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Innovation in the Finance Function

Compared to other organizational functions such as Sales, Marketing and Supply Chain, the Finance function is often lagging behind when it comes to embracing innovation.

In today’s era of disruption and rapid technological advancement, the only way CFOs and Finance teams can ensure sustained relevance and create value across the enterprise is through innovation and reinventing themselves regularly.

Although companies have been innovating for years, these days the word innovation has become a cliche used to describe new, shiny feature-rich products, services, markets or breakthrough ideas.

According to the late Harvard Business School Professor, Clayton Christensen:

While all those are certainly characteristics of innovations, they are less helpful when trying to understand how companies and nations can organize themselves in ways that can truly foster growth.

Innovation is a change in the process by which an organization transforms labor, capital, materials, or information into products and services of greater value.

Applied to the Finance function, innovation is the process by which CFOs transform the function’s operating model, processes, talent, culture, and systems to eliminate inefficiencies, generate better insights about the business, and improve enterprise value.

Unfortunately, this change does not come about at the mere flip of a switch. In other words, transforming Finance from the traditional scorekeeper role, into a more strategic value enabler is more than an ideas game.

It’s easy to envision the future Finance function, but ideas are only ideas unless they are communicated across the enterprise and effectively executed through a well-crafted plan of action.

Failing is just part of the journey and a step toward figuring things out

New tools, systems and operating models continue to alter the way CFOs and their teams perform their tasks. For instance, advanced data analytical tools are enabling finance teams to collect, aggregate, analyze and generate actionable business performance insights from large data sets.

The challenge: even though CFOs are acutely aware of the need to imbue their departments with digital and analytical capabilities, quite a number are too afraid of making mistakes so they are shelving investments to avoid errors.

In some cases, most of them have also started figuring out what they need to do, but because they lack clarity on how to do so, and have heard stories about failed experiences at other organization, the innovative ideas are shelved too.

A thoughtful strategy is, of course, critical to success in nearly any business endeavor, and data and analytics initiatives are no different. However, just because other companies or your company have tested the idea before and it didn’t work should not blind you to the possibilities of the future.

Failing while moving forward at the same time is better than playing it safe. Rather than embark on a sweeping digital transformation from top down, start with use case pilots that will ultimately build into a tidal wave of change.

Create the environment

The widely held belief that leaders need to be experts and have all the solutions is incorrect. Am I therefore advocating for dumb leaders? No, great leaders understand their strengths and weaknesses.

They understand the difference between knowing and learning, and most importantly, make it a point to surround themselves with individuals and teams whose strengths complement their weaknesses.

In order to drive innovation in the Finance function, CFOs should create an environment that champions ideas, leverages strengths, organizes desired behaviours, rewards intelligent and informed risk-taking and celebrate failures.

Today, companies that are attracting and retaining the best employees are able to do so simply through empowering them to experiment with new ideas and focus more on engaging and meaningful work, in a lower stress environment, with a transparent reward system that makes sense.

Thus, it’s important as a Finance leader to get the message across to your team that failure is part of success in order to free the members from the innovation-limiting shackles of perfection.

Most successful initiatives follow the pattern that looks like this: try, fail, learn; try, fail, learn; try, succeed, repeat. You need to make this okay and let your team know that the real failure is fear of launching an idea until it is perfect.

You can’t read the label when you are sitting inside the jar

“But we have always done it this way” is one of the other obstacles to successful innovation in the Finance function. We get so used to doing our work in particular way that we become blinded to better ways of doing so.

In an era where collaboration between companies and business stakeholders is becoming a common practice, adopting both an inside-out and outside-in approach to innovation is essential.

This requires us to step back from our current standing position in order to connect the dots and gain context. We can achieve this through engaging non-finance teams across the organization, listening to their voice on the changes required and implementing the necessary changes.

Also involves forging connections for knowledge and ideation with experts around the world from outside the organization to create game-changing products and services.

Building an innovative and successful Finance function requires not only a mindset shift, but also execution and continuous iteration of ideas. Never be satisfied with the status quo, always question why you do it that way and figure out ways of how you can do your job better.

A Proactive Approach to RPA Adoption

In order to enable their team members to focus more of their time on higher value, higher satisfaction tasks demanding high levels of flexibility, creativity, critical thinking, problem-solving, leadership and emotional intelligence rather than repetitive tasks, CFOs are increasingly turning to RPA.

Short for Robotic Process Automation, RPA uses software to complete repetitive, structured, rules-based tasks to automate business processes at scale. It starts with simple, local tasks and scales up to enterprise-wide, intelligent automation, driven by machine learning and artificial intelligence.

In the finance function, RPA is being used to automate tasks that are of a repetitive nature and require tedious manual efforts.

Examples of such tasks include bank reconciliation process, sales ordering and invoicing, fixed asset management, financial and external reporting, inventory management, receivables and payables management, financial statement consolidation, tax planning and accounting, and forecasting.

Given the diverse applications of Robotics within finance, before introducing RPA into the finance function, it’s imperative for the CFO and everyone involved in the process to clearly assess and understand the benefits and risks attached.

Not every finance process is a good-fit for RPA

RPA implementation essentially depends on structured data and defined workflows. Thus, for any process to be a viable candidate for automation, the process must involve only structured, digital input and follow a rules-based processing approach.

