The AI adoption paradox: can cautious adoption reap maximal benefits?
How do you marry the need to make the fundamental organisational changes that make the most out of AI with the need to do it cautiously, with high levels of oversight to ensure that you can trust it to work responsibly, and ethically?
In the race to adopt AI, there is a flurry of activity happening in boardrooms and technical teams across the country. AI, which even a few years ago seemed to be the preserve of a vanguard of highly innovative companies, has suddenly become a prerequisite for organisations in every sector. Perhaps the stern warning from McKinsey’s 2019 report is ringing in their ears, that “Front-runners [...] could increase economic value by about 120 percent by 2030” whereas “Laggards, who adopt AI late or not at all, could lose about 20 percent of cash flow”.
It appears easy, then, to stay ahead of the curve and reap the financial benefits you need to adopt AI. Yet, according to MITSloan 2020 AI Global Executive Study, it’s not quite that simple, and only 10% of companies are obtaining significant financial benefits from AI technologies.
Humans and AI learning together
So, why is that the case? Fundamentally, humans and machines “think” very differently, and therefore bring different strengths to an organisation. Assuming that you can just “plug in” AI to fix particular business problems, or to guide you where to put your best resource, is a narrow approach and doesn’t often translate into maximal business benefit.
Flipping AI adoption on its head, and using it as a catalyst for organisational change, will enable you to transform your business from the ground up. An organisation that is prepared to learn from AI, learn with AI and teach AI is going to reap significant benefits--with MITSloan reporting that “with organisational learning, the odds of an organization reporting significant financial benefits increase to 73%”.
Does this mean that in order to reap the biggest benefits, you need to jump in wholeheartedly and fundamentally change your systems, skillsets and processes all in one go? That approach for some is scary, and for others, impossible.
We know this firsthand. Our clients often have deep organisational constraints, regulatory requirements and sometimes even a challenging history with innovation - and yet some of their applications and use cases have the broadest potential for AI to enact real societal change, and human impact. Understandably, these organisations are more likely to take a more cautious approach to innovation and AI adoption. In fact, these applications are exactly the types that the recent European Commission proposal for AI Regulation across the EU, wants to set regulatory baselines for -- mandating certain transparency, quality and oversight measures in an attempt to deliver trustworthy AI.
Cautious adoption within a growth mindset
So there’s a hunt for the middle ground. Can you start building transformative AI for your business, whilst also working within your current organisational constraints? The answer is yes, when productive organisational AI and high context applications are underpinned by effective human-AI collaboration.
The first step is a mindset shift: it’s no longer about the specific use cases you’re solving, it’s about setting the foundations for collaborative AI practices horizontally across your business, that will allow your organisation to evolve as humans and AI learn together and shift their practices and capabilities.
Starting small, with a lot of human oversight and interaction, can therefore be an important step towards successful AI adoption - as long as it comes with an internal commitment to continuously upskill your humans and organisation to learn rapidly as your AI improves, and to nurture and teach your AI along the way. In time, the skills of your human team, the capabilities of your AI system, and your collective trust in its operation will enable you to extend and optimise your human-AI interactions to make more effective and productive results.
Being able to find doors to new organisational capabilities is inevitable, and a growth mindset will allow you to open them.
Joanna Crown, Product Strategist at Mind Foundry
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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Zoe is a Programme Assistant, supporting techUK's work across Policy, Technology and Innovation.
The team makes the tech case to government and policymakers in Westminster, Whitehall, Brussels and across the UK on the most pressing issues affecting this sector and supports the Technology and Innovation team in the application and expansion of emerging technologies across business, including Geospatial Data, Quantum Computing, AR/VR/XR and Edge technologies.
Before joining techUK, Zoe worked as a Business Development and Membership Coordinator at London First and prior to that Zoe worked in Partnerships at a number of Forex and CFD brokerage firms including Think Markets, ETX Capital and Central Markets.
Zoe has a degree (BA Hons) from the University of Westminster and in her spare time, Zoe enjoys travelling, painting, keeping fit and socialising with friends.
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