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Organizations are slow at adopting progressive methods. This is true for CFOs, CPAs and accountants. The accounting profession needs to prepare for change and threats to competitive advantage because there is an accelerating and disruptive digital technologies transformation in progress called the “digital revolution”. We are witnessing significant changes in the nature of technologies available for today’s managers and employee teams with regard to infrastructure, availability and capacity. These elements have accumulated in four key technologies often referred as SMAC – Social, Mobile, Analytics and Cloud. Venture capital investors have recently shifted towards big data and artificial intelligence that combine these technologies. These investments are accelerating the impact of this revolution. Examples of digitization disrupting traditional industries such as Uber for car passenger transportation are just the tip of the iceberg. While the term artificial intelligence (AI) originated in the 1950s, the world turned its back to the promises of AI. For decades, research in this space continued by a handful of researchers in Canada. Their continuing research has recently contributed to the rebirth of interest in artificial intelligence. It’s no surprise that some of the relevant startups for professional services firms emerged out of Canada. As examples: Ross Intelligence can streamline the manual search that junior attorneys in law firms labor at to research past cases. MindBridge AI is capturing and augmenting assurance associates’ knowledge into an AI, enhancing professional judgment and simplifying work around data and analytics. Both examples will create new capacities or remove obsolete job titles and will redefine workflows in these professions. Embracing “digital transformation” is the recourse for protection and preservation. This doesn’t mean that accountants should seek to become data scientists or build mobile apps. They need the competence to choose the technologies fitting them and be demanding and aggressive in adopting them. In some areas, low cognitive tasks, such as the manual and tedious tasks performed by accountants, can be augmented and monitored, down to key strokes, by an AI engine. The AI will never take a vacation or get tired. It can operate 24/7. 5 accounting functions that will be impacted As automation displaces a traditional accountant’s work, it is important for those affected to have a positive and an optimistic attitude and consider the newly-created upside potential for them to perform fulfilling work and higher cognitive tasks. Despite science fiction movies that present an apocalyptic view of robots, the future should not be feared. This is because robotic software can, for now, only handle low-cognitive tasks and does not have a sense of self-preservation like humans. Regardless, we need to clearly identify where they will impact work the most. Here are five accounting functions that we believe will be highly impacted: 1. Transactional accounting processes Clerical accountants are the most vulnerable to digitalization and automation because their roles involve routine tasks like bookkeeping and data entry. Primary examples are customer order processing, invoicing, credit, accounts receivable, payment collection, vendor purchase order processing and accounts payable, payroll processing, and travel and expense processing. 2. Fiscal period-end accounting closes The risk of digitalization for accountants is due to the increasing application of affordable commercial software that automates the workflow processes of the monthly, quarterly and fiscal year-end accounting close. Software can quickly access source data and apply tax calculation rules. Small businesses, similar to individual households, can now use commercial tax preparation software instead of hiring tax professionals from a third-party service. 3. Auditing The purpose of an audit is to obtain reasonable assurance about whether financial statements are free of material misstatements and irregularities due to error or fraud. Digitalization improves the quality of an audit in many different ways. For example, using an AI-expert system capable of scanning through 100% of the data and applying advanced analytics and anomaly detection in the audit can lead to better-informed risk assessments. It leads to a far more focused and relevant (higher quality based on risk) sample which increases the speed of engagements and decreases liability. 4. Business process outsourcing (BPO) of accounting tasks The general term for third parties who perform outsourced accounting tasks is business process outsourcing (BPO). The BPO business model is typically based on fee-for-service pricing. With centralization and economies of scale from having multiple customers, a BPO provider can often perform both front and back office accounting tasks more efficiently. 5. Regulatory filings Automation and technology have already begun to revolutionize regulatory compliance reporting. The implications are that rather than accountants requiring only mathematical acumen, mastery of tax laws or bookkeeping proficiency, accountants can devote more time with increased skill to interpreting and analyzing financial information. For example, they can use XBRL, a format that can now digitally transmit its financial statement filings to government regulatory agencies. Mitigating risk due to digitalization within accounting Automation is bound to impact accounting tasks and jobs. In some tasks where complexity is substantial and the volume, variety and velocity of data are all high, computer software may outthink a human analyst. Automation is also capable of applying what was learned from previously solved problems to new problems. For example, automated analysis to evaluate the financial return from varying capital investments, such as for different assets such as machines, can be used to evaluate acquiring different types of new customers. Accountants need to face the reality that low-cognitive tasks will soon be performed by a combination of brute computer processing power, big data and algorithms. The most severe risk an accountant faces due to digitalization and automation is the elimination of their job. Other risks are downward pressure on salaries for some accounting positions, with potential increases in workload and work hours. Different people have different reactions to change. Some people may deny the change, while others may embrace it. There are several ways that accountants can mitigate the impact on themselves: • Increasing skills with education and training As automation increases examining the output of automation, including