How AI is powering the next generation of work
Customer preferences are changing rapidly as consumers demand more engaging, intuitive and accessible digital experiences and services from companies. At the intersection of these dramatic changes is artificial intelligence (AI), powering the biggest transformations in operational and workforce processes since the internet.
In this increasingly challenging business landscape, digitally native companies such as Netflix and Amazon have ripped up rule books and disrupted industries with their agility and innovation. Traditional firms are left struggling to keep up.
Board directors at these incumbent players now accept that transforming their enterprise environments to digital is a must. But executing that is easier said than done when layers and layers of legacy systems exist. Creating digital touchpoints to attract more customers requires a complete revamp of core systems to connect them with newly digitised processes.
Meanwhile, jobs in the knowledge worker environment are transforming too, as single-domain roles become increasingly amenable to automation that might address more than 50 per cent, or sometimes nearly 100 per cent, of their tasks. Workers who become dislocated from their job functions need to be upskilled and refocused towards higher-value assignments that enhance the customer experience.
“AI is at the top of the agenda for many of the large enterprise companies we work with,” says Alex Lyashok, chief executive at automation software firm WorkFusion. “For decades, large organisations have deployed deep, complex technology in buckets and siloes. With AI, they can break down the complexities and introduce self-service automation that allows operational teams with very little technological knowledge to very quickly deploy software robots which learn and repeat what people do, at scale.
“In addition to traditional, large-scale IT projects, we see a whole trend emerging with intelligent automation, where operational teams get a degree of agility and self-service they didn’t have before. AI enables this because they no longer have to code these systems; they simply need to train the AI by feeding it with data.”
Intelligent automation can be used to release 60 to 70 per cent of a company’s manual labour, allowing the business to focus on customer experience and digital growth
AI is predominantly deployed in two ways to support and empower people who have traditionally worked in non-digital or physical labour roles to do their job better, and to work side by side with humans who can contextualise business outcomes and offer additional training or maintenance when required. Although AI will often replace roles done by humans, in turn, it also creates additional work around itself that enhances performance.
Particularly in skilled organisations, there are opportunities for companies to introduce agility into their operations by automating mundane, repeatable tasks with AI. Robotic process automation, for instance, is the agent for many pragmatic applications of AI. On the consumer side, this is visible with the development of self-driving cars. More subtly, in the enterprise, automation offers simple answers to the massive complexity of processing numerous unstructured data.
“In many cases, intelligent automation deployments allow organisations to achieve 30 to 40 per cent cost-savings while improving outcomes by two to three times,” says Mr Lyashok. “There are many scenarios where automation offers companies the chance to grow their business, whether it be better managing revenue cycles, real-time adjustments of retail promotions, or responding to insurance claims or opening bank accounts faster.
“If a bank account can be opened in 15 minutes versus 22 days by automating manual steps and checking things like proof of income or residence in near real time, this technology clearly offers companies the opportunity to grow their business at an unprecedented rate.”
Indeed, applications of AI are most effective in data-intensive and service-focused companies, and banks often provide ideal use-cases. As an industry that is mostly service driven, yet also highly powered by data, applications in financial services offer both low-hanging fruit and areas where tremendous outcomes can be achieved.
Industries that rely less on data and more on assets have until recently offered fewer opportunities for AI-powered outcomes, but this is changing. The manufacturing sector, for example, is traditionally an asset capital-intensive business, but is being transformed by the internet of things, which generates data by attaching sensors to physical objects. Until that sector catches up, service industries like banking and retail continue to lead the way.
However, even within the most suitable environments for deploying AI, challenges exist. Particularly in large companies, it can be difficult to scale AI and systems powered by machine-learning. It might be easy to run several experiments, but when deployed at scale these systems experience pitfalls related to the smart management of data and infrastructure that AI and machine-learning systems rely on.
“While we see some companies trying to do it successfully themselves, we also see many companies struggling,” says Mr Lyashok. “Part of WorkFusion’s promise is a single, unified platform that helps customers start quickly with a small footprint. But we also help them manage the solution at scale when they have hundreds or thousands of software robots running in their organisation across multiple divisions and touchpoints.
“We’re in the very early days of automation-driven transformation, but we are starting to see the promise of dramatic business results. The mundane repeatable work that has had to be done by people, due to the slowness and difficulty of using coded software, is starting to become addressable with AI. In most cases, intelligent automation can be used to release 60 to 70 per cent of a company’s manual labour, allowing the business to focus on customer experience and digital growth. That redirection of labour is the key to growing this trend.”
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