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Talking About the Risks of AI and Cognitive Technologies

According to the recently published PwC’s 22nd Annual Global CEO Survey, 85% of the surveyed CEOs overwhelmingly agree artificial intelligence (AI) will have a significant impact on their business within the next five years. For this reason, they have plans to pursue AI investments.

This is despite the fact that the information gap between the data CEOs are requiring to make informed decisions and what they are getting from their teams has not closed. Lack of analytical talent, data siloing and poor data reliability are the primary reasons the data they receive is inadequate.

Nonetheless, the application of AI and the underlying cognitive technologies such as machine learning, computer vision, natural language processing (NLP), audio and signal processing, speech recognition, predictive systems and robotics are wide-ranging, with the potential to improve performance in nearly any activity that generates large amounts of data.

Highly-powered algorithms which are the basis of these computer systems are presented with large amounts of data and subjected to supervised, semi-supervised, unsupervised, reinforced and deep learning.

The goal is to train the algorithms to identify relationships or patterns between the inputs and the outputs and use those rules to predict future outcomes with input data alone.

For example, in healthcare, AI is being used to study patient clinical data and recommend diagnoses. In finance, machine learning algorithms are being used to analyze transactions and uncover fraud and money laundering.

In the retail industry, predictive algorithms are being trained to automatically group customers into various categories based on their needs or buying patterns. These insights are then used to prioritize sales efforts and tailor promotions.

In other cases, companies have piloted NLP technology to monitor social media sentiment. The technology automatically identifies conspicuous topics of consumer conversations and sentiment surrounding those topics.

The generated insights are being used to influence decisions on improving marketing and customer service.

It is no surprise then that CEOs are now exploring how to implement these new technologies in their business.

Hype-driven or well-informed investments?

As much as AI is a source of significant business opportunities, the same technology is also a source of significant threats that must be evaluated. This is essential for helping leaders make informed and intelligent investment and risk decisions.

It is foolhardy for leaders to jump on the AI bandwagon and expect to capture the promises of AI and cognitive technologies if they lack an understanding of whether, how, and where to invest in applying these technologies.

When almost everyone is talking about the opportunities of AI and cognitive systems, it’s easy to cave in to hype-driven or ill-informed investments and overlook the fact that AI and cognitive technologies are not the solution to every business problem or situation.

That is why it is critical to evaluate the business case for investing in these technologies and assess the potential impact on your company’s business model, culture, strategy and sector.

Take a holistic view of your business processes, products and markets to weigh where the use of AI maybe be practical, profitable and crucial.

Algorithms are only as good as the data they learn from

Given that AI capabilities are data-driven, closing the information and talent gaps is key to unlocking AI’s potential. AI-powered algorithms improve over time through their experience of using data.

They learn relationships between variables in historical data sets and their outcomes. The relationships are used to develop models, which in turn are used to used to predict future outcomes without needing to be explicitly programmed by a programmer.

The systems change and evolve depending on the data that is fed to the algorithms. This therefore requires the data that is fed to the systems to be accurate, complete, diverse, and free from errors and bias. If the data is incomplete, error-prone or contains innate bias, the algorithms are likely to display false patterns as well as magnify the bias leading to misleading outcomes that have far-reaching repercussions.

Since AI and cognitive technologies deliver outcomes based on historical or existing data presented to them, leaders need to acknowledge that these systems will not necessarily provide flawless outcomes.

That is why it is critical to have appropriate data governance structures and talent in place to monitor where and how these technologies are deployed across the organization.

Skilled personnel play the critical role of overseeing biases and risks emanating from algorithms. For example, these people help identify and mitigate risks associated with programming errors.

Understand the black box of AI

As business leaders lay the foundation to pursue AI investments and entrust key decision making processes to intelligent machines, it is worthy to demystify the ‘black box’ of AI.

This is the notion that we can understand the inputs and outputs of an AI-powered system, but don’t understand what happens inside.

Accountability is an important element of decision making, and in order to make AI systems accountable for their decisions, AI-based decisions need to be explainable in order to be trusted.

