From Risk to Strategy : Embracing the Technology Shift

The role of the risk manager has always been to understand and manage threats to a given business. In theory, this involves a very broad mandate to capture all possible risks, both current and future. In practice, however, some risk managers are assigned to narrower, siloed roles, with tasks that can seem somewhat disconnected from key business objectives.

Amidst a changing risk landscape and increasing availability of technological tools that enable risk managers to do more, there is both a need and an opportunity to move toward that broader risk manager role. This need for change – not only in the risk manager’s role, but also in the broader approach to organizational risk management and technological change – is driven by five factors.

Marsh Ex 1

The rapid pace of change has many C-suite members questioning what will happen to their business models. Research shows that 73 percent of executives predict significant industry disruption in the next three years (up from 26 percent in 2018). In this challenging environment, risk managers have a great opportunity to demonstrate their relevance.

USING NEW TOOLS TO MANAGE RISKS

Emerging technologies present compelling opportunities for the field of risk management. As discussed in our 2017 report, the three levers of data, analytics, and processes allow risk professionals a framework to consider technology initiatives and their potential gains. Emerging tools can support risk managers in delivering a more dynamic, in-depth view of risks in addition to potential cost-savings.

However, this year’s survey shows that across Asia-Pacific, risk managers still feel they are severely lacking knowledge of emerging technologies across the business. Confidence scores were low in all but one category, risk management information systems (RMIS). These scores were only marginally higher for respondents in highly regulated industries (financial services and energy utilities), underscoring the need for further training across all industries.

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When it comes to technology, risk managers should aim for “digital fluency, a level of familiarity that allows them to

  • first determine how technologies can help address different risk areas,
  • and then understand the implications of doing so.

They need not understand the inner workings of various technologies, as their niche should remain aligned with their core expertise: applying risk technical skills, principles, and practices.

CULTIVATING A “DIGITAL-FIRST” MIND-SET

Successful technology adoption does not only present a technical skills challenge. If risk function digitalization is to be effective, risk managers must champion a cultural shift to a “digital-first” mindset across the organization, where all stakeholders develop a habit of thinking about how technology can be used for organizational benefit.

For example, the risk manager of the future will be looking to glean greater insights using increasingly advanced analytics capabilities. To do this, they will need to actively encourage their organization

  • to collect more data,
  • to use their data more effectively,
  • and to conduct more accurate and comprehensive analyses.

Underlying the risk manager’s digitalfirst mind-set will be three supporting mentalities:

1. The first of these is the perception of technology as an opportunity rather than a threat. Some understandable anxiety exists on this topic, since technology vendors often portray technology as a means of eliminating human input and labor. This framing neglects the gains in effectiveness and efficiency that allow risk managers to improve their judgment and decision making, and spend their time on more value-adding activities. In addition, the success of digital risk transformations will depend on the risk professionals who understand the tasks being digitalized; these professionals will need to be brought into the design and implementation process right from the start. After all, as the Japanese saying goes, “it is workers who give wisdom to the machines.” Fortunately, 87 percent of PARIMA surveyed members indicated that automating parts of the risk manager’s job to allow greater efficiency represents an opportunity for the risk function. Furthermore, 63 percent of respondents indicated that this was not merely a small opportunity, but a significant one (Exhibit 6). This positive outlook makes an even stronger statement than findings from an earlier global study in which 72 percent of employees said they see technology as a benefit to their work

2. The second supporting mentality will be a habit of looking for ways in which technology can be used for benefit across the organization, not just within the risk function but also in business processes and client solutions. Concretely, the risk manager can embody this culture by adopting a data-driven approach, whereby they consider:

  • How existing organizational data sources can be better leveraged for risk management
  • How new data sources – both internal and external – can be explored
  • How data accuracy and completeness can be improved

“Risk managers can also benefit from considering outside-the-box use cases, as well as keeping up with the technologies used by competitors,” adds Keith Xia, Chief Risk Officer of OneHealth Healthcare in China.

This is an illustrative rather than comprehensive list, as a data-driven approach – and more broadly, a digital mind-set – is fundamentally about a new way of thinking. If risk managers can grow accustomed to reflecting on technologies’ potential applications, they will be able to pre-emptively spot opportunities, as well as identify and resolve issues such as data gaps.

