AI, Machine Learning, and CPM: Experts Answer Your Burning Questions

One is a renowned German Corporate Performance Management (CPM) analyst, the other is the head of a global CPM company. We sat down with two influential figures in the international CPM market and asked them five questions about CPM, technology, and the future.

As new technologies continue to unfold, the future of CPM is ever-changing. Many finance professionals and executives are left wondering: Which technologies are simply a fad and which have the potential to turn the world on its head?

Why not let the experts speculate? Dr. Christian Fuchs (BARC), a leading analyst in Europe, and Alok Ajmera (Prophix Software), a visionary executive in North America, have been around the CPM space for quite some time. Do their different circumstances result in radically different opinions or do they share similar predictions? We asked each of them the same five questions to find out

1. In your opinion, what does the future hold for innovation in corporate planning?

Alok Ajmera: The near future holds dramatic and important change in corporate planning – a fundamentally different approach from the one based on human intuition and gut feel supported by a bit of data. The future of planning stands that model on its head, with rich, data-driven insights at the front end, as the solid foundation for human intuition.

Using traditional methodology, planners today still collect information from around the organization in a time-consuming and error-prone manner. They consolidate the data and might run a few calculations in search of backward-looking insight. But today, with cloud and computer resources underlying advances in the application of AI and machine learning, organizations can move to a more machine-based planning methodology.

In the future, plans will be built by software and technology crunching through billions of pieces of data in real time, putting it together in a meaningful, digestible form. People, rather than spending all their time collecting, entering, homogenizing and scrubbing data, can focus on understanding and integrating the end result, making sure it makes sense in their real world.

Right now, intuition and human labor play too big a role in planning. The future frees people to use their time understanding the meaning of their data.

Dr. Christian Fuchs: I particularly see the use of artificial intelligence (AI) and machine learning (ML) technologies as the next major innovations in this area. This also concurs with what we can deduce from our current market research studies: this year in our BARC Planning Survey, for example, half of all companies surveyed indicated that they want to use predictive technologies (predictive planning) for planning in the medium to long term. In my view, this development is being driven, above all, by the growing amount of data being collected and processed. In addition, both corporate planning and ML have now reached a level of maturity that makes advanced applications of this kind considerably more interesting.

Figure 1: Which of the following does your company do/use with your product for planning and budgeting? (Source: BARC study “The Planning Survey 18”, n=856)

About the interviewees:

Alok Ajmera

Alok is the President and COO of Prophix Software. His mission is to ensure that every customer, prospective client, partner and employee around the world has a memorable and positive experience with Prophix.  Alok joined the company in 2004 and rocketed from Consultant to President. Alok’s energy and enthusiasm inspire all of us to achieve greatness and exceed expectations. Around the office, Alok is the king of casual and boasts a mean t-shirt collection – just ask him.

Dr. Christian Fuchs

Dr. Christian Fuchs is BARC’s Head of Research for BI & Data Management and a senior analyst. He is the main author of BARC’s study “Software Tools for Planning” and other market analyses in the BI and planning space. As a consultant, he supports companies in the software selection process, in the introductory phase and in strategic questions about BI front end portfolios, architecture and usage scenarios.

2. Machine learning and AI are hot topics right now, what are some of the advantages of applying AI & machine learning to CPM?

Dr. Christian Fushcs: AI and ML have the potential to make planning much more effective and efficient. Ideally, these technologies can help to improve the results of planning and the planning processes themselves, in order to achieve more meaningful results more quickly and to reduce the workload of planners. However, this great potential comes with increased expectations.

Many companies are hoping for higher quality and accuracy in planning and forecasting (see Figure 2). However, this overall goal of increasing the effectiveness of planning can only be achieved by considering the relevant cause-and-effect relationships. Shorter-term and quicker forecasts as well as reduced (manual) planning effort are intended to increase the relevance of planning data and thus make planning more efficient. Faster simulations, more accurate forecasts, and increased automation in corporate planning are now within reach of companies through the use of AI, statistical methods, and ML.

An important finding, however, is that companies are not usually focused on completely automating planning. Planners should continue to be involved in the planning process, but be relieved particularly of routine tasks, such as manual activities and forecasts. So, it is not about replacing human planners with machines.

Figure 2: What benefits do you expect or have you achieved by using predictive planning and forecasting in your company? (Source: BARC study “Predictive planning and forecasting takes corporate planning to the next level”, n=308)

Alok Ajmera: A clear advantage is in being able to use data in a more sophisticated way with regards to planning. Data is becoming more accessible across all business units, departments, and systems. It is also becoming less specific to a single organization. Now, much more data is consumable and interactable outside the organization that generates it.

But that also means organizations are overwhelmed with data. There’s so much of it that it’s hard to process into anything useful. That’s the benefit of AI and machine learning as a subset of AI. It gives us the ability to create learning through technologies that are crawling through these vast oceans of internal and external data; to understand relationships and correlations in the data in a non-static way. It enables progressive learning, which means we get more accurate in our forecasting and long-term informatics as more data passes through the system.

