TagFinance Transformation

A Practical Approach to Using Artificial Intelligence for CFOs – Part IV

Part IV Getting After It: Take the Next Step and Make Your Investment in AI

If you haven’t had a chance to read Part I – Leveraging AI in the CFO SuitePart II – The Benefits of AI and What You Will Need to Make It a Success and Part III Where to Invest in AI, How to Measure the Financial Impact and Select Projects yet, please do so before continuing on.

There are four major investments you’ll need to make to use AI successfully in your business.

1. Develop an AI Strategy: This investment is about learning how to apply AI to your activities and selecting your best course of action. Consider using outside experts to help augment your thinking in this area if you are just starting your AI journey.

  • The first step of strategy development includes learning about AI, determining how it will be applied to the CFO responsibility areas, assessing the value of AI application for those areas.
  • The second step is to gauge the data needs (availability, accuracy, volume) and the cost of “creating” data that can generate the output required. “Quality, effectiveness, efficiency and insight are the four key pillars that really make this valuable stuff…” according to Nick Frost, KPMG Audit Technical Lead Partner.¹ Watch for these characteristics in your data. If they aren’t present, be wary of how you use your final product.
  • Using the value noted in a. above and the cost determined in b. an AI Strategy targeting the areas where AI will have the most impact can be constructed.
  • Skill/System assessment and timeline. Determine where growth in skills and systems are needed. The scope of these needs will also help create the resources required and a timeline. From a risk perspective, consider starting small (high expected return, low initial investment) and allow for greater investment as success is realized.
  • Include a change management plan to assist employees and other stakeholders in understanding the strategy and the impact it will have on them.

2. AI Software Selection: The investment in software will include the cost of the software and the expenses of the internal and external team members working on the process.

  • Use your Strategic Plan to target AI vendors that serve the areas highest on your list.
  1. On premise or cloud solution
  2. Data storage costs
  3. Integration with current systems.
  • If AI is new to you stay small and focused on high return, bite-sized efforts you can learn from.
  • Use your network to validate claims made from vendors in terms of system results, implementation timeline and cost.
  • This investment will include the direct payments for the software and internal costs for the selection team to do their work.
  • Our “AI Capital Investment Analysis” tool will help you summarize and communicate your planned investment in AI.

3. Implementation to Operation: It is important to focus the cultural change required during this stage to create an environment that craves the new learning AI brings to the table. The combination of our team’s desire to use AI wisely and a sound AI system add up to success. If either is missing, there is a good chance your implementation will fail.

Here are the implementation steps:

  • Research and mitigate the risks related to the implementation and data management.
  • Train and hire the skills to manage the system and leverage the new capabilities created by the AI.
  • Identify and manage the risks that are likely to occur because of the implementation.
  • Procure and implement the technology that fits your strategy.
  • Monitor and adjust the AI inputs and outputs to create optimum value for the AI stakeholders.

4. Ongoing AI growth: Your AI strategy document is the road map that will be used to plan AI follow up. It is a living document that requires updating.

  • Manage the ongoing operating costs of the AI system
  • Implement AI applications per the Strategic Plan
  • Change the priorities in the Strategic Plan as necessary
  • Consider new applications (see 1 above)
  • Assess current operating AI systems for optimization annually.

Artificial Intelligence holds great promise for financial professionals. It’s a key ingredient to enhancing the business partnering momentum established in the new millennium. Creating our AI Strategy, securing the skills to choose, implementing and operating AI systems, and growing these capabilities are new challenges demanding the attention of the CFO. Developing more efficient and “smart” transaction systems while improving decision support activities are huge value drivers for businesses today. Our ability to harness the power of AI to these means will be a significant measure of our success.

We’d love to hear about your AI experience (email us at info@erpminsights.com)!

¹Eleanor O’Neill, “How is the accountancy and finance world using artificial intelligence?” CA Today, July 31, 2016

A Practical Approach to Using Artificial Intelligence for CFOs – Part III

Part III Where to Invest in AI, How to Measure the Financial Impact and Select Projects

If you haven’t had a chance to read Part I – Leveraging AI in the CFO Suite and Part II – The Benefits of AI and What You Will Need to Make It a Success yet, please do so before continuing on.