3. All of this will be complemented by a third mentality: the willingness to accept change, experiment, and learn, such as in testing new data collection and analysis methods. Propelled by cultural transformation and shifting mind-sets, risk managers will need to learn to feel comfortable with – and ultimately be in the driver’s seat for – the trial, error, and adjustment that accompanies digitalization.

MANAGING THE NEW RISKS FROM EMERGING TECHNOLOGIES

The same technological developments and tools that are enabling organizations to transform and advance are also introducing their own set of potential threats.

Our survey shows the PARIMA community is aware of this dynamic, with 96 percent of surveyed members expecting that emerging technologies will introduce some – if not substantial – new risks in the next five years.

The following exhibit gives a further breakdown of views from this 96 percent of respondents, and the perceived sufficiency of their existing frameworks. These risks are evolving in an environment where there are already questions about the relevance and sufficiency of risk identification frameworks. Risk management has become more challenging due to the added complexity from rapid shifts in technology, and individual teams are using risk taxonomies with inconsistent methodologies, which further highlight the challenges that risk managers face in managing their responses to new risk types.

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To assess how new technology in any part of the organization might introduce new risks, consider the following checklist :

HIGH-LEVEL RISK CHECKLIST FOR EMERGING TECHNOLOGY

  1. Does the use of this technology cut across existing risk types (for example, AI risk presents a composite of technology risk, cyber risk, information security risk, and so on depending on the use case and application)? If so, has my organization designated this risk as a new, distinct category of risk with a clear definition and risk appetite?
  2. Is use of this technology aligned to my company’s strategic ambitions and risk appetite ? Are the cost and ease of implementation feasible given my company’s circumstances?
  3. Can this technology’s implications be sufficiently explained and understood within my company (e.g. what systems would rely on it)? Would our use of this technology make sense to a customer?
  4. Is there a clear view of how this technology will be supported and maintained internally, for example, with a digitally fluent workforce and designated second line owner for risks introduced by this technology (e.g. additional cyber risk)?
  5. Has my company considered the business continuity risks associated with this technology malfunctioning?
  6. Am I confident that there are minimal data quality or management risks? Do I have the high quality, large-scale data necessary for advanced analytics? Would customers perceive use of their data as reasonable, and will this data remain private, complete, and safe from cyberattacks?
  7. Am I aware of any potential knock-on effects or reputational risks – for example, through exposure to third (and fourth) parties that may not act in adherence to my values, or through invasive uses of private customer information?
  8. Does my organization understand all implications for accounting, tax, and any other financial reporting obligations?
  9. Are there any additional compliance or regulatory implications of using this technology? Do I need to engage with regulators or seek expert advice?
  10. For financial services companies: Could I explain any algorithms in use to a customer, and would they perceive them to be fair? Am I confident that this technology will not violate sanctions or support crime (for example, fraud, money laundering, terrorism finance)?

SECURING A MORE TECHNOLOGY-CONVERSANT RISK WORKFORCE

As risk managers focus on digitalizing their function, it is important that organizations support this with an equally deliberate approach to their people strategy. This is for two reasons, as Kate Bravery, Global Solutions Leader, Career at Mercer, explains: “First, each technological leap requires an equivalent revolution in talent; and second, talent typically becomes more important following disruption.”

While upskilling the current workforce is a positive step, as addressed before, organizations must also consider a more holistic talent management approach. Risk managers understand this imperative, with survey respondents indicating a strong desire to increase technology expertise in their function within the next five years.

Yet, little progress has been made in adding these skills to the risk function, with a significant gap persisting between aspirations and the reality on the ground. In both 2017 and 2019 surveys, the number of risk managers hoping to recruit technology experts has been at least 4.5 times the number of teams currently possessing those skills.

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EMBEDDING RISK CULTURE THROUGHOUT THE ORGANIZATION

Our survey found that a lack of risk management thinking in other parts of the organization is the biggest barrier the risk function faces in working with other business units. This is a crucial and somewhat alarming finding – but new technologies may be able to help.

