TagFinance Transformation

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.

Human or Machine Intelligence? Augmentation Key to Better Forecasting

Forecasting is an invaluable process for any business. A forecast can play a significant role in driving company success or failure. For example, high forecast accuracy helps a business anticipate changes in the market, identify growth opportunities, reduce risks, analyze  root causes of performance and proactively respond.

On the other hand, forecasts that are poorly designed, based on weak assumptions often result in unintended consequences.

Preparing highly accurate and reliable forecasts to support decision making is one of the major challenges faced by performance management teams across sectors and industries.

Traditionally, business performance forecasters have relied on past performance to predict future performance. In a perfect, static world the formula works well. However, as we all know the world is not static. The only thing that is constant is change.

Volatility, uncertainty, complexity and ambiguity are at an increasingly alarming level. Further, new technologies are transforming how we do our work now and in the future.

A number of manual processes have successfully been automated. Where businesses have previously relied on financial data alone to make strategic decisions, the dawn of the digital age has brought new meaning to non-financial data.

The new world of algorithm-powered machines

The traditional approach of forecasting is highly manual and time-consuming. People spend a significant amount of time gathering, compiling and manipulating data in spreadsheets.

Most of the time, the data used to predict the future and create forecasts is historical financial data residing in the company’s ERP systems.

Unfortunately, in today’s rapidly changing world the future doesn’t sufficiently resemble the past.

As the new digital era continue to unfold, more and more data (financial, operational and external) will increasingly become available to support business forecasting.

Given that the traditional approach of forecasting leverages data in structured format to prepare forecasts, with more and more unstructured data available, CFOs and their teams have to rethink the old school forecasting process.

In order to increase the agility of the business to proactively respond to competitor activities, customer, market and industry changes that threaten the achievement of set objectives, or trends that present specific opportunities, the organization should consider all types of data at its disposal and discern what is important and what is not for business performance forecasting purposes.

Artificial Intelligence, machine learning, deep learning and natural language processing are disrupting traditional business operating models and companies are increasingly tapping into these new technologies to drive forecasting processes. These highly powered machines use statistical algorithms and modern computing capabilities to collect, store, and analyze large quantities of data and predict what is likely to happen in the future.

The algorithms are fed with warehouses of historical company and market data and taught to mimic human intelligence. Overtime, through learning, forecasting accuracy is improved.

In addition, NLP algorithms are able to go through a myriad of documents including articles, social posts and other correspondences written in plain text and extract insights that can be injected into the forecasting model.

Humans and machines augment each other

It is no secret that machines have a superior advantage over humans when it comes to collecting, storing and analyzing large data sets in real-time. But does this imply that decision makers should rely exclusively on machine intelligence to drive business decision making? The simple answer is no.

When it comes to applying critical thinking and judgement, human beings are much better than machines. Humans are able to evaluate and translate the machine’s conclusions into decisions and actions. Take for instance the forecasting models that are used to predict the future, the best source of information for these models are the domain experts for whom the models are designed.

The domain experts have a better understanding of the models, what assumptions to base the models on including the ability to uncover flaws that others may miss. Software developers, data scientists, AI experts and automation engineers, among others rely on expert judgement of domain experts to hard-code data features in databases that are used to train predictive algorithms.

In one of my articles, Applying Design Thinking to Finance, I highlighted how companies are heavily dependent on analytical thinking in order to drive business performance.

The solution is not to embrace the randomness of intuitive thinking and avoid analytical thinking completely. The solution lies in the organization embracing both approaches, turn away from the false certainty of the past, and instead peer into a mystery to ask what could be

The fact that the past is not a reliable predictor of the future does not necessarily mean that it is not important. History has been known to provide major lessons to us. In the same manner, human judgement can be used to determine which historical data is suitably representative of the future to be included in forecasting decisions.

When data is abundant and the relevant aspects of the business world aren’t fast-changing, it’s appropriate to lean on statistical methods to prepare forecasts. However, even after the forecasting model has been designed and adopted, human judgement is still required to evaluate the suitability of the model’s prediction under different scenarios.

Important to note is that predictive models do no more than combine the pieces of information fed to them. These machines are good at identifying trends and imitating human reasoning. If bad or erroneous data, or good but biased data is presented to the algorithms, issues can arise.

Setting aside human biases

People make decisions based on logic, emotion and instincts. One of the challenges of preparing forecasts in a complex and constantly changing world is setting aside human biases.

Subconsciously, human beings have a tendency to base judgement and forecasts on systematically biased mental heuristics rather than vigilant assessment of facts. System 1 Thinking.

