CFOs are excited and engaged, embracing their mandate to be key strategic players, and enthusiastically looking to the future. That’s the optimistic news coming out of a recent survey of over 1,500 finance executives, conducted by CFO Publishing and sponsored by SAP, from four regions across the globe.
Top SAP finance executives share regional perspectives
In a recent series of region-by-region podcasts, The Innovative Finance Function, four experts in financial trends discussed the survey findings and their own from-the-field perspectives. Commenting on their respective regions:
In North America, Thack Brown, general manager and global head of SAP’s Finance line of business
In Latin America, Fernando Lewis, VP of sales for SAP S/4HANA in Latin America
In Europe, Henner Schliebs, SAP global finance audience marketing executive
In Asia Pacific Japan, Richard McLean, SAP regional CFO
Moderated by Chris Schmidt of CFO Research, each podcast posed six survey-related questions to one of the regional experts. The following summarizes the responses to the first three questions from each financial thought leader, question by question, and pulls together cross-regional trends.
Question 1: Are you surprised that CFOs are overwhelming both pleased with their jobs and expect it to get better?
Expect Job to Get Better
None of the four executives was surprised about CFOs’ enthusiasm for their jobs or with their expectation of a continuing upward trajectory. All indicated that CFOs are being called upon more and more to expand their scope and responsibilities.
In North America, Brown noted, “Businesses are relying more on finance in turbulent times.” In Latin America, Lewis attributed the optimism largely to changes in technology that have helped CFOs move from “reactive scorekeepers to proactive partners.”
Schliebs called CFOs the “business partners of choice,” but added that the lower percentages of enthusiasm among European CFOs could largely be attributed to the recently troubled European economy. McLean pointed to the fact that CEOs in the Asia Pacific Japan region “expected more of CFOs.”
Question 2: Research found that a number of CFOs expect information technology to fall under the purview of the finance department. Why do you think that is?
Asia Pac/ Japan
Expect IT to come
under finance’s purview
All four SAP executives pointed out that both IT and finance transcend the entire organization.
Brown said that, based on this enterprise-wide point of view, “It is very natural that IT and finance form a deep partnership.” Lewis echoed the logic of the two functions being spearheaded by finance, since “transforming data and information” will impact new business models, product mix, market share, cash share, and other P&L–related initiatives.
In Europe, Schliebs remarked, “There is a mandate for the finance department to drive changes that support corporate growth and expansion, with IT being the platform for driving digital transformation.” In McLean’s view, “Business processes are becoming more IT oriented, with CFOs being asked to be increasingly involved in a holistic way.” As a result, the CFO has to make sure money in IT is well spent.
Question 3: What are the three biggest challenges facing financial professionals today?
Although specifics differed among the SAP executives, three challenges transcend geographic boundaries: dealing with increasing business complexities, handling regulatory requirements, and providing “instant insight.” Here are the top challenges, according to the exec in each region:
Helping organizations build needed capabilities for a successful future
Dealing with complexity in its many forms: regulatory, global, informational, and processes
Developing “instant insight” for decision making related to areas like “currency, acquisition, adapting business models, and day-to-day profitability”
Dealing with governance and compliance while, at the same time, creating new business models: “keeping the lights on and driving change.”
Driving change in real time – which requires new skill sets and new attitudes
Engaging the entire company: The finance team needs to interact with the entire corporation, understanding not just finance, but the workings of the whole organization.
Supporting changes to the business model and creating the organization of the future. “How do we outpace the competition?”
Managing complexity: Finance is drowning in complexities of process, regulation, and information. Complexity has to go, and the CFO has to lead the way to simplify.
Managing performance through instant insight: There is no longer room for periodic processes. How does the finance department get out real-time info as needed?
Asia Pacific Japan:
Transforming the organization to keep up with present and future business needs without compromising compliance or increasing costs
Expanding the scope of the finance function through collaboration and timely insights
Finding and developing the best talent in an environment where quality human resources are limited and competitive. The finance team needs to become entrepreneurial so they can transform the business.
In the next blog, we’ll look at the final three questions discussed in the podcasts and the similarities and differences across regions.