Correctly identifying the process is therefore a critical step and holds the key to the success of any automation initiative.

However, the current plethora of new digital technologies and applications all promising to disrupt the way finance work is done is pausing a big challenge for finance leaders to separate hype from reality.

As a result, some leaders are deploying robotics into their operations simply due to a speed-to-market goal, resulting in missed benefits and unnecessary costs. When deploying RPA into finance, it’s not about following the herd or what your close competitors are doing.

Rather, you need to perform an objective analysis of your finance processes including an evaluation of which RPA tool or vendor relationship suits your unique needs. There is no one-size-fits-all solution.

Only a handful of processes can be neatly and entirely automated using an RPA tool alone. Therefore, it’s important to start your objective analysis with end to end process thinking because you will need to use multiple tools and techniques to realize the most transformative benefits.

Robotics tools are non-invasive

This means organizations do not need to make existing legacy changes when implementing RPA. The technology can be installed on any desktop or computer in a non-interruptive way with minimal IT involvement or coding abilities.

Because of this faster deployment, CFOs need not make the mistake that IT involvement is not required at all. Though RPA increases efficiency, it also brings with it the concern of system hacking and data breaches.

Also, platform security vulnerabilities, privacy implications and denial of service may yield ramifications that impact the RPA integrity, reliability and downstream business processes.

That is why it’s important to involve the IT from the upfront as the team plays a critical role of ensuring strong systems are in place to raise alerts of data breaches or process errors, and proactively remedy the situations.

Similar to every other system used in the organization, software bots need to be operationally managed and technically maintained.

Risk control and governance

Automation agendas are exciting and groundbreaking, yet intelligent and informed risk decisions surrounding their implementation need to be made to proactively create value and protect the business. As robots extract, aggregate, transform and upload data, risk and control considerations should become key discussion topics.

A robust governance framework should therefore be put in place to support the robotics deployment. The framework should succinctly address areas of concern such as approval of any system changes in case of process which have been automated, scalability, data storage and regulatory compliance.

The RPA tool should be capable of generating a detailed audit trail, highlighting any change or decision taken by the bot.

Just like humans whose performance is assessed, we should also be able to monitor and confirm the accuracy of the tasks being performed by the bots, reliability of the systems and adaptability to process changes.

Robust monitoring and security governance is critical to ensure all the tools and related infrastructure developed in RPA are compliant with IT security policies, regulatory provisions and risk policies across the organization.

CFOs and other leaders thus need to be aware and ready to deal with new complexities that could arise as a result of introducing robotics in finance.

Avoid putting the technology or tool ahead of your people

Many new technology implementation initiatives fail because decision makers leave people out of the equation. People decisions are often an after thought, secondary to technology decisions.

Implementing robotics is about driving operational efficiency, productivity, quality, customer satisfaction and more. These outcomes will not be realized if the key people who are meant to drive the change are not informed from the start.

As a leader, despite not having complete details about the final benefits of the initiative, you still need to communicate across the team why the organization has decided to bring about the change, what the ultimate organizational structure will look like after the change and the change impact on the existing employees.

People drive change and not technology. Reskilling of the employees who will be interacting and interfacing with the bots is therefore necessary for the overall success of the initiative.

In order to gain maximum benefits out of the automation exercise, have a long-term view and consider its strategic relevance to the business.

Engage employees throughout the organization and focus on automating cross-functional end-to-end processes across multiple stages instead of deploying RPA in pockets.

Finance and Enterprise Performance Improvement

In the past several years, the role of finance in business has significantly transformed from being a back office, desk-bound, number cruncher into a more broader, stakeholder-engaging and advisory role responsible for influencing strategic and operational decision making and improving enterprise performance.

Thanks to new innovative digital technologies and business operating models, CFOs and their teams are no longer spending plenty of time working on low-value add and backward looking tasks such as account reconciliations and reporting.

Instead, the explosion in RPA, advanced data analytics and AI technologies is empowering the finance organization to generate, consolidate and analyze data from various sources, and provide actionable insights that add real strategic value to the business.

As a result, commercially-minded and technology savvy finance leaders are cementing their positions as the trusted advisor to the CEO and Board, helping define and execute on the strategic direction of the business.

Today, we live in a world that is dynamically changing and full of surprises and uncertainties.

For example, one day we are dealing with the fallout from geopolitical tensions, the next day it’s natural and man-made disasters disrupting the entire supply chain.

Then a criminal cyber attack bringing the company’s payments and receipts system to a complete halt. This cycle is never ending.

In this environment typified by impermanence – there is no guarantee that existing finance strategies, operating models, systems, processes and talent will continually take your finance organization to new heights.

Although some progress has been made over the years concerning how finance work is done, we are not yet there yet. There is still ample room for improvement.

Lead the charge for change

Familiarity is the antithesis of progress. In order to soar to new heights, finance leaders and their teams ought to be receptive to change.

In spite of the immense potential of new digital technologies to transform the way finance creates and delivers value across the business, compared to other functions, adoption of game changing technologies within finance has been lackluster in the past years.

Does this imply that CFOs should get their hands on each and every latest shiny tool out there? No, technology is simply an enabler to achieve better business decisions.