According to Daniel Kahneman in his book Thinking Fast and Slow:

  • System 1 is an automatic, fast and often unconscious way of thinking. It is autonomous and efficient, requiring little energy or attention, but is prone to biases and systematic errors.
  • System 2 is an effortful, slow and controlled way of thinking. It requires energy and can’t work without attention but, once engaged, it has the ability to filter the instincts of System 1.

Personal experiences built overtime make us overgeneralize facts and jump to conclusions. Instead of focusing on both existing and absent evidence, we act as if the evidence before us is the only information relevant to the decision at hand.

As a result, the risks of options to which we are emotionally inclined are downplayed, and our abilities and the accuracy of our judgement are also overestimated.

Further, a focus on the limited available evidence causes us to create coherent stories about business performance including causal relationships that are non-existent. We are quick to ignore or fail to seek evidence that runs contrary to the coherent story we have already created in our mind.

Such actions do not result only in overconfident judgement but also cause us to be overly optimistic and create plans and forecasts that are unrealistically close to best-case scenarios.

By addressing own cognitive biases and enabling collaboration between humans and machines, business forecasters will be empowered to create forecasts that enable faster and more confident decision making.

Machines can only assist and not displace the typically human ability to make critical judgement under uncertainty.

Applying Design Thinking to Finance

I recently finished reading The Design of Business: Why Design Thinking is the Next Competitive Advantage by Roger L. Martin. It’s well worth reading. Even though the book was published almost a decade ago, the ideas and principles espoused by the author are still relevant and applicable in today’s business environment.

Design thinking is a customer centric process used by designers for creative problem solving. The process utilizes elements from the designer’s toolkit like empathy, intuition, systemic reasoning and experimentation to arrive at innovative solutions that benefit the end user or the customer.

Finance is increasingly being called upon to provide effective business decision support. For many traditionally trained accounting and finance professionals, the request is a big ask.

Understanding and influencing the entire value creation cycle of the business is not something that they are accustomed to. Instead, many accounting and finance teams are comfortable working in financial reporting roles.

However, as businesses increasingly leverage new technologies to automate rules-based, transactional and repetitive tasks for a fraction of the full time employee salary, it’s only a matter of time before some finance team members become an endangered species.

Part of the problem is the fact that during our training, the majority of the courses we undertake make us believe that our core role is to deliver compliance-focused tasks.

Think of Financial Reporting, Taxation, Auditing and Assurance, Business Law, and Financial Accounting modules. All are compliance-focused. At the beginning of the learning, the content of each module is the basics and progresses into advanced topics towards the end.

Ultimately, we develop a box-ticking mindset. Having such a mindset will not help differentiate the business from its competitors and create a competitive advantage. I’m not discounting the importance of financial reporting or any other compliance tasks.

They too are important. But, innovative and successful companies do not become so simply by heavily investing in compliance activities.

Innovation and efficiency do not have to be at odds

In The Design of Business, Roger L. Martin highlights that one of the reasons many businesses face a struggle to innovate and create value for their stakeholders is because of an increased reliance on analytical thinking versus intuitive thinking.

The former involves senior management attempt to base strategy on rigorous, quantitative analysis (optimally backed by decision support software). The later is centered on the primacy of creativity and innovation, the art of knowing without reasoning. Roger Martin does not advocate the adoption of one approach over the other. Instead, he advises businesses to seek a balance or reconciliation of the two.

Traditionally, finance transformation initiatives are driven by cost reduction strategies. The focus is on squeezing out as much fat as possible and achieve efficiency. Take adoption of new finance software as an example. Rather than view the adoption as an opportunity to relieve finance teams of rudimentary tasks and focus on initiatives that require critical thinking, CFOs view this as an opportunity get rid of employees and cut costs.

If a business is heavily dependent on analytical thinking, especially where performance and rewards are budget and or forecast driven, maintaining the status quo often prevails. The organization finds itself operating as it always has and is reluctant to design and redesign itself dynamically over time.

When faced with a decision about investing in a new product, market or something new and promising, but not in the current budget, the answer is always no. Many at times the argument is that if something cannot be planned and budgeted for in advance, it is not worth pursuing. This ultimately breeds conformity and stifles innovation as resources are allocated to business units based on past performance.

Finding a balance between exploration and exploitation

Balancing innovation and efficiency demands the organization’s resource allocation not to be based entirely on past performance. Rather, a portion of the resources should be distributed based on the unproved ideas and projects each business unit presents for the coming year.

One of the reasons why a number of promising projects fail to see the light of the day is because management have created a culture that first seeks a predictable outcome before paving way for the project. They seek reliability, which is in direct contrast to a designer’s mindset.