Where the CFO can invest in AI to create a positive impact.

Now that we know what AI is and its benefits for finance, how can a CFO develop a plan around how to apply it in their business? To borrow a phrase from Stephen Covey, Begin with the end in mind. Visualize where you want to be and work backwards, considering what is preventing you from realizing your future today. This step will help prevent you from building AI around current systems and processes that are encumbering your digital transformation.

The next step in identifying where to invest in AI is to summarize the outputs your team creates for the company’s stakeholders. Define output as anything your team delivers to a stakeholder that they use. Examples of outputs include; invoices to customers, financial reports to management, pay checks/stubs to employees, borrowing base to the bank, work papers to the auditor, KPIs to the Board of Directors, credit information requests from vendors, accounts receivable aging report to the credit department, new project investment analysis for the CEO, productivity reports for the COO, etc.

​To be highly effective the implementation of AI is a multi-discipline exercise that will require resources from many parts of the business. A good example of this can be illustrated when using AI to assist in auto invoicing and payment applications. The sales department, manufacturing and shipping departments will provide data that allows these two functions to operate autonomously. The data from these departments will be incorporated into algorithms that function to determine how much, when and to whom to send an invoice; and, how to apply payments when the bank reports them as received.

​ Below are some important criteria to think about when selecting where to apply AI:

​ 1. Stakeholder focused; Serve your most important constituents first – Customers, Vendors, Employees (including management) and Directors

​ 2. Determine where AI has the largest potential impact

  • ​ Where improvements speed, accuracy and/or volume have significant impact
  • ​ Revenue generation
  • ​ Cost savings

 3. Understand the complexity of AI application.

  • ​ Data requirements
  • ​ System requirements
  • ​ Process requirements

Measuring the (financial) benefits of an investment in AI for a business

​Just like any other business case development, it is important to measure the benefits of investing in AI technology. These benefits are either tangible or intangible. Tangible benefits are those that can easily be quantified, you can put a value against. On the other hand, intangible benefits are difficult to quantify, but expected to occur as a result of the investment.

​So, is one set of benefits better than the other? Our answer is no. Both tangible and intangible benefits are important. But only tangible benefits can be used to calculate the financial return of AI investment. This can be looked at from the perspective of additional savings or income generated as a result of AI.

​However, the challenge for many CFOs when it comes to implementing new technological solutions for their companies is clearly defining how success will be measured and quantifying the ROI.

​Since the adoption of AI technologies is not yet widespread but still in the pilot phase we suggest CFOs take a simplified approach to calculating the value of AI projects and follow these steps:

1. Identify a specific problem. Although AI is promising to be a huge game changer for your business, AI is not the answer to all your business problems. Don’t fall into the trap of investing in AI for the sake of investing, or worse, succumb to “herd mentality”. To successfully benefit from AI, first identify a specific problem that may be solved though AI. The AI Identification Worksheet discussed earlier can help you here.

​2. Define the outcomes. What will success look like in your company? What is the result you are targeting, and can this be defined in monetary or percentage values?

​3. Measure the results. After clearly defining the outcomes, the next step is estimating the performance of AI against your baseline measurements or outcomes. The spread between your expected performance and the baseline provides with the expected benefits of the proposed AI solution. Put in place a system to measure the actual results

4. Identify and calculate the costs (investment) incurred in delivering the results. Here you need to consider things like initial investment costs, ongoing support costs and the impact on cash flow.

​5. Calculate the return on investment (ROI). This final step involves calculating the ratio of money gained (or lost) relative to the amount of money invested (the total cost). If the projected ROI meets your hurdle rate, you’ll move ahead with the project. Set up to schedule to review the actual performance vs. the expected results to develop the feedback loop to improve your investment model.

