By itself, data isn’t useful for business; the application of analytics is necessary to transform data into actionable information. Data analysis of one sort or another has long been a core competence of finance departments, applied to balance sheets, income statements or cash flow statements. Today, however, Finance must go beyond these basics by expanding the scope of the data being examined to include all financial and operational information that can yield actionable insights. Analysis thus should include, for example, data from the systems that manage sales operations, human resources and field service and that data must be available to all departments and applications that need it.
Topics: Customer Experience, Human Capital Management, Marketing, Voice of the Customer, business intelligence, embedded analytics, Learning Management, Analytics, Collaboration, Data Governance, Data Lake, Data Preparation, Information Management, Internet of Things, Contact Center, Data, Product Information Management, Sales Performance Management, Workforce Management, Financial Performance Management, Price and Revenue Management, Digital Technology, Digital Marketing, Digital Commerce, ERP and Continuous Accounting, blockchain, natural language processing, robotic finance, Predictive Planning, candidate engagement, Intelligent CX, Conversational Computing, Continuous Payroll, AI and Machine Learning, revenue and lease accounting, collaborative computing, mobile computing, subscription management, agent management, extended reality
For several years, I’ve commented on a range of emerging technologies that will have a profound impact on white-collar work in the coming decade. I’ve now coined the term “Robotic finance” to describe this emerging focus, which includes four key areas of technology: Artificial intelligence (AI) and machine learning (ML), robotic process automation (RPA), bots utilizing natural language processing, and blockchain distributed ledger technology (DLT), each of which I describe below. Robotic finance will have a disproportionate impact on finance and accounting departments: I estimate that adoption of these technologies potentially will eliminate one-third of the accounting department’s workload within a decade.
The application of artificial intelligence (AI) and machine learning (ML) to business computing will have a profound impact on white collar professions. This is especially true in heavily rules-based functions such as accounting. Companies recognize the transformational potential of AI and ML, but the progression and pace of the adoption of these technologies is unclear. Some applications of AI and ML are already in use but others are a decade or more away from replacing human tasks.