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As technology allows for increasingly accurate, relevant, and holistic risk measures, organizations should find it easier to develop risk-based KPIs and incentives that can help employees throughout the business incorporate a risk-aware approach into their daily activities.

From an organizational perspective, a first step would be to describe risk limits and risk tolerance in a language that all stakeholders can relate to, such as potential losses. Organizations can then cascade these firm-wide risk concepts down to operational business units, translating risk language into tangible and relevant incentives that encourages behavior that is consistent with firm values. Research shows that employees in Asia want this linkage, citing a desire to better align their individual goals with business goals.

The question thus becomes how risk processes can be made an easy, intuitive part of employee routines. It is also important to consider KPIs for the risk team itself as a way of encouraging desirable behavior and further embedding a risk-aware culture. Already a majority of surveyed PARIMA members use some form of KPIs in their teams (81 percent), and the fact that reporting performance is the most popular service level measure supports the expectation that PARIMA members actively keep their organization informed.

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At the same time, these survey responses also raise a number of questions. Forty percent of organizations indicate that they measure reporting performance, but far fewer are measuring accuracy (15 percent) or timeliness (16 percent) of risk analytics – which are necessary to achieve improved reporting performance. Moreover, the most-utilized KPIs in this year’s survey tended to be tangible measures around cost, from which it can be difficult to distinguish a mature risk function from a lucky one.

SUPPORTING TRANSFORMATIONAL CHANGE PROGRAMS

Even with a desire from individual risk managers to digitalize and complement organizational intentions, barriers still exist that can leave risk managers using basic tools. In 2017, cost and budgeting concerns were the single, standout barrier to risk function digitalization, chosen by 67 percent of respondents, well clear of second placed human capital concerns at 18 percent. This year’s survey responses were much closer, with a host of ongoing barriers, six of which were cited by more than 40 percent of respondents.

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Implementing the nuts and bolts of digitalization will require a holistic transformation program to address all these barriers. That is not to say that initiatives must necessarily be massive in scale. In fact, well-designed initiatives targeting specific business problems can be a great way to demonstrate success that can then be replicated elsewhere to boost innovation.

Transformational change is inherently difficult, in particular where it spans both technological as well as people dimensions. Many large organizations have generally relied solely on IT teams for their “digital transformation” initiatives. This approach has had limited success, as such teams are usually designed to deliver very specific business functionalities, as opposed to leading change initiatives. If risk managers are to realize the benefits of such transformation, it is incumbent on them to take a more active role in influencing and leading transformation programs.

Click here to access Marsh’s and Parima’s detailed report

Data Search and Discovery in Insurance – An Overview of AI Capabilities

Historically, the insurance industry has collected vast amounts of data relevant to their customers, claims, and so on. This can be unstructured data in the form of PDFs, text documents, images, and videos, or structured data that has been organized for big data analytics.

As with other industries, the existence of such a trove of data in the insurance industry led many of the larger firms to adopt big data analytics and techniques to find patterns in the data that might reveal insights that drive business value.

Any such big data applications may require several steps of data management, including collection, cleansing, consolidation, and storage. Insurance firms that have worked with some form of big data analytics in the past might have access to structured data which can be ingested by AI algorithms with little additional effort on the part of data scientists.

The insurance industry might be ripe for AI applications due to the availability of vast amounts of historical data records and the existence of large global companies with the resources to implement complex AI projects. The data being collected by these companies comes from several channels and in different formats, and AI search and discovery projects in the space require several initial steps to organize and manage data.

Radim Rehurek, who earned his PhD in Computer Science from the Masaryk University Brno and founded RARE Technologies, points out:

« A majority of the data that insurance firms collect is likely unstructured to some degree. This poses several challenges to insurance companies in terms of collecting and structuring data, which is key to the successful implementation of AI systems. »

Giacomo Domeniconi, a post-doctoral researcher at IBM Watson TJ Research Center and Adjunct Professor for the course “High-Performance Machine Learning” at New York University, mentions structuring the data as the largest challenge for businesses:

“Businesses need to structure their information and create labeled datasets, which can be used to train the AI system. Yet creating this labeled dataset might be very challenging apply AI and in most cases would involve manually labeling a part of the data using the expertise of a specialist in the domain.”