What is important is for the CFO to understand the art of the possible.

That is the capabilities provided by the new technology to support and adapt the business’ value propositions, processes, pricing and revenue models, strategy execution, and growth.

The CFO should take charge and act as catalyst for change, ensuring that all represented stakeholders have a complete view understanding of what the business is seeking to achieve, what problems you’re trying to solve and what processes you’re looking to make more efficient, and what the investment’s contribution towards the achievement of enterprise objectives is.

Think differently enough to provide an alternative perspective.

Today, finance is no longer just a numbers game. What was typically a role centered on cost, compliance and reporting has now expanded to include strategy, risk decision-making and performance management.

It’s therefore important that finance leaders and their teams invest some time developing an in depth understanding of the business model, strategy, market opportunities and threats, competition dynamics, product portfolio, supplier relationships and customer profiles as this is key to providing a unique perspective that looks across all departments.

Historical performance reporting has been overtaken by trend recognition, forward-looking business and operating plans, real-time metrics, and driver-based rolling forecasts ultimately accelerating the need for the modern finance leader to be more proactive and growth-oriented, rather than being restrictive.

Being a finance leader does not necessarilly mean you should have all the answers to business performance related matters.

What is required is curiosity. Continuously raising key performance questions that ultimately kickstart productive conversations and collaborative efforts across the business.

CFOs with the ability to utilise modern digital offerings to close information gaps across the business, uncover hidden opportunities, accurately predict the future and improve decision making will by far differentiate themselves from their counterparts.

People, People, People

To be successful CFOs must build teams capable and empowered with the right tools and support to deliver a high standard of work across the various finance pillars.

This is essential for freeing up time for the CFO to support the board in driving the business forward.

Whether it’s for themselves or other members of their team, CFOs should continually look at reskilling and upskilling opportunities. The changing dynamics of CFOs’ role mean they need to keep learning to have the business, analytical and data skills both them and their team require.

In addition to reskilling and upskilling team members, CFOs also need to create an environment that encourages testing of new ideas, processes and tools. When teams and individuals are encouraged to explore, great things happen.

The journey to great heights is sometimes fraught with twists and turns and failures. But it is only through ongoing learning from these experiences that we become better. CFOs should never be afraid to test new business models, processes, technologies and skills for fear of failure.

The time to embrace change and transform is now.

Analytics and AI: Humans and Machines are Good at Different Aspects of Prediction

Mostly driven by growth in the IoT, and the widespread use of internet, social media and mobile devices to perform search, send text, email and capture images and videos, the amount of data that we are producing on a daily basis is startling.

Consequently, companies are turning to data analytics and AI technologies to help them make sense of all the data at their disposal, predict the future, and make informed decisions that drive enterprise performance.

Although adoption of analytics and AI systems is increasingly extending in more mission-critical business processes, the implications of these emerging technologies on busines strategy, management, talent and decisions is still poorly understood.

For example, the single most common question in the AI debate is: “Will adoption of AI by businesses lead to massive human job cuts?”

Borrowing lessons from historical technological advances, yes, certain jobs will be lost, and new ones also created. However, machines are not taking over the world, nor are they eliminating the need for humans in the workplace.

Jobs will still be there albeit different from the traditional roles many are accustomed to. The majority of these new roles will require a new range of education, training, and experience.

For instance, nonroutine cognitive tasks demanding high levels of flexibility, creativity, critical thinking, problem-solving, leadership, and emotional intelligence do not yet lend themselves to wholesale automation.

Analytics and AI rely on data to make predictions

As more and better data is continually fed to the machine learning algorithms, the more they learn, and improve at making predictions.

Given these applications search for patterns in data, any inaccuracies or biases in the training data will be reflected in subsequent analyses.

But how much data do you need? The variety, quality and quantity of input, training and feedback data required depends on how accurate the prediction or business outcome must be to be useful.

Training data is used to train the predictive algorithms to predict the target variable, while the feedback data is used to assess and improve the algorithm’s prediction performance.

Undoubtedly, advanced analytics and AI systems are only as good as the data they are trained on. The data used to train these learning algorithms must be free of any noise or hidden biases.

You therefore need to understand how predictive technologies learn from data to perform sophisticated tasks such as customer lifetime value modeling and profitability forecasting.

This helps guide important decisions around the scale, scope and frequency of data acquisition. It’s about striking a balance between the benefits of more data and the cost of acquiring it.

Humans and machines both have shortcomings

In the context of prediction, humans and machines both have recognizable strengths and weaknesses.

Unless we identify and differentiate which tasks humans and machines are best suited for, all analytics and AI investments will come to naught.

For instance, faced with complex information with intricate interactions between different indicators, humans perform worse than machines. Heuristics and biases often get in the way of making accurate predictions.

Instead of accounting for statistical properties and data-driven predictions, more emphasis is often placed on salient information unavailable to prediction systems.

And, most of the times, the information is deceiving, hence the poor performance.

Although machines are better than humans at analyzing huge data sets with complex interactions amidst disparate variables, it’s very crucial to be cognizant of situations where machines are substandard at predicting the future.

The key to unlocking valuable insights from predictive analytics investments involves first and foremost understanding the definite business question that the data needs to answer.

