Balancing creation of short-term value and early learnings with building long-term capabilities and vision is key, says Madhavi Mantha, head of industry products – banking, at software provider Element AI.
Corporate finance departments and treasuries faces multiple challenges that technologies such as artificial intelligence (AI) can help overcome. Most financial directors and CFOs understand this, but are sometimes unaware of what it takes to maximise outcomes.
Of course, the CFO needs to deliver cost-reductions as well as new capabilities that redirect or reshape the business in the longer term, which is never easy. From more complex regulatory requirements, shortages of talent and talk of recession, to greater security risks – the unprecedented scale and velocity of the challenges is increasing pressure to achieve digital transformation with maximum effectiveness and speed. The danger is of rushing into a forest of technologies without any clear sense of direction or destination.
In March, Grant Thornton and CFO Research found that CFOs recognise the necessity for digital transformation and are ready to make substantial investments. The survey of senior finance executives at companies with revenue of more than $100 million found AI was implemented by 25% in 2019, up from 7% in 2018. Three-in-ten (30%) have already put money into machine learning. A quarter (25%) expect to invest in AI and machine learning, respectively, in the next 12 months.
Yet the survey also found that more than 80% of CFOs believe technology investment has to deliver defined return on investment. The consultants at Grant Thornton recommend CFOs gain an appetite for experimentation, which is a key step in an organisation’s AI journey.
Implemented with care, artificial intelligence offers genuinely new capabilities in data analysis and decision-making. Finance departments and treasuries adopting AI can bring their organisations clear competitive advantages, as these solutions can significantly cut down on redundant manual work while providing performance insights and streamlining processes. There is a potential to leverage AI to transform how an organisation handles its capital and cash flow requirements, performs audit and compliance activities, and other shared-service tasks.
Yet, there is no straight line between the technical capabilities of machine learning and business applications that create value over the longer term. To create lasting value with AI, an organisation must position the technology to learn from and strengthen its business DNA – the unique way of creating value that’s encoded in its data, processes, expertise, and other elements.
Although a world away from corporate finance, it’s worth considering how eBay rapidly delivered benefits by adapting its AI application performance through the company’s unique data and context. An MIT study found that when eBay launched automatic translation on its e-commerce platform in 2014, trade between English-speaking and Spanish-speaking countries increased by 17.5 per cent. By achieving a good fit between AI capability and business application, eBay opened up new markets for buyers and sellers around the world, benefiting the company and its users alike.
The essence of strategy for all companies, regardless of sector, is competitive differentiation: to create value that no one else can. This is as true in corporate finance as it is for online marketplaces like eBay.
To unlock the greatest value from AI, corporate finance needs to understand how AI technologies match its business needs, how to customise them to the unique strengths of the organisation, how internal processes need to change to enable success, and how business strategy may be reshaped as a result. This, in effect, is intelligent AI adoption – adapting AI to the business and adapting the business to AI.
In finance, of course, there isn’t “one right answer” when an organisation starts setting priorities for AI implementation. Context is everything. A great place for finance departments to start is in the development of an execution-focused roadmap for injecting AI into key business processes and functional areas.
To be successful, this must engage stakeholders across the business, technology, operations, risk and compliance and so on. In other words, this isn’t purely a technology or CIO-only discussion. And, like any other successful transformation effort, the right tone and sense of urgency need to be set from executive leadership.
The range of potential applications is broad. At a high level, any application worth pursuing must deliver business value across at least one of the following dimensions: revenue-generation, efficiency gains, improved client experience, and risk management (including cybersecurity and financial crime more broadly).
A typical roadmap effort is likely to begin with an opportunity-generation exercise which could reveal scores of potential applications/use-cases. What then follows is an essential education exercise to segregate “real” AI use-cases from those that could be addressed with other solutions.
These other technologies can include digitalisation, standard automation, business intelligence/reporting, data visualisation techniques and descriptive or predictive analytics. Not every business problem requires an AI-based solution. But for the “right” business problem, an AI-based solution can unlock value that other technologies simply cannot.
The business value and feasibility of the options must be rigorously assessed, considering data-readiness and AI complexity. The organisation must move on to examine viability, too – where humans fit in, whether the company is ready for what is proposed and who or what the enablers or barriers to adoption are.
AI suppliers should bring many resources to the table, ranging from industry experts to applied research scientists with deep expertise in specific domains, to experts in human-machine collaboration and design. This is the right approach to develop execution-focused roadmaps that focus on how AI will augment the ability of employees and customers.
Specialists in AI will also identify the key internal and external data sources that enable value for specific business applications.
The range of data that AI can ingest and act on is very broad, and can include both structured data sources such as client or third-party transaction data or market data, as well as semi-structured or unstructured sources such as call notes, e-mails, financial reports, research, newsfeeds and macro-economic data.
More fully leveraging these data sources can generate new insights that allow employees to make faster and better informed decisions by, for example, automating company and market analysis that is a key input to many corporate finance activities.
There are also significant efficiency gains to be had by automating what is still a largely manual task of extracting data and knowledge from semi-structured and unstructured data sources. This can significantly streamline key middle- and back-office processes such as customer onboarding and reconciliations that drive high costs. This frees qualified staff from the mundane, repetitive tasks that have always been a feature of corporate finance and allows them to focus their talents on adding real value.
Adapting corporate finance operations is also about understanding what needs to change in a business to support its AI ambitions, from updating internal processes to reassessing the business strategy itself. That’s a key element in adapting a business to AI.
To create lasting value, corporate finance must prepare the business to use AI capabilities by: creating a shared understanding across the organisation of what AI can and cannot do, enhancing data, redesigning processes, developing and organising people, and revisiting business strategy in light of all new options.
The art of adopting AI is to balance the creation of short-term value and early learnings with building long-term capabilities and vision. AI can be a key lever in helping corporate finance and treasury departments assume their rightful position as major revenue-enhancers in their own right, with substantial benefits to overall profitability. The key is to get started on defining an AI journey that is well aligned to your company’s DNA and strategic objectives.