To learn more about how finance executives can empower themselves with the right tools and play a vital role in business innovation and value chain, download the regional research reports which offer additional information and valuable insights.
In the futurist article, Friedman says one of the biggest factors that will contribute to the CFO’s significant change over the next five years is technology.
Digital technology is obviously expected to drive change in high-tech companies, but Friedman says it’s industries outside of the tech sectors that are of particular interest, as they struggle to understand how to grasp and harness the digital capabilities available to them.
Working with high tech in low-tech industries
Five years from now, a finance team may be defined by how well it uses technology and innovative business tools, regardless of what industry it’s in. The article outlines some examples of ways that digital technology will increasingly be used by CFOs in “non-tech” sectors:
Predictive analytics: CFOs in manufacturing companies can forecast results and produce revenue predictions based on customer-experience profiles and current demand, instead of comparing to previous years as most companies still do today.
Social media and crowdsourcing: You may not think CFOs spend a lot of time on social media or crowdsourcing sites, but these methods can actually expedite finance processes, such as month-end responsibilities of the finance organization.
Big Data: CFOs already have a lot of data at their fingertips, but in 2020 they will have even more. CFOs in both tech and non-tech sectors who understand how to use that data to make valuable, informed decisions, can strategically guide their company and industry in a more digitally oriented world.
To do this, Friedman says CFOs can lead the way by addressing some critical areas:
Know the issues: Gather the key questions that leaders expect Big Data analytics to answer.
Make data easily accessible: Collect data that is manageable and easy to access.
Broaden skills: The finance team needs people with the skills to understand and strategically interpret the data available to them.
The tech-savvy CFO
The role of today’s CFO has already expanded to include strategic corporate growth advice as well as managing the bottom line. In 2020, Friedman says expectations placed on the CFO are presumed to be even greater, and CFOs will likely need a much more diverse, multidisciplinary skill set to meet those demands.
The article details several traits and skills that CFOs will need in order to keep up with the pace of digital change in their role.
Digital knowledge: CFOs must be tech-savvy in order to capitalize on technical innovations that will benefit their company and their industry as a whole.
Data-driven execution: CFOs will need the ability to execute company strategy and operations decisions based on data-driven insights.
Regulatory compliance: Regulations continue to be more stringent globally, so CFOs will need to be proficient at working closely with regulators and compliance systems.
Risk management: With the growing global economy comes increased cyber and geopolitical risks worldwide. The CFOs of 2020, especially those in large multinational organizations, will need to have the expertise to monitor and manage risk in areas that may be unforeseen today.
The future CFO’s well-rounded resume
By 2020, the CFO role will require much more than just an accounting background. According to Deloitte’s Frank Friedman, “CFOs may need to bring a much more multidisciplinary skill set to the job as well as broader career experiences, from working overseas to holding positions in sales and marketing, and even running a business unit.”
So if you’re a current or aspiring CFO, you have five years to round out your resume with the necessary skills to be ready for the digitally driven role of the CFO in 2020.
The Digitalist Magazine is your online destination for everything you need to know to lead your enterprise’s digital transformation.
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About Estelle Lagorce
Estelle Lagorce is the Director, Global Partner Marketing, at SAP. She leads the global planning, successful implementation and business impact of integrated marketing programs with top global Strategic Partner across priority regions and countries (demand generation, thought leadership).
Too many organizations ignore the business potential from streamlining accounts payable operations. In a digital economy, however, this may represent one of the best opportunities to improve financial performance and boost the bottom line.
In its recent report, ePayables 2015: Higher Ground, the research and advisory firm Ardent Partners made a strong case for accounts payable transformation. “In 2015, more AP groups are accelerating their plans to transform their operations and scale to new heights,” states the report.
The digital makeover
From a payables perspective, how you go about fixing outdated procure-to-pay (P2P) practices is much like the decision to improve an aging home. Do you tear your house down and build a new one, or leverage as much of the existing structure as you can and begin a major home improvement project?
There is, of course, a third option. Take no action and make calls to plumbers, electricians, roofers, and other specialists as needed before the house falls apart altogether. While few organizations would consider a “triage” strategy the best option to address deficiencies in P2P operations, many still do. (Just don’t share that with your CFO.)