This dictates your analysis plan and the data collection approaches that you will choose. Get the business question wrong, conclusively expect the insights and recommendations from the analysis to also be wrong.

Recall, with plentiful data, machine predictions can work well.

But, in situations where there is limited data to inform future decision making, machine predictions are relatively poor.

To quote Donald Rumsfeld, former US Secretary of Defense:

There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don’t know we don’t know.

Donald Rumsfeld, former US Secretary of Defense

Thus, for known knowns, abundant data is readily available. Accordingly, humans trust machines to do a better than them. Even so, the level of trust changes the moment we start talking about known unknowns and unknown unknowns.

With these situations, machine predictions are relatively poor because we do not have a lot of data to ingest into the prediction model.

Think of infrequent events (known unknowns) that occur once in a while, or something that has never happened before (unknown unknowns).

At least for infrequent events or happenings, humans are occasionally better at predicting with little data.

Generally so because we are good at comparison and applying prudent judgement, examining new situations and identifying other settings that are comparable to be useful in a new setting.

We are naturally wired to remember key pieces of information from the little data available or the limited associations we have had in the past.

Rather than be precise, our prediction comes with a confidence range highlighting its lack of accuracy.

Faced with unknown unknowns, both humans and machines are relatively bad at predicting their arrival.

The simple truth is that we cannot predict truly new events from past data. Look no further than the current Brexit conundrum.

Nobody precisely knew the unintended consequences of the UK leaving the EU. Leavers and Remainers both speculated as to what the benefits and disadvantages of leaving the EU maybe.

Of course, nobody knows what will happen in the future but that doesn’t mean we can’t be prepared, even for the unknown unknowns.

In their book Prediction Machines: The Simple Economics of Artificial Intelligence, Ajay Agrawal, Joshua Gans, and Avi Goldfarb present an additional category of scenarios under which machines also fail to predict precisely – Unknown Knowns.

Per the trio:

Unknown knowns is when an association that appears to be strong in the past is the result of some unknown or unobserved factor that changes over time and makes predictions we thought we could make unreliable.

PREDICTION MACHINES: THE SIMPLE ECONOMICS OF ARTIFICIAL INTELLIGENCE

With unknown knowns, predictive tools appear to provide a very accurate answer, but that answer can be very incorrect, especially if the algorithms have little grasp of the decision process that created the data.

To support their point of view, the authors make reference to pricing and revenue analysis in the hotel industry, although the same viewpoint is applicable elsewhere.

In many industries, higher prices are analogous to higher sales, and vice versa.

For example, in the airline industry, airfares are low outside the peak season, and high during peak seasons (summer and festive) when travel demand is highest.

Presented with this data, and without an understanding that price movements are often a function of demand and supply factors, a simple prediction model might advocate raising different route airfares to sell more empty seats and increase revenues. Evidence of causal inference problems.

But, a human being with a solid understanding of economics concepts will immediately call attention to the fact that increasing airfares is unlikely to increase flight ticket sales.

To the machine, this is an unknown known. But to a human with knowledge of pricing and profitability analysis, this is a known unknown or maybe even a known known provided the human is able to properly model the pricing decision.

Thus, to address such shortcomings, humans should work with machines to identify the right data and appropriate data analysis models that take into consideration seasonality and other demand and supply factors to better predict revenues at different prices.

As data analytics and AI systems become more advanced and spread across industries, and up and down the value chain, companies that will progress further are those that are continually thinking of creative ways for machines to integrate and amplify human capabilities.

In contrast, those companies that are using technology simply to cut costs and displace humans will eventually stop making progress, and cease to exist.

Finance Analytics: Using The Right Data to Generate Actionable Insights

We are living in the information age. Data is everywhere and affecting every aspect of our lives. As the use of data and analytics become pervasive, in future it will be uncommon to find an industry not capitalizing on the benefits they can provide.

CFOs are increasingly leveraging data and analytics tools to optimize operational and financial processes, and drive better decision-making within their organizations. Finance is no longer simply a steward of historical reporting and compliance.

Today, the expectation on finance is to engage with new technologies, collaborate with various business stakeholders to create value and act as a steward of enterprise performance.

That is, deliver the right decision support to the right people at the right time.

Delivering impactful data-driven insights does not happen on its own. You need to have the right tools, processes, talent and culture working well together.

Here are some of the questions you can start asking to kickstart you on this journey:

  • Does your organization have the necessary tools and capabilities to collect and analyze the data (structured and unstructured) and make sense of it all?
  • How robust are your data governance and management processes?
  • How do you define the structure of your finance analytics team? Are you focused heavily on traditional and technical skills or diversity?
  • How are people armed to make better decisions using the data, processes, and analytical methods available?
  • As a finance leader, are you promoting a culture that bases key decisions on gut feel versus data-driven insights?

Both intuitive and analytical decisions are important and can create significant value for the business.

However, I’d recommend against promoting a culture that embraces the randomness of intuitive thinking and avoids analytical thinking completely.

What are you doing with your data?

In a world where data is ubiquitous and plentiful, it can be overwhelming to separate the noise from the important.

According to IBM, 80 percent of today’s data is originating from previously untapped, unstructured information from the web such as imagery, social media channels, news feeds, emails, journals, blogs, images, sounds and videos.