Businesses face many challenges in terms of collecting and structuring their data, which is key to the successful implementation of AI systems. An AI application is only as good as the data it consumes.

Natural language processing (NLP) and machine learning models often need to be trained on large volumes of data. Data scientists tweak these models to improve their accuracy.

This is a process that might last several months from start to finish, even in cases where the model is being taught relatively rudimentary tasks, such as identifying semantic trends in an insurance company’s internal documentation.

Most AI systems necessarily require the data to be input into an AI system in a structured format. Businesses would need to collect, clean, and organize their data to meet these requirements.

Although creating NLP and machine learning models to solve real-world business problems is by itself a challenging task, this process cannot be started without a plan for organizing and structuring enough data for these models to operate at reasonable accuracy levels.

Large insurance firms might need to think about how their data at different physical locations across the world might be affected by local data regulations or differences in data storage legacy systems at each location. Even with all the data being made accessible, businesses would find that data might still need to be scrubbed to remove any incorrect, incomplete, improperly formatted, duplicate, or outlying data. Businesses would also find that in some cases regulations might mandate the signing of data sharing agreements between the involved parties or data might need to be moved to locations where it can be analyzed. Since the data is highly voluminous, moving the data accurately can prove to be a challenge by itself.

InsIA

Click here to access Iron Mountain – Emerj’s White Paper

 

EIOPA reviews the use of Big Data Analytics in motor and health insurance

Data processing has historically been at the very core of the business of insurance undertakings, which is rooted strongly in data-led statistical analysis. Data has always been collected and processed to

  • inform underwriting decisions,
  • price policies,
  • settle claims
  • and prevent fraud.

There has long been a pursuit of more granular data-sets and predictive models, such that the relevance of Big Data Analytics (BDA) for the sector is no surprise.

In view of this, and as a follow-up of the Joint Committee of the European Supervisory Authorities (ESAs) cross-sectorial report on the use of Big Data by financial institutions,1 the European Insurance and Occupational Pensions Authority (EIOPA) decided to launch a thematic review on the use of BDA specifically by insurance firms. The aim is to gather further empirical evidence on the benefits and risks arising from BDA. To keep the exercise proportionate, the focus was limited to motor and health insurance lines of business. The thematic review was officially launched during the summer of 2018.

A total of 222 insurance undertakings and intermediaries from 28 jurisdictions have participated in the thematic review. The input collected from insurance undertakings represents approximately 60% of the total gross written premiums (GWP) of the motor and health insurance lines of business in the respective national markets, and it includes input from both incumbents and start-ups. In addition, EIOPA has collected input from its Members and Observers, i.e. national competent authorities (NCAs) from the European Economic Area, and from two consumers associations.

The thematic review has revealed a strong trend towards increasingly data-driven business models throughout the insurance value chain in motor and health insurance:

  • Traditional data sources such as demographic data or exposure data are increasingly combined (not replaced) with new sources like online media data or telematics data, providing greater granularity and frequency of information about consumer’s characteristics, behaviour and lifestyles. This enables the development of increasingly tailored products and services and more accurate risk assessments.

EIOPA BDA 1

  • The use of data outsourced from third-party data vendors and their corresponding algorithms used to calculate credit scores, driving scores, claims scores, etc. is relatively extended and this information can be used in technical models.

EIOPA BDA 2

  • BDA enables the development of new rating factors, leading to smaller risk pools and a larger number of them. Most rating factors have a causal link while others are perceived as being a proxy for other risk factors or wealth / price elasticity of demand.
  • BDA tools such as such as artificial intelligence (AI) or machine learning (ML) are already actively used by 31% of firms, and another 24% are at a proof of concept stage. Models based on these tools are often cor-relational and not causative, and they are primarily used on pricing and underwriting and claims management.