Below is an example of calculating the ROI using the steps above:

1. Identifying a specific problem: ABC Company P2P process is highly manual and incurs annual labor costs of $300,000. During a cost and profitability analysis exercise, Brenda, the company’s CFO established that due to high error rates and rework as a result of these manual processes, the company is incurring additional overhead costs of $100,000 per annum. She remembered that from one of the CFO conferences she attended, the speaker spoke about AI and the technologies potential to drive process efficiencies. She proposes to the Board that the company invests in AI, specifically for improving P2P and test the concept.

​2. Defining the outcomes: After a series of meetings with various functional leaders, stakeholders and consideration of various factors, Brenda presents to the board her findings. By piloting AI for the P2P function, the company stands to achieve annual labor cost reduction of 10% and overhead reduction of 15%. The Board approves the project, expecting savings of $45,000 excluding the potential benefits from higher accuracy and improved vendor relations.

After conducting a thorough market analysis of the suitable AI solutions available, with the support of the Board, Brenda engaged the services of FinancePro, a cloud-based software provider specializing in AI software for the CFO office.

​3. Measuring the results: After conducting a thorough market analysis of the suitable AI solutions available, with the support of the Board, Brenda engaged the services of FinancePro, a cloud-based software provider specializing in AI software for the CFO office. It is now 12 months since the pilot project went live and the Board wants to know if the company managed to achieve the 10% labor cost and 15% overhead cost reduction targets. Brenda compares last years’ costs against current years’ costs and her targets of 10% and 15% cost reductions have been met. In year 2, the company estimated benefits of $60,000.

4. Identify and calculate the costs (investment) incurred in delivering the results: Although the cost reduction targets have been met, Brenda believes that these figures evaluated in isolation are not helpful for evaluating the overall investment. She therefore decides to identify and calculate the total cost ABC Company incurred in meeting these targets. She takes into account all initial costs such as license fees of the new AI software, implementation costs and employee training costs for the full amount of $30,000. She also calculates ongoing costs such as maintenance and support, communications and data storage costs which amounted to $20,000.

​5. Calculate the return on investment (ROI): This is calculated as follows

​ • She uses a cash on cash analysis to determine the 2-year ROI:

​In this example, ROI is calculated by taking the total financial benefits ($105,000) subtracting the total financial costs ($70,000), dividing by the total financial costs then multiplying by 100 to arrive at the ROI (50%). This calculation is over a 2-year period but can be applied on an annual basis as well. We have developed a simple model to help you summarize and compare your AI projects. Use it to:

  1. ​ Analyze and select AI projects,
  2. ​ Get your executive team familiar with the financial benefits of AI and,
  3. ​ As a performance measurement and improvement tool once an AI project has started.

Click here to get your AI ROI Calculation model.

Next Up: Part IV Getting After It: Take the Next Step and Make Your Investment in AI

A Practical Approach to Using Artificial Intelligence for CFOs – Part II

Part II The Benefits of AI and What You Will Need to Make It a Success

If you haven’t had a chance to read Part I – Leveraging AI in the CFO Suite yet, please do so before continuing on.

Potential Benefits of AI for Finance

The potential applications of AI are varied and being considered in virtually all sectors and industries. Today, companies are using AI algorithms to predict start up success, block spam messages and comments on social media, and boost webpage ranking. Lawyers are leveraging the same AI software to speed up legal research, and Financial Advisors have recently been piloting AI to monitor huge data sets and provide data-driven decisions. This handful of uses points to an exciting AI-driven future.

The Finance function is no exception. According to one of the CEO survey findings on the performance of their CFOs published by KPMG, although CEOs are increasingly expecting their CFOs to play an important strategic business partnering role, the gap between CEOs expectations and the actual performance of CFOs is still huge.

CEOs believe, instead of helping them understand and address the business challenges they are facing, CFOs are spending significant time on financial reporting as well as compliance and regulatory issues. In the eyes of the CEOs these activities are more rear-view focused and do little to help them prepare for an uncertain and volatile future.

AI has the potential of helping CFOs close this gap. AI technology can help CFOs automate end-to-end financial processes, make them much more efficient than previously, and spend reduced amounts of time and resources on repetitive and laborious tasks. This in turn helps them spend more time on strategic issues partnering with the business.