And this unstructured data holds the important insights required for faster, more informed decisions. Take news feeds as an example. Breaking news on economic policy can help you reevaluate production and supply chain decisions, and adjust forecasts accordingly.

But, for fear of missing out, many businesses end up hoarding data with little to zero analysis performed on this data. You don’t need to collect and store each and every single piece of data type out there.

Instead, only data which matters most and is key to unlocking answers to your critical business performance questions. The rest is all noise.

That’s why it’s critical to regularly ask the question, “What are we doing with all the data we have?” This way you would also be able to identify any information gaps that require filling via new data sources.

For analytics to work, people need the foresight to ask the right questions

People play a very critical role in an organizational analytics efforts. Without insightful guidance on the right questions to answer and the right hypotheses to test, analytics efforts are foiled. They cannot direct themselves.

Asking the right questions is key to drawing the right conclusions. Let’s look at the steps involved in making better decisions using data and analytics for one of your product lines. After a strong start, you’re now starting to witness a decline in online revenue but are not sure why.

Before jumping into complex analytics:

  • First, identify the problem that the data need to answer. You already know What happened. However, this is not enough. To address the issue, it’s important that you have a better understanding of the impacted segment and potential reasons for the revenue decline. This is where you start asking questions such as why is revenue declining? When did the revenue decline start? Where did it happen (country, region, city)? What might have caused this (potential drivers)? What actions can we take to address the decline?
  • Then come up with hypotheses of the issue to be addressed. A hypothesis describes a possible solution, such as a driver or a reason behind your business question. Many people make the mistake of simply jumping into data collection and analysis before forming a hypothesis to answer the critical business question. Going back to our revenue decline example, supposedly you have identified several hypotheses and then prioritized two to explain the revenue decline – A slowing down economy and impeding recession causing customers to tighten their purse strings quite a bit, and a new website design with enhanced security features but also additional online check out steps.
  • Based on these two hypotheses you can then identify the exact data that should be collected and analyzed to prove or disprove each hypothesis.

Irrespective of how advanced your analytical tools are, asking the wrong business performance questions results in flawed data being collected, and ultimately poor insights and recommendations. Hence the importance of diversity within the team.

Remember, no data or analytics model is perfect. Finding good data and making sure it’s cleaned and validated is fundamental but the organization also shouldn’t wait for perfection.

It’s easy to get fixated on perfection and fail to see the forest for the trees.

Transforming Your Business in Times of Continuous Change

In times of continuous change, there are both winners and losers. Some company’s grow to become high performing, innovative and competitive enterprises while others develop into fighters, fighting for survival on a daily basis.

Today’s business environment is constantly evolving, with many factors both internal and external to the organization affecting the achievement of its stated objectives including the level of its competitiveness compared to competitors.

Some of the contributing factors include prolonged geopolitical and economic uncertainty, unresolved trade issues, rapid advancement in technological innovation, increased competition from new market participants, and fickle customers with constantly evolving needs.

As a result, the business has to be adaptive if it is to grow and succeed in such a disruptive environment.

When everything is going well, it’s easy to focus more attention on the good stories and less on what could go wrong. The blue overshadows the red, and this is a major problem in some companies.

These companies allow their past success stories such as successful product launches, increased market share, core technologies, and other organizational capabilities to blind their ability to view the future with a different pair of eyes.

Culturally, they are locked into the old way of working, bound by legacy systems and processes. Little time is spent on reviewing and evaluating the existing business model to establish whether it is still viable or not in these disruptive times.

On the contrary, transformational companies are not satisfied with the status quo. They are appreciative of the fact that past success is not a guarantee of future success.

Just because you are doing well today doesn’t not mean you’re going to enjoy everlasting success.

Business history pages are littered with doom and gloom stories about companies that have collapsed due to lack of innovation and unwillingness to evolve with the market.

Examples of such companies include the technology company Xerox, the retailer JC Penney, the social networking company MySpace, the department store Sears, the high tech company Polaroid, the bookstore Borders, and Circuit City the consumer electronics company.

What do all these companies have in common? At some point in time, they were all mighty industry titans, too big to fail and led by great, smart people.

However, in the midst of their successes they failed to adapt to changing customer needs, new technologies, competition and business models.

Even though these companies had built their businesses from the ground to the top of their respective industries, their death knell was the self belief that no other company was capable of doing better than what they were already doing and unseat them at the top.

Unfortunately, because of this fallacious way of thinking and ignorance they all paid a hefty price.

To avoid having your company join this list of colossal business failures:

  • Don’t get comfortable doing the right thing for too long. Continuously look for opportunities ahead and remember that today’s success can obscure tomorrow’s possible failures.
  • Regularly ask yourselves if what you’re doing and how you’re doing it is enough. It’s about making productive use of the resources available to you to improve your company’s performance and competitiveness.
  • Don’t dwell too much on the past. It’s important to know what has happened, but more importantly you need to understand why it has happened and how your company would perform in the future.
  • Commit sufficient time to analyzing new technologies, industry trends and competitors. Reviewing financials provides a rearview mirror of business performance, and you need forward looking indicators to understand your customers, competitors and the competitive status of your business (in terms of products, core technologies, market share, talent, culture)
  • Stay open minded. As highlighted above, when a company has been successful for too long, very little time is spent on thinking through alternative downside scenarios. It’s so easy to focus on the good news, spurn bad news and avoid discussing negatives. Questions such as “Why haven’t we done it before, What if this doesn’t work? What would we do then? What might make this not work?” are reluctantly answered. As a result, what begins as minor issues eventually develop into major issues. Don’t be a victim of own success to such an extent that you become ignorant of change.

