TagMachine Learning

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.

Intelligent Automation: The Next Frontier in Finance Transformation

Advancements in technologies and the exponential growth of data are presenting massive opportunities for the finance function to embrace intelligent automation, aggregate and analyze data from disparate sources and ease reporting and analysis.

Today, we are witnessing technology evolve at different rates, providing a spectrum of capabilities ranging from simple, rules-based and deterministic process automation to smart machines that can learn and adapt. Thus, the potential to deploy automation in the finance function is significant.

Intelligent Automation as the Way Forward

To meet today’s finance challenges, finance chiefs have to embrace digital transformation, harness new technologies to supplement or automate the tasks undertaken by knowledge workers, drive better process effectiveness and cost efficiencies in ways not previously possible.

Process automation is not a new thing. Automation has for a very long time proven itself as an operational efficiency driver, quite a number of organizations have successfully automated their Order-to-Cash, Procure-to-Pay, Record-to-Report and Cash Management processes and achieved their stated objectives.

However, in today’s digitally-enabled environment, businesses have to think beyond basic automation and embark on intelligent automation.

That is transition from automation of basic tasks that are administrative, repetitive, and mostly transactional within a workflow to also utilizing more advanced technologies such as AI and ML to analyze unstructured or complex information.

Important to note though is that digital transformation is not simply implementing new technologies alone, but also investing significant time to understand the art of the possible. In other words, developing a deeper understanding of the “why” and “what” of automation.

With intelligent automation, repetitive and rules-based tasks are automated, workflows are streamlined, and humans are freed up to spend more time on value-adding tasks or projects that require critical thinking, problem solving and collaboration capabilities.

Also, machines augment human skills and capabilities to deliver new business solutions that would not otherwise be possible.

Intelligent automation thrives when it’s augmented with people to drive better outcomes as humans have to educate machines the reasoning steps necessary for transforming raw data into valuable and actionable insights.

Adopting Intelligent Automation

Prior embarking on this journey, it is critical to have a thorough understanding of the intelligent automation technology landscape.

At the lower end of intelligent automation spectrum is Robotics Process Automation (RPA) which many finance executives are familiar and comfortable with.

RPA systems act as a virtual agent to execute tasks and imitate the same manual path through an application would take using a combination of user interface interaction or descriptor technologies. They are good solutions for aggregating data, performing basic analysis and then visualizing the data.

The substantial benefits of an efficient RPA process makes the technology very attractive to finance teams. Its best applications are generally highly manual, transactional and rules-based processes that have a low exception rate and are subject to high operational risk, for example, accounts payables/receivables, claims processing, invoice processing, regulatory reporting, journal processing, periodic accounting books closure, contract/SLA compliance and account reconciliations.

RPA technology allows accounting and finance teams to configure computer software to reason, collect and extract knowledge, recognize patterns, learn and adapt to new situations or environments. Think of this as your everyday excel macros but at a more advanced level.

Another added advantage of RPA is that it helps finance functions achieve cost efficiencies by lowering the Full Time Equivalent (FTE) costs.

Successfully implemented, a robot or computer software program reduces the number of full time personnel required to run the finance function. Also, the system can be configured to perform tasks during hours of the day humans are not available.

More sophisticated than RPA systems are Machine Learning (ML) and Natural Language Processing (NLP) systems. ML involves computers improving their performance as a result of being exposed to large datasets and developing learning capabilities by themselves without the need to follow explicitly programmed instructions.

The smart machine automatically discovers patterns in data and uses these patterns to make predictions. The more transaction data a machine learning system processes, the better its predictions are expected to become, to the point where it can predict situations just before they actually happen. Machine learning capabilities can be used to enhance sales forecasting and fraudulent transaction detection.

On the other hand, NLP is a field in which computers process and interpret human language by. Applications include speech recognition, sentiment analysis and also analysis of large sets of information such as legal documentation.

NLP technology has the capacity to organize and structure data in real-time on a large scale from a multiple sources and can facilitate an employee’s work by carrying out demanding tasks such as going through client information to single out discrepancies or summarizing lengthy financial regulation documents.