Examples where Finance can benefit from AI include:

1. Invoice Processing: Employees spend a significant amount of time on Procure to Pay (P2P). Manually entering invoice data resulting in high and costly error rates. Using an AI powered system, CFOs can significantly simplify and automate these manual processes. Because of the many data points on an invoice, an AI system can “learn” the relationship between the individual elements of an invoice. In the future, based on previous experience and data, the system autonomously processes the invoices and allocates them to the appropriate general ledger accounts. If there are any misallocations which are corrected by an expert, the system learns and improves from such interventions.

2. Bank Reconciliations: The reconciliation of account data and receipts as well as the allocation of banking information can be carried out faster and reliably using AI. The software retrieves both sources of data directly. Independently-learning algorithms match the document information with the transactions in the company’s bank accounts. This renders the bank reconciliation process much more reliable, transparent, and most importantly it can be carried out in real-time. This in turn helps CFOs to evaluate in real time the liquidity position of the business.

3. Budgeting and Forecasting: By using AI, CFOs will be able to improve the accuracy of their company’s forecasts, speed up and automate closing the books with lower compliance and auditing costs. Traditionally, CFOs have relied on financial data housed in ERP systems to drive budgeting and planning processes. This reliance on internal data alone to drive key performance decisions excluded important external data. Thanks to today’s advancements in computing processing powers and speed, CFOs are now able to make use of data sources once deemed inaccessible. AI algorithms are able to aggregate data from multiple data sources, analyze this data very quickly (in real time), identify patterns, calculate the probability and impact on business performance and feed that information into the forecasting model.

New skills, expertise and knowledge required to deliver and operate AI systems

As with any other new technology or system, delivering and operating AI systems requires new skills, expertise and knowledge. New technologies are enabling CFOs to do more with less and create added values for the organizations. Finance and Technology used to be miles apart. Not anymore, the two are now joined at the hip. The CFO has to be tech-savvy and possess a stronger understanding of the new technologies in the market, how easily they can be integrated into the company’s overall IT infrastructure, and their potential to drive business performance.

In addition to having knowledge of the technology landscape, these skills are also a prerequisite:

1. Quantitative: To successfully support effective decision making, CFOs have to make sure that the advice given to business partners is evidence-based and not mere guess work. Having strong analytical capabilities is therefore critical. As data volumes and types continue to grow at exponential rates, making sense of it means the traditional skill set of the Office of Finance has to change. New data analysis capabilities are required; developers, data scientists, data engineers, data architects, data visualization experts, behavioral scientists and cyber security experts working together with traditionally trained Finance professionals.

2. Deep Process Knowledge: Tasks where the desired outcome can easily be described and there is limited need for human judgement are generally easier to automate. Not all Finance processes are candidates for automation. Some processes are higher-value adding requiring judgement or creativity, and are therefore not easily automated. The CFO must be able to differentiate between transaction processing and value-add processes and select the suitable ones for applying AI technology.

3. People Management: Leadership, communication and change management abilities are all essential. Whenever there is talk of AI, the conversation ends up being a debate of Machines versus Humans. There is a common belief that AI has evolved to replace workers. We believe this theory is far-fetched. Implementing AI is also about people and not software alone. Automation is a huge opportunity but it’s also about “augmented intelligence”. In other words, combining human intelligence with technology-enabled insights to make smarter choices in the face of uncertainty and complexity.

The CFO must be able to address any employee fears that might arise, clearly communicate the rationale for adopting AI, and motivate and inspire their team to embrace the change. People are often the differentiator between success and failure. If they don’t buy into the vision of what the company is trying to achieve, the initiative is bound to fail. Also, emotions rise high during such initiatives because of conflicting priorities and as such, it is important for the CFO to manage and resolve such conflicts.

A recent article published by McKinsey in the Harvard Business Review¹ highlights another skill that is important as organizations start working with these new technologies – Data Translator. According to the authors of the article, translators are neither data architects nor data engineers. They’re not even necessarily dedicated analytics professionals, and they don’t possess deep technical expertise in programming or modeling.