Transforming a business into a high performing, innovative and competitive enterprise is a journey characterized by ups and downs. Consider every challenge, every problem and every piece of bad news as an opportunity to learn and improve.

The Accuracy in your Forecast Matters More than the Forecast Itself

One of the roles of the FP&A function is predicting future business performance and help business leaders prepare for an unplanned future through forecasting and decision support.

Although anticipating the future is challenging given today’s fast-changing environment, looking ahead is increasingly essential.

The world is far, far more complex than we think. Unknown unknowns and known unknowns have replaced the routine, the obvious, and the predicted.

Resultantly, many of the assumptions on which important future business decisions are based are easily refuted with the passage of time. For example, one of the most common outcomes of the typical business planning process is a hockey stick forecast.

These forecasts usually show significant business growth and profitability prospects. The last few years of actual results are flat, and then magically shoot up for future years just like the blade of a hockey stick.

It’s a rare experience to come across a forecast that shows a downward spiral of business performance.

Businesses leaders often present a positive outlook of enterprise performance even if the odds of achieving their bold aspirations are slim. This is emblematic of human’s limited ability to accurately predict the future.

Tunnel vision

In his book The Black Swan, Nassim Taleb demonstrates how humans suffer from the delusion of knowing. We underestimate what the future has in store.

In the same manner, we tend to develop a tunnel vision while looking into the future, making it business as usual, when in fact there is nothing usual about the future.

Instead of acknowledging our unknowledge of the future, we continue to project into the future as if we are experts at it, using tools and methods that exclude rare events or outliers.

Although these rare events are most of the time external to the organization, they play a significant role in influencing the operational and strategic performance of the business.

The problem with many business performance forecasts is that they tend to focus on a single point destination or outcome, including a few well-defined sources of uncertainty ( known knowns) at the expense of others that do not easily come to mind.

The goal is not to predict or forecast all improbable events but rather to have an open mind and acknowledge that the likelihood of your actual future being different to your predicted future is considerably high.

Think of new products that failed to hit the mark with customers, projects that experienced cost overruns or took longer to complete, companies that failed to survive their forecast horizon etc.

The list of forecast horror stories is endless. I am sure in 2003 the thought of Lehman Brothers going under five years later was a laughable idea and outside the company’s projections.

Mitigating the tunnel vision

When it comes to forecasting, most of us adopt the inside view to assess the future performance of the business or any other project.

In other words, we tend to plan and forecast based on the information in front of us, neglecting some sources of uncertainty outside the plan itself. Daniel Kahneman, the well-respected psychologist has termed this WYSIATI – What You See Is All There Is.

As a result, we produce plans and forecasts that are unrealistically close to best-case scenarios. However, there are many ways for any plan to fail, and although most of them are too improbable to be anticipated, the likelihood that something will go wrong is high.

The cure for tunnel vision is taking an outside view of that which is being forecasted. Optimism bias often gets into the way of accurate forecasting leading to some of the horror stories mentioned above.

Thus, to avoid falling victim to optimism bias it’s important that you go through all the statistics of projects or initiatives similar to that being forecasted. This will help you identify an appropriate reference class and use the statistics to generate a baseline prediction which acts as an anchor for further adjustments.

Measure your forecasting error

Even though the world is complex and constantly changing, many planners are still adopting a simple view of the world as evidenced by their click and drag forecasts projecting into the long term future. Simply extrapolating projections from one year into the next is a mistake.

The accuracy of forecasts is more important than the forecast themselves. Do you attach possible error rates to your forecasts and measure the actual error rate after the forecasted horizon has passed? As the projected period lengthens, the larger the cumulative forecasting errors.

Despite evidence of enormous forecasting errors in the past, there is an ingrained tendency in us to ignore failure statistics and believe we are suddenly better at predicting the future compared to our uncomprehending predecessors.

Should we therefore discard predicting the future altogether? No, we first need to acknowledge that what we think we know about the future is not all there is. Our comprehension of the future is limited. From there on we can plan while bearing in mind such limitations.

In other words, we should stop overestimating our known knowledge about the future. We may be good at predicting the ordinary, but not the irregular, and this is where we ultimately fail.

Talking about Digital Transformation

These days I’m hearing quite a number of business leaders talk about digitization, going digital or digital transformation.

Regardless of which term you’re most comfortable with, it’s a good sign that leaders are seriously considering taking advantage of the power of modern technologies to transform their businesses.

An investment in IT is no longer considered a cost to the business. Rather, in highly performing organizations, embracing emerging digital technologies is considered an enabler of business performance.

In these organizations, business leaders are cognizant of the conditions in which technology supports the overall business strategy as well as those in which it helps shape the business strategy itself.

It’s no secret that we are in the digital era. Digital technologies are everywhere. Just think of the significant increase in the pervasiveness and the power of digital technologies in new domains such as cloud computing, robotics, wearable devices, 3D printing, drones, machine learning, blockchain, virtual reality etc.

