categoryPerformance Improvement

Reimagining Business Processes in an Era of Cognitive Technologies

For years, the focus of many organizations has been on standardizing and automating existing business processes to achieve significant gains in efficiencies.

Within the office of finance, mundane transactional processes such as order-to-cash, procure-to-pay and record-to-report have been the epitome of standardization and automation.

As a result, a number of finance and accounting professionals have had their jobs taken over by automation or machines.

Compared to humans, machines are best at handling repetitive tasks, analyzing enormous data sets, and handling cases with usual modus operandi. On the other hand, humans are best at resolving cases that are complex, requiring application of critical thinking and problem solving capabilities, listening skills, and empathy.

In spite of the job losses of the past as a result of standardization and automation, we are continuing to witness a plethora of new technologies come to the fore and play a vital role in adapting operating models and driving business transformation.

For example, modern technologies such as cloud computing, RPA, advanced analytics, artificial intelligence, and machine learning are transforming the finance function and progressively enabling finance and accounting professionals create and deliver value across the organization.

Sadly, because of these technological advances, many finance people have embraced the false gospel that we are in the era of men-versus-machines.

They are of the incorrect view that machines have arrived to oust humans from the workplace. As a result, they are constantly fighting to protect their turf and are hamstrung by old habits.

Although there are always casualties as a result of implementing new technologies or solutions, the simple truth is that machines are not taking over the world, nor are they removing the need for humans in the workplace.

Instead, these new tools are augmenting human capabilities and collaborating with us to achieve productivity gains that have previously not been possible. Further, the emergence of modern technologies is also creating completely new roles and new opportunities up and down the organization’s value chain.

Given robotics and automation are here to stay, it’s imperative for business leaders to let go of this woefully misguided view of men-versus-machines, and embrace the modern era in which humans and machines collaborate to drive business performance.

Instead of becoming stuck on the old way of doing things, making it difficult to envision things that might be, a completely different mindset is required.

The key to achieving the expected benefits from having humans and machines working closely together is laying the proper foundation and sending out a clear message across the organization to alleviate any fears.

Humans and machines should not be viewed as rivals fighting for each other’s jobs. Rather, they should be considered as close collaborators, each impelling the other to higher levels of performance.

Since machines are better at performing tedious or monotonous tasks, and people rarely find delight in fulfilling these tasks on a daily basis, in order to take advantage of human-machine augmentation, companies should discontinue training their teams to work like robots.

Management and leadership must conduct a resolute review of organizational processes, identify and determine which tasks humans do best, and those that are best suited to machines.

The ultimate goal is to have people focus less on low-visibility tasks and more on higher-value tasks, requiring their judgement, experience and expertise.

In determining which processes to change, there are certain elements to look out for in your business operations. These include repetition, replication, redundancy or a well-outlined process. A significant presence of these elements is a sign that tasks or processes are ripe for change.

But before you reinvent business processes, job descriptions, and business models, you need to make prudent decisions about how best to augment your existing employees. For example, they are needed to design, develop, train, and manage various new applications.

A large part of that effort requires experimentation or trial and error to determine what work should be done by humans, and what work would best be completed by a collaboration between humans and machines.

Replicating the best-in-class process of an industry leader no longer cuts it through. In today’s highly competitive environment, to compete, management and leadership must customize processes to the eccentricities of their own businesses. That’s why experimentation is key.

Additionally, to get buy-in from employees across the company, leadership should foster a culture that encourages experimentation and not discourage mistakes. Provide clear objectives and also clarify to employees that you are investing in new solutions to replace tedious tasks and make their day-to-day work more engaging.

Technology is only an enabler of step-level increases in performance. Don’t rush into human-machine augmentation without initially laying the proper foundation.

First, automate routine work and concentrate on developing the full potential of your employees; then they can begin to focus on human-machine augmentation.

Challenge of Finance Best Practices and What CFOs Should Do About It

The modern CFO is touted as the right hand man of the CEO, providing strategic and operational decision support. No longer is the CFO only responsible for preparing and interpreting financial statements based on historical accounting data, but also for taking a holistic view of business performance and helping the organization move forward.

Thanks to new technologies and improved business operating models, CFOs across industries have been able to transform finance into a value creation function. Further, finance leaders are overwhelmed with finance best practices advice from professional services firms, research analysts and consultants.

Finance leaders are advised to standardize ERP systems, adopt financial planning and analysis technologies and ditch spreadsheets, streamline budgeting processes and implement driver-based rolling forecasts, automate and accelerate financial close and reporting etc.

The list is endless, but does a complete reliance on best practices advice improve finance’s performance and value creation?