Accounting and finance teams can leverage NLP to explore unstructured data to gather market intelligence, comply with regulations or provide procurement surveillance. For example, NLP queries can identify possible incremental sales/purchase orders and flag out any potential red zones.

In addition to the above, when combined with other digital technologies such as RPA and machine learning, NLP can help finance organizations make accurate customer and pricing decisions.

For instance, RPA can help aggregate all the required customer information from multiple systems and build customer profiles based on a scoring model. NLP technology analyzes and interprets the structured data, prepares the reports in an easily readable format, then communicates relevant customer pricing insights in a narrative form.

Establish a Small Business Use Case

As much as understanding the intelligent automation technology landscape is critical, this alone is not enough. You also need to build a coherent business case for adopting intelligent automation.

One of the hurdles faced by finance executives when trying to implement new technologies is lack of buy-in and support from other senior executives. So often these initiatives are greeted with less enthusiasm and labelled costly and time-consuming investments.

Instead of initially embarking on wide-scale deployments, finance chiefs are advised to first start with the discovery mode. This involves selecting an individual process or processes for automation and use these as a yardstick for success.

The rationale behind starting small is that you want establish quick wins and business use case, tell a compelling story, then build on that success.

There are certain factors that need to be considered when identifying the process candidates for automation and these include – the amount of time spent on each activity, the number of steps or people involved as well as what systems already exist to perform some of these tasks.

If an activity or process requires more time to complete, is labour intensive and has many steps to follow, then automation might be worth the investment.

Regarding systems already in place to perform some of these tasks, it is important to consider if there are any other alternatives for achieving process efficiency that the organization is bypassing and that could yield better outcomes.

If the results of the pilot program are evident enough and positive in terms of the amount of time savings, the level of operational costs reduction, better outcomes and what the business is able to achieve now that wasn’t possible before intelligent automation, then it becomes less of a daunting task to secure buy-in and support from the project sponsors.

Organizational Culture and Communication are Key Ingredients 

The tendency for employees to resist change can derail a move towards wide-scale deployment of intelligent automation. This is often the case if employees have concrete belief that their jobs are being taken over by machines leaving them redundant.

With intelligent automation, smart employees are freed-up from monotonous, non-value-adding work, and empowered to focus more on higher value work and tasks that require creativity, critical thinking and judgment to achieve increased individual productivity and greater employee satisfaction.

Thus, in order to secure employee buy-in, business leaders must be proactive in commencing this process. They must engage employees and communicate the change and benefits of new technologies, offer to retrain or redeploy those affected and provide a clear path towards new roles as well as prepare employees to work alongside automated processes.

To be successful, change strategies should be established and reinforce the connection between intelligent automation and ongoing employee development, and the ability of new technology to augment existing roles, while also giving rise to new and engaging ones.

Reinforce Collaboration Between Business and Technology Teams

A significant number of new technology failures are attributed to the lack of collaboration between business and IT teams. Many business leaders are often under the incorrect assumption that adoption of intelligent automation systems does not require extensive IT support.

As a result, they tend to ignore the input of these specialists leading to immense project failures. The opposite also holds true. In the case of IT-extensive projects, IT leaders often undermine the contribution of business teams and fail to take process nuances into consideration.

To be successful and yield better outcomes, intelligent automation should be a combined effort between business subject matter specialists and technologists with an operating model that defines the roles and responsibilities of each player.

Business subject matter experts play the critical role of defining operational requirements, leading process design initiatives and monitoring performance, while technologists focus on ensuring effective data security and governance, systems integration as well as monitoring identity and access and control.

Intelligent automation should be tackled as part of a bigger digital transformation initiative contrary to being delivered in isolation. Senior executives, leadership teams and employees must all share an integrated automation vision, commit to its success, and develop measurable goals across the organization against which performance can be assessed.

As technology evolves at an accelerated pace, finance leaders have no choice but to concede that automation in all its forms is already playing a role in their organization’s future. Aligning technology investments to customers’ needs and business outcomes is now a critical endeavor than before.

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