This is justifiably an incredibly hot topic in planning. With the cloud’s ability to bring all these technologies and data together, and to apply so much more computing horsepower to our business issues, we are truly at the threshold of a new era for business planning where we’re able to quickly and easily run scenarios. Imagine if you could say “Show me what happens in 12 months if X, Y, and Z happens.”  Right now, it takes tremendous amounts of time and energy to do that, but that’s where these new technologies are rapidly taking us.

Today we already have many powerful tools that can be put together to create value. Within the next five years, there will be more of those tools and they will be more robust, more compelling and more accurate.

3. How can executives get started with AI?

Alok Ajmera: There’s a lot of basic groundwork executives can do. Internal financial executives must first understand there is a role for AI in corporate finance. That can be a challenge. Today if you ask 100 CFOs to define AI, you’ll get 100 definitions.

To be able to do anything with technology, you need to invest in getting the right skills in your organization. The requirements of the finance team in the future will be different from what they are today. Finance needs to invest in skills including data management and data science to be able to exploit these emerging technologies.

At the same time as developing the right skills in finance, you will benefit from partnering with the organizations and companies that are facilitating this future. There are opportunities for finance executives to start working with partners who are thinking about deploying the right technologies.

Finally, as you start down this path, be aware that the infrastructure you put in today must be scalable to meet the requirements of your future.

Dr. Christian Fuchs: The use of AI and ML in the context of corporate planning will by no means be automatic and many hurdles need to be overcome. The biggest challenges today are building and maintaining the required competencies and skills, as well as the availability of monetary and human resources. A large number of companies are therefore planning to develop know-how over the short to medium term as a central starting point. In principle, this can be achieved in two ways: internally, by training own employees, for example, or externally, through the hiring of new employees as well as support from specialized consulting firms. The majority of companies plan to invest in their own expertise and skills, instead of using external help (consulting, for example). This clearly shows the importance of these capabilities for a company’s competitiveness.

4. How should executives prioritize AI & machine learning?

Dr. Christian Fuchs: From BARC’s perspective, the use of AI and ML in corporate planning will become indispensable in the future and, accordingly, should be a high priority for company executives. Due to the maturity of the technology and the simple provision of computing capacities in the cloud, modern planning methods are becoming affordable and relevant for more and more businesses. For this very reason, it is vital to develop know-how at an early stage and gain experience through pilot projects, as well as build trust and promote acceptance. In the view of analysts at BARC, the use of AI and ML has the potential to take corporate planning to the next level, making it one of the key trends for optimizing corporate planning over the next few years.

Alok Ajmera: Machine learning is a subset of AI and it is the piece executives need to prioritize. While AI has many more capabilities than ML, today there are real problems that can be solved with ML today. It is important executives understand what problems can be solved, and then prioritize how they can get value today.

For example, anomaly detection offers a great value opportunity. How do you leverage ML to crawl through transactional data to identify anomalies? That’s a manual task today. Most organizations can only sample their transactional data looking for errors or fraud. But with ML, you can crawl through millions of transactions to spot errors and fraud. Rather than sampling and hoping for the best, you can literally crawl through every transaction to help mitigate expensive problems.

5. Are there any AI or machine learning features you envision building into CPM software or are you more in favor of a best-of-breed approach?

Alok Ajmera: There’s so much capability we can build into CPM software today, which is why it has become a core piece of infrastructure. It contains data that can be exploited for AI-based capabilities. So, absolutely core AI abilities are being built into CPM software. But the real answer is we’ll see a little of both approaches. There are also opportunities to leverage best of breed, especially in the cloud. A benefit of the cloud is you can do best of both.

This is the frontier. It’s incredibly exciting. There’s a lot of promise, great potential. We are starting to see some of that brought to reality but we’re still on the frontier. It’s going to be exciting to see how it will evolve in the next three-to-five years, and it is certainly going to evolve. There’s no turning back.

Dr. Christian Fuchs: Our studies suggest that companies have clear expectations of the CPM solutions they are using: the tools already in use should also provide functionality in the areas of AI and ML in the future in order to improve corporate planning (see Figure 3). The desire is not to use additional software solutions (best-of-breed). This highlights the importance of integrating statistical methods, ML, and AI with traditional planning software functions and is a clear instruction to CPM software providers to make this further development.

Our current observation as a market analyst is that many CPM software providers have made integrating statistical methods, ML, and AI into their own tools a central element of their future roadmap. Firstly, this involves the integration of statistical programming languages such as R or Python and, secondly, the integration of methods from the classic statistics and data mining environment (e.g. regression analyses for the identification of drivers and analysis of cause-effect relationships, anomaly detection techniques, neural networks for forecasts based on historical data).

Figure 3: What software support do you primarily use to implement predictive planning and forecasting in your company today or will do in the future? (Source: BARC study “Predictive planning and forecasting takes corporate planning to the next level”, n=308)


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