EIOPA BDA 3

  • Cloud computing services, which reportedly represent a key enabler of agility and data analytics, are already used by 33% of insurance firms, with a further 32% saying they will be moving to the cloud over the next 3 years. Data security and consumer protection are key concerns of this outsourcing activity.
  • Up take of usage-based insurance products will gradually continue in the following years, influenced by developments such as increasingly connected cars, health wearable devices or the introduction of 5G mobile technology. Roboadvisors and specially chatbots are also gaining momentum within consumer product and service journeys.

EIOPA BDA 4

EIOPA BDA 5

  • There is no evidence as yet that an increasing granularity of risk assessments is causing exclusion issues for high-risk consumers, although firms expect the impact of BDA to increase in the years to come.

In view of the evidence gathered from the different stake-holders, EIOPA considers that there are many opportunities arising from BDA, both for the insurance industry as well as for consumers. However, and although insurance firms generally already have in place or are developing sound data governance arrangements, there are also risks arising from BDA that need to be further addressed in practice. Some of these risks are not new, but their significance is amplified in the context of BDA. This is particularly the case regarding ethical issues with the fairness of the use of BDA, as well as regarding the

  • accuracy,
  • transparency,
  • auditability,
  • and explainability

of certain BDA tools such as AI and ML.

Going forward, in 2019 EIOPA’s InsurTech Task Force will conduct further work in these two key areas in collaboration with the industry, academia, consumer associations and other relevant stakeholders. The work being developed by the Joint Committee of the ESAs on AI as well as in other international fora will also be taken into account. EIOPA will also explore third-party data vendor issues, including transparency in the use of rating factors in the context of the EU-US insurance dialogue. Furthermore, EIOPA will develop guidelines on the use of cloud computing by insurance firms and will start a new workstream assessing new business models and ecosystems arising from InsurTech. EIOPA will also continue its on-going work in the area of cyber insurance and cyber security risks.

Click here to access EIOPA’s detailed Big Data Report

The evolution of GRC

Attitudes to governance, risk and compliance (GRC) activities are changing among Tier 1 financial institutions. The need to keep up with rapid regulatory change, and the pressure of larger, more publicised penalties dealt out by regulators in recent years have prompted an evolution in how risk is viewed and managed. Financial firms also face an increasingly volatile market environment that requires them to remain nimble – not just to survive, but to thrive.

As a result of these market developments, GRC is now seen, rather than as one strand of the business, as a far more integrated activity with many companies realigning resources around the ‘three lines of defence’ model. GRC is increasingly being treated as an enterprise-wide responsibility by organisations that are successfully navigating these challenging times for global financial markets. This shift in attitudes is also leading to a rethink in relation to the tools used by all three lines of defence to participate in GRC activities. Some are exploring more innovative solutions to support and engage infrequent users – particularly those in the first line of defence (1LoD). The more intuitive design of such tools enables these users to take a more active role in risk-aware decision-making.

These and other innovations promise to bring greater effectiveness and efficiency to an area into which firms have channelled increasing levels of resource in recent years but are struggling to keep up with demand. A recent survey carried out by Risk.net and IBM found that risk and compliance professionals acknowledge the limitations of existing operational risk and regulatory compliance tools and systems to satisfy current and future GRC requirements. The survey polled 106 senior risk, compliance, audit and legal executives at financial firms including banks (53%), insurance companies (21%) and asset management firms (12%). The results revealed that nearly one third of these respondents remain unimpressed with the effectiveness of their organisation’s ability to cope with the complexity and pace of regulatory change. Nearly half gave a similar response regarding their organisation’s efficiency in this area.

With these issues in mind, many of the firms surveyed have started to explore user-experience needs more deeply and combine the results with artificial intelligence (AI) capabilities to further develop GRC systems and processes. These capabilities are designed to enhance compliance systems and processes and make them more intuitive for all. As such, user-experience research and design has become a key consideration for organisations wanting to ensure employees across all three lines of defence can participate more fully in GRC activities. In addition, AI-powered tools can help 1LoD business users better manage risk and ensure compliance by increasing the efficiency and effectiveness of these GRC systems and processes. The survey shows that, while some organisations are already developing these types of solutions, there is still room for greater understanding of the benefits of new and innovative forms of technology throughout the global financial markets. For instance, nearly half of respondents to the survey, when asked about the benefits of AI for GRC activities, were unsure of the potential time efficiencies such tools can bring. More than one-quarter were undecided on whether AI would free up employees’ time to focus on more strategic tasks.