These new technologies are influencing not only the way humans live and work, but also how we learn, play, innovate, transact and govern.

Your existing strategies will not carry you into the future

Although I’m not able to predict with certainty what the business landscape will look like in the next 5 or 10 years, I strongly believe that organizations that will survive and succeed during this period will be defined by their ability to master and take advantage of the power of these emerging technologies to deliver value to customers.

Traditional, tried-and-tested ideas that propelled your business to where you’re today are no guarantee they will continually move you forward and ahead of your industry incumbents in this digital economy.

Let’s look at Amazon as an example. The global ecommerce retailer started off as an online bookstore and along the way embraced emerging technologies to become a leader in cloud computing services, media and artificial intelligence.

The company did not become successful because of its size. Rather, taking advantage of digital technologies helped it to become a powerful global brand.

Just because your current business model is working does not necessarily mean you should allow it to run its course. It’s critical to always question, refine and enhance your competencies and strategy otherwise you risk becoming irrelevant and falling victim of some successful, but now outdated, past practices.

It’s not all about a set of technologies

Simply overlaying technology, however powerful, on your existing business infrastructure does not work. A change in structures, systems, processes, skills and network relationships is paramount.

Neither does digital transformation entail simple automation of traditional, routine and repetitive processes. It’s also about embracing new rules of engagement and continually experimenting with new approaches and adapting them to suit your performance objectives.

For instance, embracing advanced data and analytics tools to learn about and better understand your customers and solve their problems, challenge your current business model and ultimately alter the sources of your revenues and profits.

Shift your focus from thinking about how digital technologies support your current business to exploring how they could also shape your future strategy and business models. In other words, think of innovative ways these digital technologies can help you create and capture business value.

Further, effective decisions on digital transformation are not hype-driven.

Different companies are at different stages of the transformation journey, experimenting with different technologies to create new capabilities, establish new relationships and identify differentiated drivers of value.

Thus, instead of just mimicking what other players in your industry are doing, acknowledge that there is no one-size-fits-all solution.

Consider how the different forms and functionality provided by digital technologies could influence your company’s strategic actions and provide better value for customers of your products and services.

Don’t go it alone

Success in today’s digital economy depends on your ability to build a network of relationships and co-create value with other digital players. As an example, think of how traditional banks are partnering with fintech and regtech entrepreneurs to fundamentally enhance, transform and disrupt their current business models.

These tech entrepreneurs are ambitious, with bold views on how they can disrupt and reorder the traditional banking model. By taking advantage of different digital technologies, they have challenged and disrupted traditional methods of delivering financial services.

So depending on your industry setting, you need to recognize the role of such specialized entrepreneurs in solving your fundamental business problems in new and effective ways.

As digital technologies increasingly pervade the very fabric of our society and business, are you keeping pace with the change or you strongly believe in your established playbooks to continually help you survive and succeed?

Plan Continuation Bias and Decision Making

Businesses of all shapes and sizes often operate in a system with the different parts of the system interacting with one another to produce effects or outcomes that are anticipated or not.

Some systems are linear with easily predictable outcomes. Other systems are more complex, more like a spider web, with many of their parts intricately linked and easily affecting one another.

Understanding the pertinent system dynamics is therefore critical for making better, informed decisions.

Unfortunately, the business environment in which key performance decisions are made on a regular basis is not linear and the outcomes easily predicted.

Businesses inhabit and operate in environments that consist of interdependent networks in relationships which connect with and interact with each other to produce outcomes.

In their book Meltdown: Why Our Systems Fail and What We Can Do About It, Chris Clearfield and András Tilcsik saw that even seemingly unrelated parts in a system are connected indirectly, and some subsystems are linked to many parts of the system. In the unfortunate event of something going wrong, problems show up everywhere, making it difficult to understand what’s happening.

So what do complex systems have to do with plan continuation bias?

We can plan for the future, but we don’t have a crystal ball to predict accurately what will happen next week, month, quarter or year. Based on past experiences, and analysis of data, we might have an idea but that is all there is.

In spite of the past not always being the best predictor of future performance, it’s surprising to see the level of system blindness that decision makers still exhibit.

Let’s look at a decision to enter a new international market as an example. The strategy and factors that contributed to success in one market by all means do not guarantee success in a different market.

Instead of following a step-by-step approach to better understand the system dynamics of the new market, most of the time leaders adopt a copy and paste approach resulting in widespread failures.

Even though there are tell-tell signs of the expansionary move heading sideways, leaders push ahead by implementing the bad strategy. An indication that plan continuation bias is probably the contributing factor.

Simply put, plan continuation bias is the tendency we all human beings have to continue on the path we have already chosen or fallen into, or pursue a decision we have made without rigorously checking whether that is still the best decision or not.

In business decisions, this form of bias is prevalent in strategy implementation, project management, as well as forecasting and planning. We often don’t take the time to review the plan against actual results and change course. We persist even when the original plan no longer makes sense.

It could be that the system in which we based our original plan assumptions has significantly changed, ultimately requiring us to take a step back and reflect to better understand what’s going on and decide how to proceed.