reports and analysis, will be emphasized. As this emphasis changes, accountants can convert their feared risks into opportunities. They can do this by acquiring new skills and capabilities such as with planning, strategizing and analysis which contribute higher value to the organization than simply reporting data. This can be accomplished via education and training. For example, The Institute of Management Accountants reports that members who pass its Certified Management Accountant (CMA) exam earn on average a 35% higher salary relative to comparable accountants without the CMA degree. • Augmenting digital automation In certain cases, accountants will find that robotic and analytic software does not fully replace a job function. Instead, it will automate the repetitive tasks of a workflow process, and the accountant can then augment the automation with value-adding work. For example, as automation reduces errors and generates information more quickly, the accountant can shift from producing reports to investigating discrepancies. In effect, the accountant becomes the machine’s supervisor. As automation occurs many jobs will be redefined rather than eliminated. • New business models from digital disruption Entrepreneurial accountants will recognize the opportunities that digitalization, automation and AI can bring for expanding existing business models such as business process outsourcing and tax processing services. Additional opportunities are to pursue new business models, such as financial software implementation services, including providing the analysis generated from the information produced from the software. As one begins to more fully understand the impact of software automation and the speed at which it will affect accounting jobs, accountants have two broad choices on how to react. The first is fearfully, wondering if they chose the wrong profession and should pursue a different career. The second is to seize the opportunity for change and embrace the positive and imminent impacts from automation. This includes preparing themselves for less tedious and more fulfilling work that will bring increasing value to their organizations and their clients – as well as themselves. The choice will be their own. No one has a crystal ball, but our bet is they will make the latter choice. Article co-written by Gary Cokins and Solon Angel Image credit by Getty Images, Photographer's Choice, Biddiboo Want more? For Job Seekers | For Employers | For Influencers
IBM Watson  is partnering with tennis players and coaches to enhance their game strategy and better prepare for matches. As a partner of the US Open tournament for more than 25 years, IBM and the USTA will integrate its AI Highlights technology into player performance as the tournament enters the next phase of its technology journey. For the past year, IBM has been working with the USTA Player Development's performance team to develop a technology solution that will help coaches and players analyze and improve their performance. The new player development solution uses AI Highlights, enabling them to create real-time content to engage their fans. It also reviews hours of match footage and automatically identifies and indexes key points and stats, allowing coaches to design reports for subsequent matches. This enables coaches to reference and review a comprehensive database of players' indexed match video that they otherwise would not have been able to access. "Coaches and tennis players look to video as a useful resource that helps to evaluate players and develop scouting reports before and during tournaments. Working with IBM enables us to process and index video using AI to free up valuable intellectual capital that we can re-allocate to more interpretive and customized analysis," said Martin Blackman, General Manager, USTA Player Development. "Analyzing footage of previous matches is normally very time-intensive, involving many hours of manual 'match-tagging.' Video tagging that used to take hours can now take Watson minutes to execute." Two new AI Highlights solutions will be available for the USTA Digital Team: An AI Highlight dashboard that will populate in near real-time every shot of a match and its excitement level. The US Open Digital Team will be able to view and find the most exciting shot of the day or the match and leverage this content across all their digital channels, including social media. An AI Highlight builder will allow the US Open Digital Team to generate a highlight video for any match played on one of the seven show courts. The system will generate a list of proposed points to be included in the highlights package. Once shots are selected from the list, the system generates the highlights package for the USTA Digital Team to publish. New features to evolve the real-time fan experience: On Facebook Messenger and all US Open digital platforms, Virtual Concierge gives fans access to an AI-powered chatbot. Enabled by Watson services, the chatbot answers questions about scoring, schedules, transportation, and dining options. Depending on the question, the Virtual Concierge may also direct users to their points of interest within an interactive map of the venue, which will also indicate an attendee's current location. The SlamTracker experience now includes a "momentum" feature, so at a glance, fans can see who has advantage as well as the shifts in momentum over the course of all live matches. SlamTracker also provides scores, stats and insights for all matches in progress, giving US Open fans a level of analysis and engagement. "The US Open offers an enormous amount of tennis across multiple courts over the two weeks of the tournament. We're proud to be able offer multiple fan experiences both on-site at the USTA Billie Jean King Tennis Center, and on the official US Open digital platforms that serve millions of fans around the world," said Noah Syken, VP of Sports & Entertainment Partnerships, IBM. "AI technology will help fans follow matches and navigate their time on the US Open grounds. While we're seeing this type of technology come to life through tennis, these AI-powered solutions also are impacting many other industries," said Syken. Article published by Anna Hill Image credit by IBM Want more? For Job Seekers | For Employers | For Influencer
