and retain these specialized skills.
While the report is thin on implications for policymakers, the authors suggest a few ideas to encourage the broad adoption of AI: greater public investment in AI research and training programs to nurture more AI talent, open data initiatives, and opening up public-sector data to spur private-sector innovation.
The most overlooked obstacle here though might be public disapproval, if the burgeoning techlash is any indication. Here’s McKinsey’s shockingly anodyne description of one use case, which to my ears sounds positively dystopian: “Hawaii’s state tourism authority, working with a major online travel company, uses facial recognition software to monitor travelers’ expressions through their computer webcams and deliver personalized offers.”
If you want a picture of the future, imagine personalized ads delivered as instantaneously as your facial expressions adjust, forever.