The face of corporate performance management is changing as new technologies, specifically in the scope of artificial intelligence and advanced data analytics to mine previously untapped corporate data and take future-oriented decisions, meet growing demands for better, more agile and more change-oriented enterprise-wide performance management approaches.
The ways in which we can move from strategy to strategic actions, while forecasting, monitoring and managing performance need more than what corporate performance management previously has allowed. AI in performance management – a look at the occasion of the AI Summit 2018 in Antwerp.
Among the reasons for different approaches in corporate performance management: change (management) in the scope of digital business transformation, the growing need for holistic, real-time and future-oriented customer and business insights to drive better decisions and the demands of ever more dynamic markets.
Corporate performance management (also known as business performance management or enterprise performance management) obviously isn’t new. Yet, in times where operational excellence is key in all areas of the business (costs, customers, the list goes on) and organizations are changing, there is an evolution in the reality and needs of corporate performance management (CPM) on one hand and the technologies enabling operational excellence and change across the board in a highly data-driven and insights-powered way.
Corporate performance management isn’t an isolated activity or strategy (on the contrary, the holistic view matters a lot) nor just a matter of technologies or solutions. Gaining the strategic, financial, analytical and business intelligence to drive better business decisions is just one part of the equation.
Artificial intelligence in corporate performance management: forward-thinking organizations look at the future
Within the broader scope of strategy and planning in a corporate performance monitoring, reporting and analysis environment the means to leverage data as they have evolved enable more ways to not just learn from the past and present but, most of all, understand and plan for the future using the proper KPIs.
According to Sven Arnauts, who is responsible for Performance Management & Customer Insights at delaware BeLux, allowing AI to get into the core of an organization’s corporate performance management cycle enables to take full advantage of all data available in the organization’s ecosystem in order to take the best possible decisions in previously unseen ways.
Arnauts, one of the speakers at the AI Summit in Antwerp (Belgium) on February 27 2018, sees the impact of AI in performance management as nothing less than huge and transformational.
Gartner defines corporate performance management or CPM as “an umbrella term that describes the methodologies, metrics, processes and systems used to monitor and manage the business performance of an enterprise”. And that is indeed a broad scope as business performance does touch several areas.
Traditionally seen as a subset of business intelligence, corporate performance management of course needs to be supported by analytical applications that provide the functionality to support the processes, methodologies and metrics as Gartner further points out in its definition.
Such applications are typically suites which enable to bridge strategically focused information to operational plans and send aggregated results Gartner says, adding that “these applications are also integrated into many elements of the planning and control cycle, or they address BAM or customer relationship optimization needs”.
For the record: corporate performance management, also known as business performance management or enterprise performance management is not the same as performance management in the sense of HR and employees although here as well AI is increasingly seen as a game-changer (and in the holistic perspective of corporate or enterprise-wide performance management one can indeed say all aspects matter).
So, with corporate performance management we’re in the sphere of business intelligence and, more specifically, as the name indicates in monitoring, forecasting and managing the key performance indicators of the performance of the organization.
It’s, among others, in the scope of monitoring, reporting, analysis, forecasting and the previous mentioned analytical intelligence (as offered by those analytical applications) that artificial intelligence is altering the face of corporate performance management.
Moving beyond the sphere of historical information in corporate performance management
As Sven Arnauts points out, in the past corporate performance management only enabled to predict the future in a limited way and was rather reactive (whereby cloud and Big Data already changed and change, among others, the data provisioning part, for instance in data integration). With artificial intelligence and the detection of patterns, statistical forecasting, the analysis of large volumes of market intelligence (and automatic decision making), decisions can move away from the predominant sphere of historical information and not just enable to gain the insights that previously went unnoticed but, also, to quote Arnauts, be able to look into the future.
Sven Arnauts on the impact of AI on performance management: “AI will have a huge impact on performance management because it will allow us to correlate information and create insights that weren’t possible before, so we can take the best possible decision”.
And thus corporate performance management enables to better leverage all data in the organization’s ecosystem and help the organization reach its strategic objectives in times where change, change management and the redefinition of KPIs (including making sure you track the right KPIs, which is already an art as such) to optimize performance where changes in the organization’s ecosystem in the broadest sense require it and strategies need to be translated in decisions and actions, while monitoring, forecasting and managing those corporate performance parameters which includes correcting decisions and, indeed, automating (part of them).
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