Getting smart about integrating AI technology

As cognitive capabilities move into the mainstream, finance leaders ought to work with their technology counterparts to reexamine their business models.

Enterprise finance leaders should be giving serious thought to how AI could reshape their business models. The technology, which enables computers to be taught to analyse data, identify patterns, and predict outcomes, has evolved from aspirational to mainstream, opening a potential knowledge gap among some finance leaders. In fact, in Deloitte’s North American CFO Signals™ survey for the third quarter of 2020, accelerated business digitisation, including AI, is one of the top strategic shifts CFOs say their companies are making in response to the turbulent economic environment.

What many finance leaders recognise is that AI is more than the next cutting-edge tool. By unleashing its full capabilities in finance and throughout the business, companies can turn it into a source of differentiation that not only boosts productivity but also drives growth. In the finance function, for example, AI is well-suited to replacing repetitive and labour-intensive tasks, executing such transactional work with increased speed and accuracy. Moreover, with its capacity to learn from large datasets, the technology can also boost the accuracy of predictions based on past data, improving budgeting and forecasting and enhancing overall decision-making.

Machine learning (ML), meanwhile, involves allowing the machine to teach itself based on the data it processes. Such an approach is well-suited to the finance function, which routinely relies on large and complex volumes of data, both financial and operational, to fuel its many processes. In Deloitte’s State of AI in the Enterprise survey, 67% of respondents report that they are currently using ML, and almost all (97%) plan to use it in the near future. Among executives whose companies have adopted AI, many envision it transforming not only businesses but also entire industries in the next five years.

Technology That Knows Better

The algorithms underlying these technologies, of course, only know what they absorb from the data – which is based on countless human decisions and a vast array of systems. As such, their knowledge base reflects and projects flaws ranging from inconsistent data quality to potential human bias. Identifying and eliminating such deficiencies requires ongoing maintenance and testing, subjecting the algorithms to quality control so that, for instance, a bank doesn’t unfairly reject the lending application of a credit-worthy individual.

The technology’s capacity for learning depends on not only the volume and quality of data it receives but also on how well it is aligned with the problem that needs solving. To lay down a firm foundation of data for the technology, companies need to assess and mitigate any quality issues involving data, undertaking data-cleansing initiatives to boost integrity and accuracy. Companies that set their expectations high and find the availability of relevant data low may be setting themselves up for disappointment.

To support AI, data governance issues need to be addressed beforehand. Internal wrangling over data, for whatever reason, can result in needless delays. Leaders who remain focused on realising the ultimate benefits of ML sooner rather than later – aware that it can free their teams to spend more time on strategic issues – can see past the initial questions they may have, including:

• How can we fund AI projects? Taking a cross-functional, integrated approach to ML will likely produce the most value for the enterprise, resulting in a shared decision-making tool. But companies can start with point solutions, aiming the technology at a specific problem, rather than investing in a more costly enterprisewide solution. Barriers to entry for AI have dropped significantly as platforms offering ready-made infrastructure and algorithms have become available. If necessary, finance leaders can explore creative funding sources, such as vendor subsidy and ecosystems programs, co-investment strategies, and other models to provide funding for technology innovation within finance. Teams can also explore venture capital models to fund AI use cases and use the outcomes as proof points for further investment.

• Which early use cases are likely to yield a financial return?

The technology’s self-learning capabilities mean it gains value over time. But identifying a specific problem and defining the desired outcome can enable leaders to measure the technology’s impact early on. The greatest opportunity for shortterm ROI may lie with streamlining back-office activities, including transaction processing (particularly in shared services). Decreasing labour-intensive, repetitive tasks will quickly and clearly justify long-term investment in AI technology. In the State of AI survey, respondents cited improved process efficiency as the top benefit AI enabled them to achieve. The best use cases tend to be function-specific but should also offer broad visibility if possible.

• Is it better to build or buy AI? Finance leaders may want to collaborate with their technology counterparts to determine whether to partner with thirdparty AI providers, develop solutions internally, or pursue a hybrid approach. In making this decision, finance and IT should investigate other use cases being implemented in the organisation and leverage homegrown experience and talent to understand what suits the current environment. Organisations frequently mix bought capabilities and homegrown models. When evaluating whether to expand partnerships with cloud vendors and other providers or to foster new ones, consider whether the problem is shared across other areas of the enterprise and ensure alignment of the organisation’s AI ambitions. Is the process the organisation is solving for specific to finance (e.g., revenue forecasting)? Or is it a solution that could benefit other areas as well (e.g., invoice matching)?

• How can a company quickly develop in-house expertise? Assessing off-theshelf solutions and designing realistic use cases requires deep competency in AI. One option is to outsource the technical end to a provider of managed AI services, enabling finance to focus on excavating data out of functional silos. Developing in-house expertise can begin with prioritising AI-related skills in recruitment and training. It may be helpful to stage a hackathon to solve a specific business problem, using it to identify a group of developers who are interested in becoming ML engineers. By making it part of its job to do so, the company can build a knowledgeable team.