Gartner published a report yesterday*, revealing that half of current finance artificial intelligence (AI) deployments will be either delayed or cancelled by 2024, while the use of business process outsourcing (BPO) for AI will rise from six percent to 40 percent within two years. The report outlines how, despite finance departments making reasonable progress in laying the groundwork for AI, challenges arise when they attempt to scale up solutions that can manage the complexities of function-wide use.
It also explains that digital automation in finance often fails to meet the expected benefits outlined in the related business cases. Much of this is down to a lack of truly functional automated processes; a significant proportion of automation work fails and is rerouted to a human for manual input. Without making changes to this idea of “fake automation”, Gartner predicts that finance departments will struggle to scale automated solutions, such as AI, effectively across the function.
Ash Finnegan, digital transformation officer at Conga comments on how finance leaders can adapt their approach to revenue operations transformation by assessing their current digital maturity:
“If companies think automation will solve all their problems, they are approaching transformation all wrong. AI is only as good as the data provided and if there are bad processes in place, automation will only accelerate this issue.
“Finance departments, just like any other organisation, need to fully optimise their revenue operations process, before considering any new technology. It is crucial that leaders and IT teams establish the company’s digital maturity – where they are and where they need to get to – and review the operational model throughout every stage of their digital transformation journey, from foundation to full system integration. Teams may have stumbled across a number of bottlenecks or unnecessary processes, and this will have affected overall workflow.
“By taking a step back and reviewing the operation model, leaders will have a clearer picture of the current state of their business, and what the next stage of their revenue operations transformation journey should be. After identifying any operational issues and reviewing legacy systems, leaders can then establish clear objectives, such as unifying systems of record and streamlining data flows between teams. Only then can leaders consider incorporating or scaling AI and streamlining the processes that matter to help them to achieve these overarching goals.
“Furthermore, they need to consider each phase of their operations – front to back office. By streamlining their operational model and unifying systems of record, companies will have far greater insight into data streams, and this will empower AI, taking their business to a true state of intelligence.
“It is vital that at every stage of the digital transformation journey, leaders fine-tune the basic workflow to ensure any inefficiencies are removed.”