Rather than blindly entrust machines to make important decisions, leaders therefore need to develop an understanding of how the technology works and how it makes decisions.

Thus, business leaders must be able to identify and explain the layers of decision making which underpin the operation of the systems and influence the final outcomes.

For example, are you able to identify and explain which connections have predictive value in the multilayered deep neural networks? Although it’s impossible to analyze all the connections in a deep neaural network, it’s important to prioritize what you need to know, what you want to understand, and why.

Over time, through testing and measuring, or trial and error, you will be able to understand the thought process behind algorithms, trust the decisions they make and ensure a robust governance structure is in place to monitor these technologies as they mature.

Surge in Cyber Attacks

Big data has been a boon to the development of AI and cognitive technologies. Thanks to advances in technology, our digital lives are producing staggering amounts of data each day.

As a result, interest in AI application is surging as decision makers try to make sense of all the data at their disposal.

Nevertheless, leaders need to be aware that the more data is generated the higher the probability of cyber criminals or hackers targeting the company’s AI systems to steal personal data or business confidential information.

A major data breach can have unintended consequences that can create legal, brand and public relations issues for the business.

Therefore, as leaders seek to capture the opportunities of AI and cognitive technologies, they mustn’t turn a blind eye to the limitations of these systems.

They must also consider the various ethical, moral, and legal issues associated with the AI systems that their organizations deploy.

More Data Doesn’t Always Lead to Better Decisions

Thanks to advancements in technology, our digital lives are producing expansive amounts of data on a daily basis.

In addition to this enormous amount of data that is produced each day, the diversity of data types and data sources, and the speed with which data is generated, analyzed and reprocessed has increasingly become unwieldy.

With more data continuously coming from social spheres, mobile devices, cameras, sensors and connected devices, purchase transactions and GPS signals to name a few, it does not look like the current data explosion will ebb soon.

Instead, investments in IoT and advanced analytics are expected to grow in the immediate future. Thinking back, investing in advanced data analytics to generate well-informed insights that effectively support decision making and drive business performance have been the paragon of big corporations.

And smaller businesses and organizations, as a result, have for some time embraced the flawed view that such investments are beyond their reach. It’s no surprise then that adoption has been at a snail’s pace.

Thanks to democratization of technology, new technologies are starting to get into the hands of smaller businesses and organizations. The solutions are now being packaged into simple, easy-to-deploy applications that most users without specialized training are able to operate.

Further, acquisition costs have significantly reduced thereby obviating the upfront cost barrier, that for years, has acted as a drag on many company IT investments.

While the application of data management and advanced analytics tools is now foundational and becoming ubiquitous, growing into a successful data-driven organization is about getting the right data to the right person at the right time to make the right decision.

Distorted claims such as data is the new oil have, unfortunately, prompted some companies to embark on unfruitful data hoarding sprees. It is true that oil is a valuable commodity with plentiful uses. But, it is also a scarce resource not widely available to everyone.

The true value of oil is unearthed after undergoing a refinement process. On the contrary, data is not scarce. It is widely available. Nonetheless, akin to oil, the true value of data is unlocked after we have processed and analyzed it to generate leading-edge insights.

It’s a waste of time and resources to just hoard data and not analyze it to get a better understanding of what has happened in the past and why it has happened. Such insights are crucial to predicting future business performance scenarios and exploiting opportunities.

More data doesn’t necessarily lead to better decisions. Better decisions emanate from having a profound ability to analyze useful data and make key observations that would have otherwise remained hidden.

Data is widely available, what is scarce is the ability to extract informed insights that support decision-making and propel the business forward.

To avoid data hoarding, it is necessary to first carry out a data profiling exercise as this will assist you establish if any of your existing data can be easily used for other purposes. It also helps ascertain whether existing records are up to date and also if your information sources are still fit-for -purpose.

At any given time, data quality trumps data quantity. That is why it is important to get your data in one place where it can easily be accessed for analysis and produce a single version of the truth.

