The conversation about applying artificial intelligence (AI) in businesses has shifted dramatically
Not that long ago organisations were being urged to ‘just get started’ – to set up a dream team of data scientists and protect them from the rest of the company, letting them get on with developing proofs of concept and allowing them to fail or succeed fast.
Those carefree days are over. The time has come to operationalise AI across organisations at scale and to start deriving clear business benefits, including improved customer service and ROI from time and money already spent. Beyond that, the pandemic has undoubtedly accelerated digitalisation – compressing years into months – and data-first, AI-enabled organisations are the ones that are going to succeed in the future.
In a way, AI needs to become boring. It needs to become an unnoteworthy part of the fabric of any organisation. It should be a foregone conclusion that AI is integrated into a business’ operations. We’re a long way off this. However, much of the extant conversation around operationalising AI has looked at data strategies, partnerships, and ethical frameworks around scaling. All of these are of great importance but they’re missing the most important ingredient to ensuring technology uptake: humans.
The conversations happening around humans and AI typically focus on reskilling and educating people about the synergy between the two – that the latter is an opportunity rather than a threat to their jobs.
Still, we’re missing a critical aspect of the role humans need to play in driving AI across an organisation, and that is bridging the gap between data and business. While it made sense to separate your AI innovation team from the rest of the company in the early days, we now need to fold data back into the main business.
This takes work and effort from both sides. For AI to scale and operationalise across the organisation effectively and quickly while avoiding some of the most common scaling pitfalls, data and business need to become best friends.
Three reasons AI fails to scale and what you can do about it:
1) Data science/AI and the business are disconnected. Companies need to stop thinking about AI and business functions as being distinct. For instance, customer experience and AI should not be treated like separate entities. AI should be a fully paid-up, card-carrying member of the CX team. A disconnect here is a common reason why AI does not successfully get pushed through an organisation. Data scientists need to be embedded in the functional teams – they need to learn how to speak business and understand business strategy. They need to be incentivised in the same way as the business they are serving. For example, they need to be motivated by revenue and project delivery.
2) Building out the AI model is only the first step. When AI is disconnected from the rest of the organisation, it is easy for data scientists to forget that their code is only a small fraction of the overall system. Their model needs to be used in a real-world context by businesspeople, so, 90% of the time, building this model is where money and work get spent. Ultimately, business users also need capabilities such as reporting and auditing, as well as a user-friendly UX and security for the AI model to drive business results. The data scientists that are most successful are the ones who fully engage with the business side of their organisation. They keep asking questions and they think through how their work is being consumed by the business user or customer to genuinely understand the business they are serving.
3) Your legacy systems are holding you back. Say you’ve managed to shepherd an AI-driven CX project through the organisation. Data and business have teamed up to illustrate the real-world business benefits of the project. They have demonstrated how these benefits are aligned with business objectives and not bamboozled business folk by throwing an algorithm up on a screen. The CX team is happy they’ll be able to use AI in a way that works for them, and everyone is happy that adopting it will improve customer experience and drive incremental revenue. But there’s one more bridge to cross – to get the data into the AI model and then the outputs onto the website, siloed legacy systems need to be completely overhauled, costing time and money businesses often don’t have. IA powered digital workers are the solution: use IA to ‘manually’ migrate the data, quickly and accurately.
The world has shifted from a ‘just get started’ mindset to an insistent call to embed AI into the core of organisations. We’ve only just begun to scratch the surface of the capabilities AI has to offer.
About the Author
Eric Tyree is SVP AI and Innovation at Blue Prism. Blue Prism is the global leader in intelligent automation, transforming the way work is done. At Blue Prism, we have users in over 150 countries in more than 1,800 businesses, including Fortune 500 and public sector organizations, that are creating value with new ways of working, unlocking efficiencies, and returning millions of hours of work back into their businesses. Our intelligent digital workforce is smart, secure, scalable and accessible to all; freeing up humans to re-imagine work.