A designer seeks validity over reliability with the goal of producing outcomes that meet a desired objective. The end result is shown to be correct through the passage of time.

The current business environment is awash with mysteries, which take an infinite variety of forms. For example, we don’t know how our product and market segments will continue to perform in future. We are not certain which technologies will have an immediate impact on our business. Or we might explore the mysteries of competition and geopolitical tension.

Data on past performance might help us extrapolate future performance but the future is no guarantee.

Given that the future is a mystery, the business should embrace a new way of thinking that provides a simplified understanding of the mystery and in turn help devise an explicit, step-by-step procedure for solving the problem.

An organization may decide to focus on exploration, which involves a search for new knowledge and the reinvention of the business, or exploitation which focuses on business administration and seeks to increase payoff from existing knowledge.

Intuition, originality and hypotheses about the future are often the driving forces behind exploration. On the other hand, analysis, reasoning, historical data and mastery are the forces behind exploitation. Both approaches can create significant value, and both are important to the success of any business organization. However, organizations struggle to pursue both approaches simultaneously.

More often, an organization chooses to focus on exploitation, to the exclusion of exploration and to its own disadvantage. The solution is not to embrace the randomness of intuitive thinking and avoid analytical thinking completely. The solution lies in the organization embracing both approaches, turn away from the false certainty of the past, and instead peer into a mystery to ask what could be.

In other words, balance exploration and exploitation, invention of business and business administration, and originality and mastery.

Finance plays a critical role in helping the business achieve efficiencies, redeploy the savings and redirect freed-up resources towards exploration of new opportunities.

Building design into finance

As design thinking is frequently associated with marketing and product development, finance is deemed an unlikely place to apply design thinking principles. However, design thinking can be applied to the finance function in every organization. The key is to identify and define the customers clearly and approach their needs empathetically.

Unlike the marketing function which focuses its efforts on external customers, finance’s efforts are focused on meeting the needs of its internal customers. To elevate design thinking in finance, the function should think differently about its structures, its processes, and its cultural norms.

Quite a number of finance organizations are organized around ongoing, permanent tasks. Roles are firmly defined, with clear responsibilities and reward incentives linked tightly to those individual responsibilities. The problem with such a structure is that it discourages employees to see the bigger picture. Individuals employees see their work as own territory to be protected by all means.

There is little to none collaboration. It’s all about “my responsibilities,” not “our responsibilities.” As a result, individuals limit their focus to those individual responsibilities, refining and perfecting outputs before sharing a complete final product with others. This can be routine production of monthly reports.

In contrast, designers are accustomed to working collaboratively with adhoc teams and clearly defined goals in a projected-oriented environment. Rather than waiting until the outcome is right, designers expose their clients to a series of prototypes that improve with each iteration.

Considering that finance business partnering extends beyond traditional month-end reporting tasks and involves working on various business related projects, sharing performance insights and creating value, CFOs should therefore foster a culture that supports project-based work and explicitly make it clear that working on a project is no less important or rewarded than running a business segment.

It is therefore imperative that finance business partners acquire design thinking capabilities that can help them develop a detailed and holistic understanding of their internal customers’ needs and frustrations, and serve them better by formulating and recommending creative and actionable solutions that deliver the desired outcomes.

Equally important too is having the courage to elicit feedback from business partners, develop mastery of the value proposition model and deliver improved solutions.

Rather than immortalizing the past, the focus should be on creating and influencing the future.

Finance Value Creation Goes Beyond Running the Financial Side of the Business

Advances in technology are helping the finance function reduce operational costs, streamline processes and improve productivity. Thanks to automation, tasks that used to take months to complete are being completed in weeks and those that took weeks to accomplish are getting done in days.

For instance, advanced analytics and robotic process automation are shortening the timelines finance teams require to produce a forecast, perform account reconciliations or close the books.

Technology is enabling more to be done with less and the trend is not expected to go away anytime time soon. A couple of years go the staff size of the finance function was big. CFOs were happy to have separate staff handle AP, AR, Payroll, Bank Reconciliations, Management Accounts etc.

Today the size of the finance function has shrunk significantly. Thanks to shared services centers, outsourcing and process automation. Robots have taken over rules-based, repetitive and transactional tasks that were once performed by humans.

Machine learning algorithms are already replicating highly analytical tasks, analyzing large data sets and churning out insights in real time to support decision making. Although the adoption of machine learning and/or AI tools is not yet widespread it’s only a matter of time before the technology becomes a part of our everyday life.

Implications for finance professionals

In order to stay current and move ahead finance teams need to evolve and adapt to the changing environment.