Unlike in the past where data was kept in different systems that were unable to talk to each other making it difficult to consolidate and analyze data to facilitate faster decision making, the price of computing and storage has plummeted and now the systems are being linked.

As a result, companies can now use data-mining techniques to sort through large data sets to identify patterns and establish relationships to solve problems through data analysis. If the data is of poor quality, insights generated from the analysis will also be of poor quality.

Let’s take customer transactional data as an example. In order to reveal hidden correlations or insights from the data, it’s advisable to analyze the information flow in real-time; by the hour, by the day, by the week, by the month, over the past year and more. This lets you proactively respond to the ups and downs of dynamic business conditions.

Imagine what could happen if you waited months before you could analyze the transactional data? By the time you do so, your insights are a product of “dead data”. Technology is no longer an inhibitor, but culture and the lack of leadership mandate.

As data become more abundant, the main problem is no longer finding the information as such but giving business unit managers precise answers about business performance easily and quickly.

What matters most is data quality, what you do with the data you have collected and not how much you collect. Instead of making more hay, start looking for the needle in the haystack.

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.

Thinking About The Upside of Risk

Making intelligent and informed decisions is intrinsic to effective risk management. Many at times risk management decisions are centered around loss events and the negative consequences that might eventuate. The positive aspects of risk taking are hardly noticeable.

Let’s take as an example, a decision by local-based company to build a sales and distribution presence in a new international market. Some of the risks associated with pursuing such a move include:

  • Regulatory or unanticipated government intervention aimed at foreign players.
  • Currency volatility. Shifts in foreign currency values have both positive and negative implications on the company’s costing and selling prices, and ultimately profitability.
  • Political Uncertainty. Increased political tensions between countries often lead to trade wars, supply chain disruptions and minimal trade opportunities.
  • Heightened Corruption. Companies entering certain markets may be confronted with unorthodox ways of doing business. In a number of countries, bribery is required in order to complete trade.

On the other hand, the opportunities of expanding into the new market include:

  • The business is able to keep pace with competitors by pursuing an international business strategy.
  • Potential to serve more customers. A larger consumer market ultimately means enhanced profit margins.
  • Exploring new markets can lead to innovation through external partnerships.
  • Market diversification. Having a presence in more than one market also spreads risk as the business is not completely reliant on one market.

In spite of the opportunities lingering on the horizon, the tendency for decision makers is to fixate on the negative side of risks.

Rather than identify and exploit the upside of risk for value creation, decision makers resort to singing the default anthem ‘No, no, no. It’s too risky.’

Risk taking is strictly eschewed or mitigated – always from the downside. Given today’s surging economic uncertainty and volatility, and the integral role of effective risk management in driving business performance, an unreserved mindset change is necessary.

It’s not about eliminating or even terminating risk as risk will always be present. It’s about mastering what might happen, considering all the potential opportunities, including the potential risks, evaluating whether this is acceptable and then acting as required to effectively pursue set business objectives.

Therefore, instead of always being risk averse, decision makers need to start thinking about the upside of risk and develop an understanding that there is a benefit to taking on more risk, provided this is done in a controlled way and not higgledy-piggledy.

As a strategic advisor to the business, finance can play a critical role in helping management make better informed decisions about uncertainties.

We can achieve this through taking initiative and integrating ourselves in operational and strategic performance discussions, understanding the business and its entire operations, and asking smart questions aimed at helping management perform their jobs better.

Doing so empowers us to provide decision makers with cogent advice that ensures they have solid information about both the upside and downside of the company’s business strategy, and ultimately help them make enlightened decisions.

In other words, the advice we allot to decision makers should not act as an impediment to the achievement of business objectives. Alternatively, it should help them understand the odds of achieving the objectives and business success.

Effective risk management far exceeds risk protection and compliance, loss avoidance or arranging insurance cover to mitigate negative consequences.

Old habits die hard. Nevertheless, growth and progress ensue from challenging the status quo and embracing new habits. Stop paying attention on avoiding loss and start taking a broad, strategic view on the upside and downside of risk.

Resolve how you can literally create value and support the successful execution of business strategy and achievement of objectives.

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.

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