Translators draw on their domain knowledge to help business leaders identify and prioritize their business problems, based on which will create the highest value when solved. They then tap into their working knowledge of AI and analytics to convey these business goals to the data professionals who will create the models and solutions. Finally, translators ensure that the solution produces insights that the business can interpret and execute on, and, ultimately, communicates the benefits of these insights to business users to drive adoption.

Thus, as the role of CFOs increasingly evolves into that of a strategic advisor or internal consultant, it is imperative that CFOs develop and improve on these data translation skills. In today’s data-driven era, where data science skills are in high demand, not all of us are cut to be data scientists.

¹Nicolaus Henke, Jordan Levine and Paul McInerney, “You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role?” Harvard Business Review, February 5, 2018

Next Up: Part III Where to Invest in AI, How to Measure the Financial Impact and Select Projects

Automation: The Key to Reducing Your Financial Close & Reporting Period

Ask any finance person what is it that they like less about their job, chances are that they will tell you it is closing the books at period-end. Why is this the case? For many finance functions, the financial close period (month-end, quarter-end and year-end) is one of their most stressful seasons.

Emotions and tempers fly high. Team members put in long hours just to balance the books.

I have heard cases of teams knocking off after midnight for a couple of days and always ask myself why. Expressions such as “I am working late because it’s month-end, quarter-end or year-end” are starting to sound off like a cliché.

Are Finance teams swamped with work to such an extent that they are unable to complete their tasks within the normal working hours of the day or month? Maybe it’s tradition that must be preserved at all cost?

I am not trying to make myself a saint here. I too am a culprit. I have put in long hours at month-end or any other period-end close more than I can remember, and when I reflect back, most of the tasks that I put in the long hours for were mundane and not worthy of their time allocation.

In my experience, there are two main reasons why Finance teams find themselves working round-the-clock at the end of each financial close period – 1) Complacency and 2) Procrastination.

Complacency is the enemy of progress.

It is a common practice for humans to remain satisfied with the way things are and resist any meaningful change. Fear of the unknown makes us skeptical to embrace change.

What we forget is that not all change is bad. As much there is change associated with negative consequences, there is also a type of change associated with positive results.

Although technology has evolved over the years, it seems as if this new dawn is yet to reach the Finance function. In a number of companies, Finance has been very slow to adopt new enabling technologies in order to drive financial process efficiencies.

Take financial reporting as an example. Company stakeholders are increasingly demanding CFOs to deliver better and easy to understand performance information, more timely and credibly. Despite this need, Finance teams are still heavily reliant on manual extraction processes, and delivering stale information.

Finance people are spending a significant amount of time trying to manually consolidate company financial performance results.

So often Head Office teams patiently wait for subsidiary companies to send through their management packs for Group consolidation purposes and when they do reach the Head Office, they have not balanced and contain errors.

The Head Office team sends the management pack back to the subsidiary for rectification. If the latter is unable to rectify the errors, it automatically becomes the Head Office’s problem. This back-and-forth process results in unnecessary effort and time-wasting rework.

Love it or hate it, Excel is here to stay. Nonetheless, I believe there is a place for Excel and not all financial functions are easily performed with Excel. It is high time CFOs acknowledge the limitations of manual extraction processes and leverage financial consolidation software.

Automating consolidation functions results in reduced cycle times of discrete tasks, more accurate results at a lower cost, and also provides management with improved visibility and control, allowing for improved decision-making.

We Will Correct the Errors Later

Many at times procrastination has resulted in Finance teams working longer hours than they should. Accounting errors identified early in the month are not resolved immediately. Rather, the slogan is “We will correct the errors at month-end”.

When month-end comes, because of other issues at hand, these errors are easily forgotten and left to accumulate. What could have been resolved in one month ends up not getting resolved for another six months or so.

An example of this would be a clearing account that is not reconciled on a monthly basis. The balance is allowed to grow month-on-month until there is no more control and visibility of the account.