Many organisations are still considering how to move forward in this area, but it will be those that truly embrace user-focused tools and leverage innovative technologies such as AI and advanced analytics to increase efficiencies that can expect to reap the rewards of successfully managing regulatory change and tackling market volatility.

LoD

Current and Future Applications

The survey highlights that financial firms already recognise that these solutions can be used to more efficiently manage the regulatory change process. For example, AI-based solutions can provide smart alerts to highlight the most relevant regulatory changes – 35% of survey respondents see AI as offering the biggest potential improvements in this area.

Improving the speed and accuracy of classification and reporting of information – for example, in relation to loss events – was another area identified for its high AI potential. Nearly one-third of respondents (31%) see possibilities for improvement of current GRC processes in this area. Some financial firms have already started to reap the rewards of this type of approach. Larger firms are typically ahead of the game with such developments, often having more resources to put into research and development. Out of the 13% of larger firms that have seen a decrease in GRC resources over the past year, one-third of survey respondents attribute that to “tools and automation improvements”.

Similarly, 44% of those polled work at organisations already making improvements to improve end-to-end time and user experience in relation to GRC processes and tools. A further 19% plan to do this in the next 12 months and, in line with this, 64% of survey respondents expect their firm’s GRC resources to increase over the next 24 months (see figure 8). While it is not clear from the survey whether these additional resources will be specifically directed towards AI, more than 80% of respondents work at organisations currently considering AI for a range of GRC activities.

The most popular use of AI among financial firms is to improve the speed and/or accuracy of classification and reporting information, such as loss events – 19% of respondents say their organisation is currently using AI for this purpose, with 81% currently considering this type of use. Such events happen fairly infrequently, so training employees to classify and enter such information can be time consuming, but incorrect classification can have a real impact on data quality. By using natural language processing (NLP) tools to understand and categorise loss events automatically, organisations can streamline the time and resources required to train employees to collect and manage this information.

According to the survey, 83% of respondents are also currently considering the use of AI tools to develop smart alerts that will highlight any new rules or updates to existing regulations, helping financial firms manage regulatory change more efficiently. Many organisations already receive an overwhelming amount of alerts every day relating to new rules or changes, but some or all of these changes may not actually apply to their businesses. AI can be used to tailor these alerts to ensure compliance teams only receive the most relevant alerts. Using NLP to create this mechanism can be the difference between sorting through 100 alerts in one day and receiving one smart alert that has been identified by an AI-powered solution.

Control mapping is another area to which AI can add value. When putting controls in place relating to specific obligations within a regulation, for example, compliance teams can either create a new control or, using NLP, detect whether there is already an applicable control in place that can be mapped to record the organisation’s compliance with the rule. This reduces the amount of time spent by the team reading and understanding new legislation or rule changes to determine applicability, as well as improving accuracy and reducing duplicate controls.

Click here to access IBM’s White Paper

How to Transform Your CX Strategy with AI

Consumers have more ways than ever to communicate with the brands they buy — be it through private chat or in public on social media sites such as Twitter. If a conversation conveys a negative sentiment, it can be detrimental if it’s not addressed quickly. Many companies are leaning on early stage AI tools for help.

Companies can use artificial intelligence in customer service to build a brand that’s associated with excellent customer experience (CX). This is critically important in an era in which consumers can easily compare product prices on the web, said Gene Alvarez, a Gartner managing VP, during a September 2018 webinar in which analysts discussed ways artificial intelligence in customer service can drive business growth. « When your price is equal, what’s left? Your customer experience, » Alvarez said. « If you deliver a poor customer experience, they’ll go with the company that delivers a good one. This has created a challenge for organizations trying to take on the behemoths who are doing well with customer experience, with the challenge being scale. »

AI in customer service enables companies to understand what their customers are doing today and to quickly scale CX strategies in response. Chatbots can be deployed relatively quickly to handle customer requests around the clock, while social listening tools can track customer sentiment online to gain insight, identify potential new customers, and take proactive action to protect and grow brands.