In order to succeed in a digital world, companies must get out of inertia and negative complexity. Instead, companies must focus on the future from a digital perspective and working backwards. This includes changing retrograde accounts for predictive analytics, combined with experimental data-driven strategy. Yet even then, the data may not contain the proper answer. The combination of some data and aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data, no matter how big the data are. Moreover, in several examples, data correlation is not causation. In the more traditional, mainstream companies — firms that have built and cultivated their management teams by heavily valuing intuition and experience as a way to make decisions — the executives don’t understand the statistical black box that comes with the advanced analysis. No one is advocating using analytics as a replacement for judgment and intuition. Rather, analytics has the potential to become a much more powerful aid to judgment and intuition. Data management readiness Big data is commonly defined based on 5 Vs: Large Volumes of data Variety of data formats High Velocity of processing Veracity,  referring to the messiness or trustworthiness of the data Value, referring to our ability turn our data into value Big data and analytics technology now allows us to work with these types of data. The volumes often make up for the lack of quality or accuracy. The common notion is that bigger is often better when it comes to data and analytics, which implies a perception of high complexity and high investment required. But this is not always the case. Michael Stonebraker, a renowned database researcher, Turing Award laureate and adjunct professor at MIT's Computer Science and Artificial Intelligence Laboratory, presented in his research that 69% of corporate executives named greater data variety as the most important factor. Therefore, many firms see the big opportunity in Big data resulting from the capture of traditional legacy data sources that have gone untapped in the past. These are data sets that have typically sat outside the purview of traditional data marts or warehouses — the “long tail” data. When performing a large-scale data governance implementation, there are many critical preparations that need to be completed first — most importantly, you’ll need to establish data quality enough for business purpose. Without clean, quality data in the system, the real-time capabilities available through any technologies will not deliver the correct results. Rather, it will simply end up delivering the wrong information faster. Possibly the most difficult task in any large-scale data migration project is establishing when the data is business-ready. This means that the data is not only ready-to-load, but that it will also generate the desired result in the new target real-time system. Typical processes to determine data readiness involve creating and running scripts and execution logic along with analyzing error reports for failures and required changes. Analytics and moreover, advanced analytics are based on data and algorithms. Decision analytics supports human decisions with visual analytics the user models to reflect reasoning. Descriptive analytics gains insight from historical data with reporting. Predictive analytics employs predictive modeling using statistical and machine learning techniques. Prescriptive analytics recommends decisions using optimization and simulation. In general, there is poor competence within the organizations using predictive analytics, which algorithms are much more complex than observed in a Business Intelligent models based on semantic layer and structured data (descriptive analytics). Data science capabilities The first step is not looking for Big data solutions to follow the crowd. And also not looking to be so impressive with the big and complex cases presented as most of have taken a long journey before obtaining achievements. It is necessary to recognize that the company should be managed based on data, which implies some culture and profound mindset shifts. In addition, there are serious legal and regulatory risks around issues such as data privacy that can result in breach of trust. Most consumers do not want companies to share personal information, and many say that they are less willing to share personal information in the future. In general, you should start looking for smaller, quicker wins, but make sure you get the right data that addresses the strategic problem you're trying to measure or compare, not just the data that's easiest to obtain. While this can speed up a project, the analytic results are likely to have only limited value, which can jeopardize the whole program. The basic ways that data can be obtained are from APIs, from databases and from colleagues in various formats. Tidy data dramatically speeds downstream data analysis tasks. The components of a complete data set include raw data, processing instructions, codebooks and processed data supporting collecting, cleaning and sharing data. You should ask the right questions to manage data sets, making inferences and creating visualizations to communicate results. Traditionally, data visualization or reporting is developed based on a waterfall approach where typically one expert provides the requirements in the earlier phases of the process and verifies the final result near the end. In big data, the agile approach is more adequate as multiple experts and IT teams must work together toward the solution in a timely manner with defined iterative job tasks, requiring much more engagement and availability to the project. Applying analytical insights The extensive usage of key process indicators and dashboards have escalated the adoption of BI platforms across industries. However, the transition from big data pilots to organization-wide deployments can be difficult given costs, effort required and broader questions about whether the analytics will be used consistently by decision makers. Most companies noted several difficulties applying analytical insights — not using analytics to drive strategic decisions, uncertainty about how to apply analytics and failure to act on insights. Over the years, access to useful data has continued to increase. As the volume and complexity of data grows at exponential rates, companies wrestle with how to turn the data into useful insights that can guide the business. While technology is a critical aspect, developing strong data science capabilities within the organization — deep knowledge in statistics, software development programming and of the specific domain of the problem to be solved — is equally important. A data scientist is in high demand nowadays, and it is not easy to hire and retain high-caliber talent. Ultimately, expanding data science capabilities should closely follow the executives’ change of mindset in how to make decisions based on data and algorithms. Article written by Werther Krause Image credit by Getty Images, Caiaimage, Martin Barraud Want more? For Job Seekers | For Employers | For Influencers
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