The starting point of an era is never precise and rarely conforms to neat calendar delineations. For example, the start of the 20th century is associated with the outbreak of war in 1914. So I expect that decades from now, the consensus will hold that what became known as the 21st century began in the year 2020, with the pandemic serving as a catalyst that accelerated already existing trends and forced changes to prevailing norms and practices. This and other disruptive events that have followed are reverberating through economic and social networks and will ultimately result in some new equilibrium, but the ructions on the way there will be sharp and ever-present. Large-scale disruptions in most aspects of doing business have forced change on organizations. In this climate, the financial planning and analysis group can play a far more important role by using technology to enhance organizational agility and improve performance.
IBM Planning Analytics with Watson is a comprehensive, cloud-based business planning application that supports what Ventana Research calls integrated business planning. We coined this term in 2007 to describe a high-participation approach to business planning that integrates strategy, operations and finance. Our Next Generation Business Planning Benchmark Research demonstrated the value of IBP: Organizations that link planning processes get better results. Sixty-six percent of organizations that have an integrated method say it works well or very well, compared to only 25% that have little or no connection between plans.
Topics: Predictive Analytics, Office of Finance, business intelligence, embedded analytics, Business Planning, Financial Performance Management, Watson, Digital transformation, AI and Machine Learning, profitability management
A predictive finance department is one that can command technology to be more forward-looking and action-oriented while still fulfilling its core role of handling the financial elements of its organization including accounting, treasury and corporate finance. Beyond just automating rote tasks, technology also facilitates a shift toward becoming a predictive finance organization. Greater amounts of information, now available in near real time, and the increasing use of artificial intelligence (AI), enable more immediate analyses and assessments of possible courses of action, providing executives and managers the ability to better anticipate change and the agility to adapt quickly to unexpected circumstances.
Kinaxis recently announced it has acquired a Netherlands-based company, MPO, a cloud-based software offering that orchestrates multiparty supply chain execution. The combination is designed to enable Kinaxis to extend its concurrent planning platform to handle core elements of supply chain execution. Kinaxis acquired all the shares of MPO for approximately US$45 million, with some of the final consideration dependent on performance. MPO will continue to operate as a standalone business, but will be increasingly integrated into Kinaxis’ operations worldwide.
Organizations do not live in a vacuum and things happening outside their walls have a direct impact on how they perform. So, it is essential for them to incorporate external data in their forecasting, planning and budgeting, especially for predictive analytics and machine learning (ML) to support artificial intelligence (AI). I use the term external data to include any information about the world outside an organization (including economic and market statistics), competitors (such as pricing and locations), and customers. Until recently, it was adequate for organizations to regard external data is a “nice to have” item, but that is no longer the case. External data is necessary for many functions, including useful and accurate competitive intelligence used by sales and marketing groups. It is also essential for the effective applications of AI using ML for business-focused planning and budgeting and predictive analytics.
Anaplan offers a cloud-based business planning platform that incorporates a modeling and calculation engine. The tool makes it relatively easy to add or expand the scope of plans that can be connected and monitored on a single platform. This Integrated Business Planning (IBP) approach enables organizations to use the software for financial planning or budgeting, sales, supply chain, workforce, marketing and IT planning. These are the types of plans in which companies often need to create models that incorporate their specific requirements, business systems and strategy. I expect that by 2025, one-fourth of financial planning and analysis (FP&A) groups will have implemented IBP.
OneStream offers a platform designed to serve the needs of accounting and financial planning and analysis organizations. The software handles financial close and consolidation, planning and budgeting, analysis and reporting. For me, the most significant announcement at the company’s recent user conference was the unveiling of its Sensible ML (Machine Learning) offering, which is in limited general release. I’ve commented on the importance of artificial intelligence in business applications, and Sensible ML is a promising and important step in that direction.
A few years ago – somewhat tongue in cheek – I began using the term “data pantry” to describe a type of data store that’s part of a business application platform, created for a specific set of users and use cases. It’s a data pantry because, unlike a general-purpose data store such as a data warehouse, everything the user needs is readily available and easily accessible, with labels that are immediately recognized and understood.
Artificial intelligence using machine learning has passed through the bright, shiny object stage and software vendors are well into the process of making the concept a reality in their offerings. Ventana Research defines AI as the use of technology to process information in much the way humans do, including improving accuracy in recommendations, actions and conclusions as more data is received. I like the alternative term “augmented intelligence” because it emphasizes that these systems enhance – rather than replace – the capabilities of the humans employing them, especially through improved decision-making and eliminating the need to perform repetitive work.
The use of artificial intelligence (AI) using machine learning (ML) will be the single most important trend in business software this decade because it can multiply the investment value of such applications and provide vendors an important source of differentiation to achieve a competitive advantage in what are today very mature software categories. I assert that by 2025, almost all Office of Finance software vendors will have incorporated some AI capabilities to reduce workloads and improve performance. However, software vendors will be challenged to apply innovations in this area quickly while ensuring that the AI capabilities function well enough in the real world to foster rapid adoption while avoiding user frustration. The failures of the Apple Newton and Microsoft’s Clippy office assistant stand out as examples of too-ambitious-too-soon attempts at infusing intelligent automation.