When the year-end comes, the external auditors want a precise explanation of the item movements within the account but nobody has a clue of when did the problem start and what exactly went wrong.

The auditors make note of a possible adjustment to the financial statements unless clarity is given.

Because the company is not prepared to write-off the adjustment to the income statement, the CFO reassigns resources to work on the clearing account, resulting in excessive time being spent trying to make sense of the historical items and movements.

It is therefore imperative that as soon as accounting errors are identified, these should be rectified in the same period identified.

Fast-Changing Environment Accelerating the Need for a Faster Close

In today’s fast-changing environment, companies need the ability to make better decisions and at a moment’s notice. Changes are happening must faster than before and because of this quickening pace of business, CFOs will forever be under immense pressure to shorten the time-frame of closing the company’s books and finalizing all financial reporting needs.

Regulators, rating agencies, investors and other stakeholders are increasingly scrutinizing company financial results, at the same time asking to receive this information sooner than later. This leaves little room for financial close and reporting processes that require an overabundance of patience or are lacking in clarity and precision.

Despite dramatic improvements in financial technology over the years, why then are CFOs reluctant to upgrade their company’s current systems? Are they dealing with competing priorities? Is it because they view investment in new financial software as a cost rather than an enabler of business performance?

In order to become effective business partners, the office of Finance needs to abandon its “wait-and-see” approach, become more proactive and invest in tools that help accelerate the financial close process and advance the CFO’s role, making it more and more strategic.

Automation is not meant to oust employees. Instead, automating routine financial processes shortens the period-end close and enables Finance to get involved in high-level tasks such as interpreting and analyzing data, generating insights, managing emerging risks and driving strategy execution.

For example, by investing in e-procurement software instead of relying on a highly manual, paper-driven P2P process, CFOs are able to monitor the company’s cash flow position in real time and take advantage of early-discount opportunities.

As the pace of business change continues to accelerate, Finance has to keep up, take a real-time overview of the entire close process, and address the areas that need improvement.

By incorporating technologies, streamlining processes, and reassigning employees, Finance can build a better-documented, more efficient and accurate financial close.

FP&A Leaders are Failing to Deliver Higher Strategic Value

Last month, Prophix Software released its findings from the survey, Defining the Evolution of FP&A: Benchmarks, Challenges & Opportunities. The survey which was carried out between Q2 and Q3 of 2017 received feedback from over 300 FP&A leaders from all companies of all sizes across the globe.

The survey was conducted to establish the maturity of analytics solutions across the globe, the effectiveness and efficiency of FP&A leaders’ planning processes, how companies are leveraging technology to improve FP&A processes, and to gauge internal perceptions relative to the value of FP&A.

Although certain parts of the results are encouraging, FP&A teams significantly need to improve on their role of delivering higher strategic value to their companies.

In today’s fast moving markets, characterized by intense competitive pressures, shorter product life spans, complex business environment, increased volatility, and heightened uncertainty, it is imperative that a company’s FP&A people, processes, and systems are highly mature, effective, efficient, and leverage the necessary enabling technologies.

While going through the survey findings, a couple of statistics captured my attention.

  • 55% of the survey respondents reported being in a basic or developing state of the FP&A analytical maturity.

Comment: Essentially, more than half of the survey respondents have no formal FP&A process, no established analytical and reporting matrixes, no planning model and tools as well as BI tools. If ever they are there, these are all basic, highly manual and descriptive in nature.

This basic level of analytical maturity brings to light the fact that a culture of continuous innovation and improvement is non-existent in Finance. As the custodians of data within the organization, one would assume Finance to be at the forefront of analytical maturity, but this is not the case.

Surprisingly, 50% of respondents are mindful of technology but seldom upgrade. This statistic alone is concerning. Why are FP&A leaders reluctant to change? Are they happy with the status quo? Are they lacking the resources necessary to transform? Is it ignorance in its purest form?

Transitioning from a basic to an advanced or leading FP&A maturity level, above all, requires a cultural shift all the way from the top to the bottom of the organization. There has to be a desire to change for the better, a willingness to commit and continuous learning attitude.