This blog post is the first in a series that will examine options for upgrading procure-to-pay processes from outclassed to best-in-class. Continuing to focus time and effort on managing transactions just doesn’t make sense. With today’s business networks, organizations have new ways to collaborate with suppliers and other partners to buy, sell, and manage cash.
Automation handles low-value activities, eliminating data entry, exception management, and payment status phone calls. That leaves more time for benchmarking operations, monitoring supplier performance, expanding early payment discounts, and improving management of working capital – the kinds of things that can dramatically improve business performance.
Where do you start?
To begin, you have to recognize that getting your payables house in order is much more than a process efficiency initiative. While cost savings from e-invoicing can be 60% to 80% lower than paper invoicing, there’s much more to the business case.
Improving contract compliance and expanding early payment discounts are other components of a business case for P2P transformation. According to various procure-to-pay research studies and Ariba customer results, the cost savings from getting your payables house in order are conservatively estimated to be $10 million per billion collars of spend. We’ll break down these ROI components in greater detail in future posts on this topic.
The value of alignment
Another important first step, validated by the Ardent Partners report, is getting procurement and finance-accounts payables in alignment. As this is a holistic process, you’ll need to make sure that both organizations are in sync, and you have support from upper management to make it happen.
Now, back to the question: Do you approach a payables makeover to support P2P transformation as a tear-down or a fixer-upper? If your procurement-accounts payable teams are out of alignment, your P2P processes are predominantly paper, and decentralized buying leaves little control over spend, you’re looking at a tear-down to lay the foundation for best practices payables. We’ll share a blueprint with you in the next post in this series.
Chris Rauen is a solution marketer for Ariba, an SAP company. He regularly contributes to topics including e-invoicing and dynamic discounting as well as the value of collaborating in a digital economy.
We humans make sense of the world by looking for patterns, filtering them through what we think we already know, and making decisions accordingly. When we talk about handing decisions off to artificial intelligence (AI), we expect it to do the same, only better.
Machine learning does, in fact, have the potential to be a tremendous force for good. Humans are hindered by both their unconscious assumptions and their simple inability to process huge amounts of information. AI, on the other hand, can be taught to filter irrelevancies out of the decision-making process, pluck the most suitable candidates from a haystack of résumés, and guide us based on what it calculates is objectively best rather than simply what we’ve done in the past.
In other words, AI has the potential to help us avoid bias in hiring, operations, customer service, and the broader business and social communities—and doing so makes good business sense. For one thing, even the most unintentional discrimination can cost a company significantly, in both money and brand equity. The mere fact of having to defend against an accusation of bias can linger long after the issue itself is settled.
Beyond managing risk related to legal and regulatory issues, though, there’s a broader argument for tackling bias: in a relentlessly competitive and global economy, no organization can afford to shut itself off from broader input, more varied experiences, a wider range of talent, and larger potential markets.
That said, the algorithms that drive AI don’t reveal pure, objective truth just because they’re mathematical. Humans must tell AI what they consider suitable, teach it which information is relevant, and indicate that the outcomes they consider best—ethically, legally, and, of course, financially—are those that are free from bias, conscious or otherwise. That’s the only way AI can help us create systems that are fair, more productive, and ultimately better for both business and the broader society.
Bias: Bad for Business
When people talk about AI and machine learning, they usually mean algorithms that learn over time as they process large data sets. Organizations that have gathered vast amounts of data can use these algorithms to apply sophisticated mathematical modeling techniques to see if the results can predict future outcomes, such as fluctuations in the price of materials or traffic flows around a port facility. Computers are ideally suited to processing these massive data volumes to reveal patterns and interactions that might help organizations get ahead of their competitors. As we gather more types and sources of data with which to train increasingly complex algorithms, interest in AI will become even more intense.
Using AI for automated decision making is becoming more common, at least for simple tasks, such as recommending additional products at the point of sale based on a customer’s current and past purchases. The hope is that AI will be able to take on the process of making increasingly sophisticated decisions, such as suggesting entirely new markets where a company could be profitable, or finding the most qualified candidates for jobs by helping HR look beyond the expected demographics.
