Winning with AI is a state of mind
Thomas Meakin, Jeremy Palmer, Valentina Sartori and Jamie Vickers from QuantumBlack, a McKinsey company, share how companies capturing lasting value from artificial intelligence think differently, from the C-suite to the front line. Here’s how to make the shift from opportunistic efforts to a truly AI-enabled organization as part of techUK's AI Week #AIWeek2021
Executives have seen that the move from running artificial intelligence (AI) experiments and proofs of concept to capturing lasting value at scale requires an investment in strong foundations. These include aligning AI with core areas of the business; embracing important cultural and organizational shifts; and investing in new kinds of technology, training, and processes for building AI.
More and more organizations are adopting these basic practices, and those that do tend to report the highest bottom-line impact from AI. But successful organizations don’t just behave differently; our experience in thousands of client engagements around analytics and AI over the past five years shows that they also think differently about AI. At these companies, AI is etched in the collective mindset (“We are AI enabled”), rather than simply applied opportunistically (“Here’s a use case where AI can add value”).
Having this mindset means deeply internalizing the long-term competitive benefits of augmenting human decision making, processing data from many sources at a massive scale and enormous speed, and continuously adapting business models and operational strategies based on signals from the data.
It means valuing collaboration and continuous learning over individual knowledge and experience, with employees seeking out new data, skills, workflows, and technologies for driving ongoing performance improvements. Individuals and the collective crave not just to know more than they did, say, last year, but to know more in the future.
Finally, this mindset embraces end-to-end thinking and consistent architectural principles over siloed solutions when combining new technologies and tools with existing infrastructure.
This shift is not easy. Leaders must reorient their own thinking and then move every mindset in the organization. We find that the leaders who succeed in this effort do so in a few ways: they reorient the company’s focus toward its multiple instead of its earnings, and they actively emphasize and encourage global learning loops and technological adaptability throughout the organization. Although this mindset shift doesn’t supplant an investment in strong foundations for AI, organizations that embrace an AI-enabled mindset are better at meeting the most formidable technical and cultural challenges and making organizational changes so they can reap the full value that the data and technology offer.
We’ll explore the levers companies can use to make the shift and, since mindsets can be tricky to measure, offer ways to gauge if the shift is working. To show the utility of these ideas, we’ll describe experiences of two industry giants whose leaders are betting their futures on winning with an AI-enabled mindset. One of them is a global pharmaceutical company whose multiyear AI transformation achieved a 10–15 percent reduction in patient-enrollment times for clinical studies and a 10 percent gain in productivity across its initiatives, allowing it to redirect hundreds of millions of dollars toward other pressing needs. It is now on course to increase the rate at which drugs can safely be brought to market and replicate this ever-growing cycle of improvement in other divisions, such as manufacturing. The other company, a leading bank, is targeting a 50 percent reduction in time to value for new use cases across the organization as it makes the mindset shift, enabling it to rapidly deploy many hundreds of AI models that drive continuous learning and increase its annual revenue run rate with its first AI-driven learning system.
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Katherine joined techUK in May 2018 and currently leads the Data Analytics, AI and Digital ID programme.
Prior to techUK, Katherine worked as a Policy Advisor at the Government Digital Service (GDS) supporting the digital transformation of UK Government.
Whilst working at the Association of Medical Research Charities (AMRC) Katherine led AMRC’s policy work on patient data, consent and opt-out.
Katherine has a BSc degree in Biology from the University of Nottingham.
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