Because we are so much fixated on the end goal which seems achievable, we blindly convince ourselves to push ahead or continue even if the current results are telling us otherwise. We continue to pump resources towards the plan or project, eventually resulting in waste and worse results than before.

Sometimes plan continuation bias is a result of the organizational culture. If the culture is one that doesn’t tolerate “bad news” and suppresses speaking up when circumstances change, then chances are high that everyone will become so obsessed with getting there.

Leaders should therefore encourage speaking up, and employees will not be afraid to speak up for fear of being reprimanded.

Rather than reprimand an employee who has identified flaws in the existing plan or discovers an impending project catastrophe, why not publicly praise them given that such a move sends out a message that leaders are open to receiving feedback.

Considering the complexity of today’s business environment and its tight linkages, it might not be feasible to pause each time a key decision is made. You want to avoid a situation where decisions made are more reactive than proactive.

Instead, find a balance between focusing on the tasks or initiatives to be performed and making sense of what is happening. Avoid getting fixated on one or the other.

Making sense of what is happening gives you a chance to notice unexpected threats and figure out what to do about them before things get completely out of hand.

Avoid making key decisions under time pressure and consider all plan possibilities instead of settling just on one.

Reimagining Risk Management in a Constantly Changing Environment

Deloitte has published an interesting and useful piece, Reimagine Risk: Thrive in your Evolving Ecosystem based on its 2019 survey of risk management. The paper makes some good points, including:

In environments of change, professionals in a range of endeavours often fail to understand risks and their roles in managing them.

A lack of awareness of risks, of people’s roles in controlling them, and of ways to use risk data and new technologies and tools increases the challenges of risk management and undermines the achievement of strategic goals.

Companies that view risk management as among the most important factors for achieving strategic goals tend to achieve higher growth.

Organizations that achieve the greatest gains from risk management show a strong tendency to view the function from a more strategic perspective rather than treating it as a compliance and loss prevention function.

An integrated approach to risk eschews siloed solutions and aims to develop both an enterprise wide view of risk tied to the attainment of key corporate objectives.

In leading organizations, risk management now plays an offensive as well as a defensive role.

Risk management should proactively assist the organization in achieving superior strategy, innovation, and resilience, and not focus solely on avoiding losses and protecting assets.

Risk management’s presence at senior-level meetings increases impact. High-level presence of risk management clearly drives leaders’ confidence in risk data.

Risks are now too dynamic and unpredictable for outdated approaches. Be curious about emerging digital solutions.

Risk management has too much potential as a value-creating function to be viewed as primarily a compliance activity with no direct linkage to the attainment of enterprise objectives.

Deloitte’s 2019 risk management survey

Failure to understand and address enterprise risks holistically is often a result of inadequate processes, skills, systems and tools that effectively support intelligent and informed risk decision-making.

Often, people and organizations are unyielding of change. The natural tendency is to hold on to what we know best and how we have done things in the past.

As a result, instead of continuously scanning the environment for new risks to the business and its strategy we are tempted to believe that the future will turn out to be exactly the same as the past with similar risk exposures.

Effective risk management or decision making is not about building and maintaining a list of risk exposures identified in isolation to the overall strategy and performance of the business.

When organizations approach risk management from a “risks list” perspective, the focus is mostly on what might go wrong as opposed to the risks the organization should take in order to create value and drive business performance.

A business is an ecosystem of connected functions and other stakeholders working together to achieve the organization’s key objectives. The cause-and-effect relationship between the various stakeholders is significant.

Thus, a single decision made by one function or a group of stakeholders can have serious effects on other functions and stakeholders.

Yet despite this direct and indirect relationship between the different business functions and stakeholders, risk management is not always integrated across the enterprise. Risks are managed in silos often culminating in duplication of effort and unproductive use of resources.

Taking a system’s approach to risk thinking and decision-making is key to unlocking value from risk management processes as opposed to embracing a linear thinking approach.

Understand how the various parts of the business interrelate and work together to produce the desired outcomes.

Although compliance and risk management are closely aligned, there is a big difference between the two.

Compliance-related activities ensure the organization is compliant to established rules and regulations, while risk management helps protect organizations from risks that could lead to non-compliance.

Thus, effective risk management is more than a “box ticking” exercise performed solely to satisfy regulators. Though being compliant to prescribed rules and regulations should not be undervalued, in order to inform decision making risk management should be less reactive and more proactive.

In other words, integrate risk into your business and decision support. For example, facilitate periodic risk discussions in order to understand how business functions or units are integrating risk into their business, any opportunities and potential threats to the achievement of their business goals.

To provide effective decision support, the organization must move from a primarily compliance-based and value-protection approach to risk to an approach that also embraces risk-taking for value creation. It’s all about managing the upside and thriving in a constantly changing environment.

Further, in order to optimize results, organizations should avoid paying lip service to risk management and show commitment to intelligent and informed decision making by ensuring that risk management is represented at senior-level meetings to provide business-focused insight.

This does not necessarily mean someone with a CRO designate, as long there is clarity that the individual appointed is responsible for championing the integration of risk into the business, influence strategy and align risk reporting responsibilities.

As risks evolve, the organization must also evolve into an intelligent risk enterprise and ensure adequate processes, people, systems and tools are in place to provide informed decision support to the right people at the right time.

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