As AI takes on these increasingly complex decisions, it can help reduce bias, conscious or otherwise. By exposing a bias, algorithms allow us to lessen the impact of that bias on our decisions and actions. They enable us to make decisions that reflect objective data instead of untested assumptions; they reveal imbalances; and they alert people to their cognitive blind spots so they can make more accurate, unbiased decisions.
Imagine, for example, a major company that realizes that its past hiring practices were biased against women and that would benefit from having more women in its management pipeline. AI can help the company analyze its past job postings for gender-biased language, which might have discouraged some applicants. Future postings could be more gender neutral, increasing the number of female applicants who get past the initial screenings.
AI can also support people in making less-biased decisions. For example, a company is considering two candidates for an influential management position: one man and one woman. The final hiring decision lies with a hiring manager who, when they learn that the female candidate has a small child at home, assumes that she would prefer a part-time schedule.
That assumption may be well intentioned, but it runs counter to the outcome the company is looking for. An AI could apply corrective pressure by reminding the hiring manager that all qualifications being equal, the female candidate is an objectively good choice who meets the company’s criteria. The hope is that the hiring manager will realize their unfounded assumption and remove it from their decision-making process.
At the same time, by tracking the pattern of hiring decisions this manager makes, the AI could alert them—and other people in HR—that the company still has some remaining hidden biases against female candidates to address.
Look for Where Bias Already Exists
In other words, if we want AI to counter the effects of a biased world, we have to begin by acknowledging that the world is biased. And that starts in a surprisingly low-tech spot: identifying any biases baked into your own organization’s current processes. From there, you can determine how to address those biases and improve outcomes.
There are many scenarios where humans can collaborate with AI to prevent or even reverse bias, says Jason Baldridge, a former associate professor of computational linguistics at the University of Texas at Austin and now co-founder of People Pattern, a startup for predictive demographics using social media analytics. In the highly regulated financial services industry, for example, Baldridge says banks are required to ensure that their algorithmic choices are not based on input variables that correlate with protected demographic variables (like race and gender). The banks also have to prove to regulators that their mathematical models don’t focus on patterns that disfavor specific demographic groups, he says. What’s more, they have to allow outside data scientists to assess their models for code or data that might have a discriminatory effect. As a result, banks are more evenhanded in their lending.
Code Is Only Human
The reason for these checks and balances is clear: the algorithms that drive AI are built by humans, and humans choose the data with which to shape and train the resulting models. Because humans are prone to bias, we have to be careful that we are neither simply confirming existing biases nor introducing new ones when we develop AI models and feed them data.
“From the perspective of a business leader who wants to do the right thing, it’s a design question,” says Cathy O’Neil, whose best-selling book Weapons of Math Destruction was long-listed for the 2016 National Book Award. “You wouldn’t let your company design a car and send it out in the world without knowing whether it’s safe. You have to design it with safety standards in mind,” she says. “By the same token, algorithms have to be designed with fairness and legality in mind, with standards that are understandable to everyone, from the business leader to the people being scored.” (To learn more from O’Neil about transparency in algorithms, read Thinkers in this issue.)
Don’t Do What You’ve Always Done
To eliminate bias, you must first make sure that the data you’re using to train the algorithm is itself free of bias, or, rather, that the algorithm can recognize bias in that data and bring the bias to a human’s attention.
SAP has been working on an initiative that tackles this issue directly by spotting and categorizing gendered terminology in old job postings. Nothing as overt as No women need apply, which everyone knows is discriminatory, but phrases like outspoken and aggressively pursuing opportunities, which are proven to attract male job applicants and repel female applicants, and words like caring and flexible, which do the opposite.
Once humans categorize this language and feed it into an algorithm, the AI can learn to flag words that imply bias and suggest gender-neutral alternatives. Unfortunately, this de-biasing process currently requires too much human intervention to scale easily, but as the amount of available de-biased data grows, this will become far less of a limitation in developing AI for HR.
Similarly, companies should look for specificity in how their algorithms search for new talent. According to O’Neil, there’s no one-size-fits-all definition of the best engineer; there’s only the best engineer for a particular role or project at a particular time. That’s the needle in the haystack that AI is well suited to find.