Some of the skills we have acquired in the past and relied on to get us to the next level are no longer sufficient in the current and future environment. As a result, we have to develop a continuous learning mindset. Learn new ways of doing things, unlearn the old habits and continue to relearn.

For instance, being detailed oriented alone used to be sufficient. Not anymore. Today finance professionals are expected to be commercially aware and broad in their thinking.

Decision makers are searching for collaborative business partners who have a deeper understanding of the operational and strategic challenges facing the business. Problem solvers able to enrich the business with insightful analysis and capable of recommending the right solutions. Team players who understand the markets in which the business operates, its products, competitor business and the drivers of performance as a whole.

Build a big picture perspective of the business

If finance is to be recognized as a valuable strategic business partner we need to build a big picture perspective of the business and be able to recognize the role and contribution of each function, individual, process and activity in achieving the objectives of the company. Knowing debits and credits alone will not take us far.

With the business environment constantly changing, we need to shift our focus from historical analysis to forward looking.

Many at times we spend a lot of time producing variance analysis reports that do not drive the right conclusions and actions out of the insights. For example, simply commenting sales for the month are up 5% or operational costs are down by $1MM is not insightful enough to support key decision making.

We need to understand what the numbers mean and the real drivers behind them. For example, did sales increase because of new customers, price increases, improved demand, enhanced marketing efforts, new product lines, entry into new markets, product bundling?

CFOs and their teams need to be doing more than running the financial side of the business – recording revenues and costs. Instead, they should help the business adapt and make insight-driven strategic turns without throwing off alignment between broad strategy and day-to-day execution.

Part of building a bigger picture perspective of the business requires a finance function that is more flexible and collaborative than in the past and knows how to manage its internal working relationships. A finance function that is capable of partnering with operations instead of always pointing out what operations is always doing wrong.

Spending the majority of our time behind our desks preparing financial statements and regulatory compliance reports will not help us become more strategic and commercially aware. We need to get interested in the affairs of the business. Avail ourselves for projects that take us out of our comfort zones. Regularly interact with colleagues outside finance to get a deeper understanding of the drivers of the business, what projects the teams are working on, how they align with the broader strategy, the risks and challenges they are facing and recommend solutions.

If you are used to sitting behind a computer all day, leaving your desk to engage with the business is initially unnerving but the more you do it the more confidence you gather. Evolving priorities require a finance professional with a well-honed ability to communicate, build trust and maintain collaborative relationships with the rest of the business.

Driving business growth versus cost cutting

Too often finance teams are focused on cost cutting activities in order to improve the bottom line instead of identifying alternative ways of driving up top line growth. Today’s global companies are operating in a world of complex supply chains, intense competition, shifting customer expectations, increased regulatory demands, emerging operating models and exposure to significant business risk. Cost reduction alone will not help the business sustain its competitive relevance in this world.

The problem with many cost optimization programmes is that they fail to deliver the expected outcomes. It is not about how much you cut the costs, rather where you channel resources to differentiate, stimulate growth and achieve strategic objectives. Finance needs to look beyond narrowly defined functional or organizational structures when identifying candidates for cost cutting and take a holistic, end-to-end view of costs across the whole organization. This will help separate the strategically-aligned good costs from the non-essential bad costs.

With the adoption of big data and analytical tools becoming mainstream, it’s not too late for finance to play catch up. Transitioning to data analytics starts with putting in place a well-structured data and information management foundation and then combining technology with the right analytics and expertise.

Only then can finance transform data into true, actionable business intelligence (on products, customers, markets, process efficiencies, supply chain, competition and business risk) that drives better informed decision making and business growth.

Traditional financial reporting does not provide the actionable information the business needs to make more informed strategic decisions. Today, the business needs to leverage both structured data (which resides in enterprise databases) and unstructured data (email, social media, internet) including analytics to generate insightful analysis that can help drive operational and strategic performance.

For example, finance should be able to collaborate with the marketing function, analyze and interpret customer data to understand customer journeys, and help the function design and implement better customer/brand strategies and responses.

Finance cannot expect to drive business growth by continuously doing the same things. It’s not about this is how we have always done things here. Ask yourselves: what is the right way of doing things in today’s disruptive world and what are the expectations of the business?

CFOs Beware: Don’t Get Caught Up In the Hype of New Technologies

In one of my articles, Finance Transformation: From Efficiency to Effectiveness, I recommended CFOs to first identify a business problem before investing in a new shiny piece of technology.  Today, there is so much talk about digital transformation and the immense potential of new technologies to drive business performance.

With a plethora of tools available on the market and all promising to deliver better results, one of the biggest challenges faced by many CFOs and finance executives  is identifying, evaluating and selecting the right tool for the business.