Best practices and benchmarks are meant to help business leaders assess the progress of their companies against “leading performers” as opposed to being aspirational ideals to be attained.

The challenge with viewing best practices as standards of excellence is that, their attainment might mistakenly be interpreted by business leaders that no further effort, experimentation or thought is required.

By their nature and application, best practices are transitory. Given today’s business world which is constantly changing – practices, processes, systems and operating models that have enabled us to drive business performance are no guarantee of future success.

CFOs therefore have to realign their functions if they are to keep pace with the demands of an increasingly dynamic marketplace. Always keep in mind that best practices are only beneficial as long as the circumstances in which they are established remain stable.

Unfortunately, volatility and uncertainty are the norm today.

As a finance leader, you should be weary of copying best practices from other businesses with little adaptation otherwise you risk stagnating creativity and commoditizing innovation across the organization.

Rather than continue to depend on the widely accepted best practices, CFOs need to adopt a new mindset, break old habits and promote a continuous improvement culture.

Many at items promising ideas never experience the light of the day because the culture management has created rewards success and punishes failure. Leave some slack for experimentation and encourge constructive failure.

Simply following a complete set of rules or principles will not, on its own, drive finance function effectiveness. Before jumping at the so called best practices, at least ask yourselves:

  • How are we doing what we are doing now?
  • Why are we doing what we are doing this way?
  • What would it look like if we didn’t do things this way?
  • Who expressed this is the best practice?
  • Why is it considered best practice?
  • Does the best practice work for our business?
  • Is the best practice still valid or outdated?
  • Under what circumstances was the best practice established?

Answering the above questions will help you validate the best practice and its potential to boost organizational performance.

Adopt ideas, processes, technologies, and skills that drive change and create value. There is no hard-and-fast playbook. In a culture of innovation, new ideas spring forth from all directions, especially from the unexpected sources.

Just because the organization’s existing structures, systems, skills and processes are driving performance today does not mean they will continue to do so in the future. The past is prologue but not necessarily precedent.

Finance leaders who continue to find comfort in implementing widely accepted best practices to secure competitive advantage or embrace “this is how we have always done it” approach in today’s increasingly uncertain world are not only squandering resources but also destroying value.

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

3 Reasons Why Finance Should Adopt Lean Principles and Tools

There is a lot of discussion on what the Finance function must do in order to become an enabler to the business and remain relevant in today’s increasingly complex business environment.

From business partnering to leveraging new technologies, streamlining processes, retraining and coaching Finance teams to focus on the higher-value-add activities, and dynamically shaping the business model to ensure the company remains market relevant now and in the future – the list is endless.

All these key enablers fall into either one of these categories. People, technology or systems and processes. All three are essential for the successful transformation of the Finance function into a value-add business partner. It is no secret that the ongoing technological transformation is changing the role of Finance for the better.

However, even if an organization manages to acquire and implement state of the art technology, the full potential of that new technology will not be reached as long as the other two vital ingredients are missing from the equation.

Talented, motivated, empowered and committed people are the driving force behind any successful transformation efforts or the adoption of a new business model. At the same time, well-defined and standardized processes are needed to maximize value creation and ensure the business is not wasting time and resources on non value-adding tasks.

As business partners increasingly call on Finance teams to help them make better operational and strategic decisions and ultimately create value, it is imperative that Finance teams get the basics right, reduce the time spent on low value adding activities and channel resources to tasks with the potential of improving productivity gains.

Taking a Lean approach and applying its principles and tools can help CFOs optimize Finance processes, reduce and/or eliminate waste, free up human resources from non-value adding work, and redirect them to tasks that are more engaging and create more value for both internal and external customers.

Lean Makes Areas of Waste Visible

Although the concept of Lean has its roots in the manufacturing sector, its principles are also equally applicable in the service sector with positive results. Lean is a process improvement technique used to create customer value, identify value-added activities, simplify process flows and eliminate waste that does not create value.

Today, a number of Finance functions are burdened with inefficient Procure-to-Pay, Order-to-Cash, Record-to- Report, and FP&A processes thereby hindering their progress of becoming an effective business partner.

By applying Lean thinking to the Finance function, CFOs are able to identify and eliminate sources of waste (Transport, Inventory, Motion, Waiting, Over-production, Over-processing, Defects and Skills) and streamline processes.