With that, AI technologies including text analytics, sentiment analysis, speech analytics and natural language processing all play an increasingly important role in customer experience management. By 2021, 15% of all customer service interactions will be handled by AI — that’s 400% higher than in 2017, according to Gartner.

Where AI for customer service makes sense

With the current hype around AI, companies may rush into projects without thinking about how artificial intelligence can help execute their vision for customer experience — if it’s appropriate at all, Alvarez said.

« Organizations have to ask the question, ‘How will I use AI to build the next component of my vision in terms of execution from a strategy perspective?‘ [and] not just try AI at scattershot approaches, » he said. « Look for moments of truth in the customer experience and say, ‘This is a good place to try [AI] because it aligns with our vision and strategy and the type of customer experience we want to deliver.' »

For example, an extraordinary number of companies have deployed chatbots or virtual assistants or are in the process of deploying them. Twenty-five percent of customer service and support operations will integrate bot technology across their engagement channels by 2020, up from less than 2% in 2017, Gartner reported.

But chatbots certainly aren’t the right choice for all companies. Customers who shop a luxury brand may expect a higher level of personalized customer service; self-service models and chatbots aren’t appropriate for customers who expect their calls to be answered by a person, Alvarez said.

And it’s no secret that virtual agents haven’t delivered the success companies hoped for with AI in customer service, said Brian Manusama, Gartner research director, in the webinar. All the experimentation with chatbots and virtual agents has, in some cases, hurt the customer experience instead of contributing to it. Companies have a long way to go to learn which technologies to use for the right use cases, he said.

« Companies really getting into [AI for CX] are disproportionally getting rewarded for it while companies that don’t do well with it are getting disproportionally punished for it, » Manusama said.

Match the product to the CX

The first step in choosing software for artificial intelligence in customer service is to understand that there is no single tool that works for every customer in every scenario, said Whit Andrews, an analyst at Gartner. For example, a customer who buys an inexpensive product may be fine interacting with a chatbot about that purchase, but not other types of purchases, he said.

« You have to identify the people who want to work with a chatbot and be realistic about the fact that if someone says they’d rather work with a chatbot, they might mean that for one situation but not another, » Andrews said.

To put a finer point on it, Jessica Ekholm, a Gartner research VP, advised companies to « pick the right battles » with AI tools by examining where the customer pain points are and developing a CX strategy that uses artificial intelligence in customer service strategically.

Cohesive AI in CRM strategies requires a singular 360 view

AI in CRM today is like mobile in the 1990s and social media channels in the 2000s, according to Jeff Nicholson, vice president of CRM product marketing at Pegasystems: It seems everyone wants a piece of the pie.

« Companies are anxious to deploy AI, so they try a little over here, maybe a little over there, just to keep up, » Nicholson said. « Before you know it, you’ve created another stack of silos across the enterprise. » To succeed with AI in CRM, he explained, organizations need a holistic strategy that ties AI across all departments and customer-facing channels. Using a channel-less approach, companies can avoid a disjointed user experience and very frustrated customers and instead take advantage of the full power of their data.

At the center of an experience platform where the AI brain lives, businesses should react in real time with chatbots, mobile apps, webpages, on the phone or in person at the store, Nicholson said. « This singular AI brain approach, » he noted, « allows [companies] to extend predictive intelligence to all other channels, without having to start from scratch for each new interface that comes along. »

Pega is ahead of the curve, he claimed, with the Customer Decision Hub, which serves as the central AI brain across all its CRM applications — from marketing to sales to customer service. « We’ve seen our clients leverage it to redefine how they engage with customers to turn their businesses around, » he reported, citing two examples: Royal Bank of Scotland raised its Net Promoter Score by 18 points across its 17 million customers, while Sprint overcame industry-high turnover rates and realized a 14% increase in customer retention.