Compounding the problem are a myriad of  articles and blogs on finance digitization portraying messages such as,  “If you are not yet invested in digital, you have already missed the train or if  you are not using the cloud, be aware that everyone else is moving forward and moving faster than you are.”

For fear of missing out, some finance executives are leading their organizations on digital transformation initiatives without a clearly defined and articulated plan.

Too Many Unconnected Systems

Due to the lack of having a clearly mapped business strategy to address digitization, some finance executives are getting caught up in the hype that inevitably comes with every new piece of technology or software on the market. Instead of investing in technology or software that serves the business, they are investing in new tools that the consultants or software sellers recommend irrespective of whether the decision is rational or not. So often the end product is a disintegrated technological infrastructure.

New technology, combined with streamlined processes and talented people is supposed to transform finance from an inefficient function into an effective team player in the business. Unfortunately, this is not always the case. Technology is acting as a hindrance. Businesses are superimposing automation on broken or marginally improved processes in turn expecting magical results. In other instances, finance teams are spending a significant amount of time reconciling and aggregating data from different systems.

Today there is an increasing call on finance to play the role of a strategic advisor to business teams yet when it comes to answering basic performance questions, most finance teams are hard pressed to do so.  One of the reasons being that the information required to answer such important questions is housed in different systems all over the organization.

Further, the individuals responsible for partnering with the business to support decision making lack access to some of the systems. They have to rely on information on spreadsheets or reports produced by those with access and most of the time this information is not readily available.

Imagine the frustration of having to wait on someone for days or weeks to send you information and when the information finally arrives you realize that it is not what you expected. For example, the report is not for the business unit you are reviewing or the period selected for the report is incorrect. Because you don’t have access to the system you have to go back to the report compiler and explain again your information requirements.

This back and forth process slows down decision making at a time when accuracy, speed and agility are increasingly important.

Looking at the same information

One of the keys to have meaningful performance conversations is have everyone look at the same information. For example, if the business wants to review the level and nature of capital investments for a given period it is imperative to ensure that the source of this information is common across the organization.

I have come across situations whereby more than one system is used to record capital expenditures often with major record differences between the systems in use. Time and resources are then redirected to focus on reconciling and resolving the reporting differences. It is therefore imperative for CFOs and finance executives to understand that technology alone will not drive transformation.

The data you input into a system will determine the output of that system. That’s why it is important to make sure that everyone is working from a central data repository. Having multiple copies of solutions is inefficient and counterproductive.

As an organization, you do not need too many systems to look at the same information. Thus, before acquiring that new piece of technology always ask yourself, “What is the problem that this new technology will resolve and also how will the investment enhance or strengthen your existing technological capabilities?” Many at times, we are quick to point out the limitations of the current system and use that as the reason for investing in an alternative solution.

Instead of taking a holistic view of the technological needs of the business, we take a piecemeal approach. For example, if AP is not happy with the current system we invest in another AP-focused system. If another team identifies system deficiencies, we look around on the market for a specific tool that addresses that function. This cycle continues over a period of time and ultimately the organization is left with a handful of siloed pieces of systems not completely integrated into the overall technological infrastructure of the business.

As the business and its information needs evolve, sometimes a reconfiguration of the current system(s) as compared to implementing a new one is what is needed. That is why it is important, from the onset, to evaluate the suitability of each piece of technology against the various growth phases of the business. Ask the software seller, “If our business continues to grow, will your product still be able to support our business needs and help us deliver our unique value proposition?”

Considering all the plausible scenarios and options available will help you determine if the technology will serve you for the short or long-term future.

Fear of missing out

Studies have revealed that as individuals we are prone to mimicking people’s actions and their product choices instead of applying our own independent assessment and best judgement. This not only happens at a personal level but also at a professional level.

For example, imagine as a CFO you recently attended an industry conference and the majority of finance executives you met spoke about investing in AI capabilities. Some have already implemented pilot projects and others are at an advanced planning stage. Since your organization hasn’t made plans yet, you make a hasty decision to invest in AI to avoid missing out and keep pace with what others are doing.

The problem with this simple approach is that rather than initially evaluate AI investment from an internal point of view and your business’s strategy perspective, you are now investing in AI from an external point of view based on what other businesses are doing.

I am not saying it is wrong to collaborate and get ideas from industry peers. In fact, this is one of the key reasons for attending conferences. To get informed about emerging trends and disruptive technologies and prepare for an uncertain future.

What is important is that you don’t allow competitor behaviours to drive technology investments in your business. Rather, clarify your business questions first, fix any broken processes before looking into technology and assess how the investment aligns with your overall business strategy.

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