Finance teams face a paradox: They feel unprepared to embark on AI projects without a data scientist but often feel unable to make the business case for hiring a data scientist without proven AI use cases.
In reality, many on the finance team have enough experience through their use of programs such as R or Python to make headway with realistic use cases. At one company, for example, two non-data-scientist employees working part time developed an ML pilot that reduced time to accounts receivable settlement by 40% and did it in just six months.
The business doesn’t care about correlation coefficients and “p-values.” Communicate and emphasize the business context: Why does AI augmentation solve a business problem? How does it affect the output? How accurate is the output?
Actual barriers to adoption
Not all AI obstacles are perceived. Here are some of the most common real issues:
Use of poor-quality data
To ensure quality, standardize business-critical data that is common and shared across the organization. At most companies, this will be data that you report externally and data that supports your core, strategic KPIs. This approach helps surface missing, incomplete and duplicative data.
Then create an organized data environment to handle that data effectively, while deploying other data storage solutions to manage nonstandard data. A simple rule to remember: 80% of your analytics will come from 20% of your data. But the remaining 20%, the exploratory, AI-driven analytics, will come from 80% of your data.
Data scientists can introduce cognitive bias into the AI models they create, based on previously used projections or even anecdotal personal experience. If you don’t monitor for and remove such biases, outputs may be skewed — exposing the organization to financial and regulatory risks. Diversity in teams and datasets help to identify biases, validate outputs and train a model objectively, as does a framework for testing for bias in models.
Fear of job losses
One study claims that 43% of workers cite the fear of losing their jobs to AI as their top cause of workplace stress. Do not underestimate the potency of this fear. It leads to adoption resistance, fewer opportunities to deploy technologies and, ultimately, lower business performance.
Identify ways to bypass employee fears in the near term while building understanding and digital dexterity to complement AI with human judgment. Create new roles, such as an FP&A analyst or forecasting analyst with a responsibility for handling anomalies and exceptions arising from AI to provide opportunities for finance team members to learn and improve their skills.
Read more: How AI Will Transform Financial Management Applications