Look Beyond the Obvious
AI could be invaluable in radically reducing deliberate and unconscious discrimination in the workplace. However, the more data your company analyzes, the more likely it is that you will deal with stereotypes, O’Neil says. If you’re looking for math professors, for example, and you load your hiring algorithm with all the data you can find about math professors, your algorithm may give a lower score to a black female candidate living in Harlem simply because there are fewer black female mathematicians in your data set. But if that candidate has a PhD in math from Cornell, and if you’ve trained your AI to prioritize that criterion, the algorithm will bump her up the list of candidates rather than summarily ruling out a potentially high-value hire on the spurious basis of race and gender.
To further improve the odds that AI will be useful, companies have to go beyond spotting relationships between data and the outcomes they care about. It doesn’t take sophisticated predictive modeling to determine, for example, that women are disproportionately likely to jump off the corporate ladder at the halfway point because they’re struggling with work/life balance.
Many companies find it all too easy to conclude that women simply aren’t qualified for middle management. However, a company committed to smart talent management will instead ask what it is about these positions that makes them incompatible with women’s lives. It will then explore what it can change so that it doesn’t lose talent and institutional knowledge that will cost the company far more to replace than to retain.
That company may even apply a second layer of machine learning that looks at its own suggestions and makes further recommendations: “It looks like you’re trying to do X, so consider doing Y,” where X might be promoting more women, making the workforce more ethnically diverse, or improving retention statistics, and Y is redefining job responsibilities with greater flexibility, hosting recruiting events in communities of color, or redesigning benefits packages based on what similar companies offer.
Context Matters—and Context Changes
Even though AI learns—and maybe because it learns—it can never be considered “set it and forget it” technology. To remain both accurate and relevant, it has to be continually trained to account for changes in the market, your company’s needs, and the data itself.
Sources for language analysis, for example, tend to be biased toward standard American English, so if you’re building models to analyze social media posts or conversational language input, Baldridge says, you have to make a deliberate effort to include and correct for slang and nonstandard dialects. Standard English applies the word sick to someone having health problems, but it’s also a popular slang term for something good or impressive, which could lead to an awkward experience if someone confuses the two meanings, to say the least. Correcting for that, or adding more rules to the algorithm, such as “The word sick appears in proximity to positive emoji,” takes human oversight.
Moving Forward with AI
Today, AI excels at making biased data obvious, but that isn’t the same as eliminating it. It’s up to human beings to pay attention to the existence of bias and enlist AI to help avoid it. That goes beyond simply implementing AI to insisting that it meet benchmarks for positive impact. The business benefits of taking this step are—or soon will be—obvious.
In IDC FutureScapes’ webcast “Worldwide Big Data, Business Analytics, and Cognitive Software 2017 Predictions,” research director David Schubmehl predicted that by 2020 perceived bias and lack of evidentiary transparency in cognitive/AI solutions will create an activist backlash movement, with up to 10% of users backing away from the technology. However, Schubmehl also speculated that consumer and enterprise users of machine learning will be far more likely to trust AI’s recommendations and decisions if they understand how those recommendations and decisions are made. That means knowing what goes into the algorithms, how they arrive at their conclusions, and whether they deliver desired outcomes that are also legally and ethically fair.
Clearly, organizations that can address this concern explicitly will have a competitive advantage, but simply stating their commitment to using AI for good may not be enough. They also may wish to support academic efforts to research AI and bias, such as the annual Fairness, Accountability, and Transparency in Machine Learning (FATML) workshop, which was held for the third time in November 2016.
O’Neil, who blogs about data science and founded the Lede Program for Data Journalism, an intensive certification program at Columbia University, is going one step further. She is attempting to create an entirely new industry dedicated to auditing and monitoring algorithms to ensure that they not only reveal bias but actively eliminate it. She proposes the formation of groups of data scientists that evaluate supply chains for signs of forced labor, connect children at risk of abuse with resources to support their families, or alert people through a smartphone app when their credit scores are used to evaluate eligibility for something other than a loan.
As we begin to entrust AI with more complex and consequential decisions, organizations may also want to be proactive about ensuring that their algorithms do good—so that their companies can use AI to do well. D!