Taking Procure-to-Pay as an example and applying Lean thinking to the process, the eight wastes of Lean are:

  1. Transport: This is associated with the movement of people, products and information across the organization. Sometimes the handling or movement is too much resulting in wasted efforts and energy. Applied to Finance, an example would be the excessive number of manual approvals or decision points a supplier invoice has to go through before it gets paid, some of which are unnecessary but a duplication of efforts.
  2. Inventory: This is excess Work in Progress that mounds up between work stations, which is a result of imbalanced demand and supply. An example of this are invoice backlogs pending processing as a result of incomplete or inaccurate supporting documentation or absent invoice payment authorizers.
  3. Motion: Unnecessary movements by people which do not add value. A case in point is movement of people between departments chasing for invoice approvals or missing supplier information.
  4. Waiting: Time wasted while waiting for parts, information, instructions or equipment. Applied to P2P, an example would be the procurement administrator sitting idle because he/she is waiting for invoices from other departments, or multiple invoices piling up in a tray waiting to be approved by a department head delaying the next step in a process.
  5. Over-production: This relates to producing, processing or making more than what is immediately required. An example of over-production within the context of P2P is the creation of vendor reports that are not used or at worst, considered useless by business partners.
  6. Over-processing: Excessive or undue process steps when a simpler approach suffices. For example, repetition of data required on the same form when on-boarding a new supplier to the system. The resulting effect is that more time is unnecessarily spent on vendor creation as opposed to say spend analytics in order to generate useful supplier insights.
  7. Defects: Rework, scrap or incorrect documentation that requires costly remediation. Defects common in a P2P process include mixing up backing documentation of different supplier invoices, incorrect entry of invoice amount resulting in payment errors and wasted efforts trying to recover the overpayment or rectify the underpayment.
  8. Skills: Underutilizing employees’ knowledge, skills and abilities or delegating tasks to employees with limited training. An example is using a highly-skilled and experienced professional to perform procurement tasks that can easily be automated or are considered entry-level requiring minor formal training.

Waste exists everywhere in the organization in various forms. By highlighting areas of inefficiencies CFOs and their teams are able to focus on potential improvement opportunities.

Finance Will Get a Better Understanding of Financial & Operational Processes

Not only will implementing Lean thinking help CFOs highlight areas of waste within the value chain, it also helps them develop a stronger understanding of existing business processes and their cause-and-effect relationships.

So often, companies are fixated on improving processes without first developing a better understanding of where in the process is value created or destroyed. Because processes flow across functions and departments, few people involved have comprehensive view of the end-to-end workflow, and interdependencies are often concealed.

This can result in costly inefficiencies and high error rates. A detailed analysis of the current state often uncovers significant opportunities to improve performance.

The Lean approach helps Finance organizations carry out a detailed analysis and evaluation of all the key activities and decision points involved in a process and know exactly which activity is value-adding and which is non value-adding. This in turn helps to conduct a root cause analysis for the areas of inefficiencies, challenge current ways of thinking, and prioritize improvement opportunities.

Conducting a root cause analysis is key to understanding why there are problems in the first place, so that the improvement process can focus on fixing the root causes and not the symptoms of waste. Because multiple linkages exist between the multiple stakeholders of the business, root cause analysis also helps identify possible primary and secondary causes of problems and how they are all interrelated.

Lean Embeds a Culture of Continuous Improvement in the Finance Function

The Lean approach is a continuous process improvement technique not solely focused on implementing once-off improvement initiatives and tools. Rather, it is more about driving sustainable results by building capabilities and an effective continuous improvement culture.

To achieve its business partnering objectives, the Finance function should develop a culture of learning and improvement so that employees are receptive and supportive of positive changes.

As the role of Finance continues to evolve, it is therefore imperative that Finance teams receive ongoing relevant training and coaching to ensure they are equipped with the relevant skills essential to make the Finance function better.

Transformation is a journey. Do not expect immediate results or perfection. It may take a while for positive results to materialize but what is critical is for the entire team to be positive in its approach and exhibit appropriate behaviours that signify a desire to continuously learn and improve.

One of the challenges often faced by team leaders when presented with new concepts and tools is identifying and selecting the right one. Faced with this confusion, organizations end up trying to implement all the concepts and tools in one go resulting in sub-optimal outcomes. You do not need to apply all Lean principles in your organization as not every tool is relevant to your company.

Instead, evaluate which principle and tool will be most appropriate to your business, and pilot your new methods and approach to a specific process, function or geography before implementing it enterprise-wide.

A majority of waste is left unidentified or dealt with in many Finance organizations because no one is accountable and responsible for process optimization.

By implementing Lean principles and tools, CFOs would be able to establish KPIs that are linked to value creation and promote a culture which holds employees accountable for continuous improvement, removing any inertia which may exist.

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