Attending industry conferences, seminars or webinars and reading thought leadership resources as well as listening to their podcasts can help FP&A leaders keep abreast of trends and benchmark their company’s performance against peers.

  • Only 12% of the survey respondents have access to the right data, at the right time, to inform strategic decisions at their companies.

Comment: Having access to the right data, at the right time is key to informing strategic decisions and driving business performance. Unfortunately, 88% of respondents do not have this access.

This means the majority of critical decisions in companies across the globe are based on gut-feel and not evidence-based.

In today’s Big Data age, it’s startling to know that companies are not leveraging advanced analytical tools to aggregate and analyze data from disparate sources and generate key nuggets on customer experiences, competitor behaviour, trends, emerging risks and opportunities.

Moving forward, FP&A leaders need to make use of data management framework that facilitates the creation of a central data repository and ensures everyone in the company has access to relevant data whenever they need it.

This can only happen if the company makes a key decision to advance its analytical maturity model. Highly manual processes make it difficult to update FP&A models in real time, thereby inhibiting quick decision-making processes.

With the recent advancements in technology, the costs of implementing new software to enhance FP&A processes have significantly reduced. Companies should therefore not use cost as an excuse for low adoption rates.

  • Only 10% of companies reported that they find it somewhat easy to perform scenario analysis.

Comment: In today’s VUCA business environment, companies need to be proactive, develop superior forward-looking capabilities and be ready to deal with any disruptive forces threatening their survival and existence.

They need to become more flexible, adaptable and be increasingly aware of the impact on business performance of changes in the environment. This will help them take corrective actions more quickly and efficiently.

Unfortunately, 90% of the surveyed companies are finding it difficult to perform scenario analysis. As already reported, over half of them are still reliant on basic and highly manual processes which in turn makes it difficult to consider all possible scenarios in their FP&A models.

For the 10% who are finding it somewhat easy to perform scenario analysis, what are they doing right? They have managed to figure out that FP&A is a collaborative process extending beyond the walls of Finance. Also, rather than use fixed time-specific budgets, they are using driver-based rolling forecasts to see beyond 12 months.

Instead of sitting on their desks all day long, these professionals engage the wider business community, learn about the external and internal factors influencing strategy execution, how they are all interrelated and their material impact. They are then able to leverage new technologies, calculate any probabilities and update their FP&A models in real time.

Having a deeper understanding of the key drivers of business performance helps FP&A leaders define relevant scenarios that describe a range of future operating environments, and generate forecasts reflecting the changes in scenarios which in turn helps decision makers to adjust their strategic plans, targets and action plans.

  •  55% of respondents conveyed that their companies don’t think that FP&A delivers high strategic value.

Comment: FP&A professionals are a critical part of the Finance team. They help operational and strategic decision makers make informed decisions by providing them with reliable, timely and fact-based recommendations.

They bridge the gap between financial and operational plans and ensure decision makers receive the right information, at the right time. The reason why 55% of the surveyed companies are not happy with value-delivery of FP&A is because FP&A teams are spending the majority of their time on low-hanging fruits.

According to the survey findings, 51% of the time spent on FP&A is allocated to data collection or validation. Thus, instead of spending more time on generating insights and influencing business decisions, FP&A teams are busy reporting on the past and justifying reported results.

This quite understandable given the high levels of technological immaturity in many companies. By leveraging advanced analytical tools, FP&A will be able to reduce time spent on data collection, reconciliation and cleansing and free up resources that can be used to deliver higher strategic value.

Instead of generating reports and analysis that they feel are valuable, FP&A teams should regularly liaise with business teams and establish their reporting and information requirements. This will help ensure that resources and time are constantly not being wasted on non-value adding activities.

For the majority of companies that are still basic or developing their analytical maturity, I recommend that they take a candid review of their current FP&A people, processes and systems, and make an honest conclusion of whether they are happy with where they are at the present moment or need to make significant changes.

Everything might look good today, but always remember that Good is the Enemy of Great!

© 2019 ERPM Insights

Theme by Anders NorénUp ↑