Over the last 10 years, the exponential growth and power of technology have brought some fascinating, if not mind-bending, opportunities. Machines talk to one another with computer-connected humans on the other end observing, analyzing, and acting on the explosion of Big Data generated. Doctors use algorithms that mine patient history or genetic information to detect possible diagnoses and treatment. Cars are programmed with data-driven precision to direct drivers to the best-possible route to their destination. And even digital libraries for 3D parts are growing rapidly – possibly to the point where we can soon print whatever we need.
With all of this technology, it is common sense to believe that productivity would also rise over the same span of time. However, according to a recent 2016 productivity report released by the Organisation for Economic Co-operation and Development (OECD), this is, sadly, not the case. In fact, most advanced and emerging countries are experiencing declining growth that is cutting across nearly all sectors and affecting both large and small firms. But more interesting is the agency’s observation that this trend does not exclude areas where digital innovation is expected to improve information sharing, communication, and finance.
Although nearly 5 billion people on our planet have a computer in their pocket or their hands at any moment of the day, our digital ways have not translated into productivity gains for the enterprise. The culprit? Businesses are not changing their processes to allow that technology to reach its full potential.
Technology alone does not bring real digital transformation
Every week, I hear how companies worldwide are so excited about their digital transformation initiatives. Some are developing their own applications or executing a new digital commerce strategy. Others may decide to deploy a new analytics tool. No matter the investment, there is always great hope for success. Yet, they often fall short because the focus is typically on how technology will change the business – not how the enterprise will change to fully embrace the digital innovation’s potential.
Take, for example, a bank’s decision to allow the loan process to be initiated through a mobile app or online store. The bank may receive the information from the consumer faster than ever before, but no real benefit is achieved if it still takes three weeks to approve or decline the loan request. Technology may be changing the customer experience online, but back-office processes are unaffected. The same old ways of work are still happening, and productivity is not improving. For a digital world where everything is supposed to be automatic and immediate, a customer will inevitably turn to a competitor that will approve the loan faster.
True digital transformation requires more than technology. Companies must evolve their processes with a keen focus on outcomes, not just infrastructure. All too often, they are focused on creating this sort of digital facade where it appears to be a digital experience for the customer, but, in reality, the back-office still has not caught up to support that level of digitization.
Deep digital transformation starts with process innovation
In the coming year, most companies will look to transition to real-time analytics that drives predictive decision-making and possibly draw from the Internet of Things. While this technology presents a clear opportunity for greater insight, organizations are no better off unless they transform business processes to act quickly on them.
Traditional data processes require days to move data from one database to another, process it, and generate reports in an easy-to-understand format. In-memory computing accelerates these processes from days and weeks to hours and minutes – paving the way for transformative power by moving decision-making closer to data generation. However, no matter how fast the analysis, no benefit is realized if downstream processes and decisions do not capitalize on the resulting insight. Like the loan process I mentioned earlier, you need to make sure that the back office and front office are aligned in order to produce improved business outcomes. Legacy systems and databases may still hinder the ability to achieve faster results, unless they are aligned with in-memory analytics.
The ability to modernize core systems with technologies like in-memory computing and innovative new applications can prove to be highly transformational. The key is to integrate these new technologies into an overall business architecture to support digital transformation and deliver real business improvements.
The Digitalist Magazine is your online destination for everything you need to know to lead your enterprise’s digital transformation.
Read the Digitalist Magazine and get the latest insights about the digital economy that you can capitalize on today.
About Hu Yoshida
Hu Yoshida is responsible for defining the technical direction of Hitachi Data Systems. Currently, he leads the company's effort to help customers address data life cycle requirements and resolve compliance, governance and operational risk issues. He was instrumental in evangelizing the unique Hitachi approach to storage virtualization, which leveraged existing storage services within Hitachi Universal Storage Platform® and extended it to externally-attached, heterogeneous storage systems.
Yoshida is well-known within the storage industry, and his blog has ranked among the "top 10 most influential" within the storage industry as evaluated by Network World. In October of 2006, Byte and Switch named him one of Storage Networking’s Heaviest Hitters and in 2013 he was named one of the "Ten Most Impactful Tech Leaders" by Information Week.