SAP‘s Leonardo AI and machine learning tool can help companies with their digital transformation and customer engagement strategies. It also helps organizations address key technologies, including machine learning, internet of things, blockchain, big data and analytics. SAP Hybris follows an organic development approach to AI in CRM, using data scientists and development teams across all areas of the business. Find out more about Pega, Oracle, Salesforce and SAP AI systems in the following chart:

content_management-cohesive_ai_crm

AI strategy comes first, then AI tools second

For all the talk and focus on technological innovations that have disrupted and changed business processes, what has really changed the most during the technology revolution of the last 20 years is the customer.

Customers enter the buying process equipped with more information and perspective than ever before. From a bygone era of personal experiences and finite wells of word-of-mouth reviews, customers are now engaged with millions of other customer experiences through social media and online reviews, as well as unlimited resources, when making product or service comparisons. This paradigm shift has left marketers, sellers and service teams playing catch-up to develop strategies combined with technology to better equip themselves and capitalize on the customer’s experience.

Companies and brands hope that infusing a CRM AI strategy within their business will help balance the scales when interacting with customers. No business wants to enter a negotiation knowing less than its counterpart. And based on the marketing churn of most software companies, it’s easy to assume that many businesses have already implemented AI into their marketing and sales processes, and those that haven’t will be left in the dust.

« If the AI-driven environment can learn enough and be trained correctly, it can deliver better customers that are more relevant and timely and on the right device and right promotion, » Forrester Research principal analyst Joe Stanhope said. But AI in customer experience comes with a caveat. « It will play out as a multiyear process, and it’s not necessarily a technology problem, » Stanhope warned. « It’s more of a change of management and a cultural issue. »

Delivering on customer expectations

The importance of implementing an AI strategy into the customer experience isn’t lost on business executives. According to Bluewolf’s latest « State of Salesforce » annual report, 63% of C-level executives are counting on AI to improve the customer experience. A 2017 IBM study also indicated that 26% of respondents expect AI to have a significant impact on customer experience today, while 47% expect the impact to be within the next two or three years.

Chief marketing officers set sights on CRM AI

In the next two to three years, one-third of organizations plan to implement AI technologies, according to a 2017 study conducted by the IBM Institute for Business Value. Yet some organizations surveyed have already implemented AI technologies and intend to license more.

IBM‘s « Cognitive Catalysts: Reinventing Enterprises and Experiences With Artificial Intelligence » divided chief marketing officers into three groups of respondents:

  • Reinventors are AI-enabled with significant future investment,
  • Tacticians are AI-enabled with minimal future investment
  • and Aspirationals are planning their first AI-enabled investment.

In the next two years, 63% of reinventors, 48% of tacticians and 70% of aspirationals plan to implement AI technologies to help reinvent the customer experience, demonstrating that an AI implementation needs to start at the executive level and work its way down to the user base.

By then, there should be a substantial increase in use cases for AI customer service — not just in the product servicing sense, but also in the marketing and sales stages of the customer experience. « Buyers expect something different these days; they come in much more educated, » said Dana Hamerschlag, chief product officer at sales consultancy Miller Heiman Group. « The trick and challenge around AI is how do you leverage this powerful machine to tell you that process, rather than just give you the outcome data. »

The significance of gaining an edge on the customer extends to marketing, too, with a CRM AI strategy that can solve prospecting concerns. According to the Bluewolf’s annual report, 33% of marketing organizations that are increasing AI capabilities within the next year expect the technology to have the greatest impact on the ability to qualify prospects. « You need to enter a conversation with a customer understanding their context, » Hamerschlag advised. « You need to be informed and, with AI, not only [of] who they are but what they have looked at, what they are reading on my site, what emails they have opened. »

Technology based on strategy

The emphasis on customer experience has provided an outlet for AI’s potential. Companies are beginning to explore ways that a CRM AI strategy and the subsequent technologies can help improve customer service and experience.

Personalized photo books company Chatbooks Inc. helps customers convert photos on their phone or tablet into physical photo albums. It uses customer service reps to help customers complete the process and started implementing chatbots to streamline the customer service process. « It’s important that the customer service team is there when customers need them, » said Angel Brockbank, director of customer experience at Chatbooks, based in Provo, Utah.

The initial chatbot established by Chatbooks, created using Helpshift, a San Francisco-based customer service platform, helps customers create an account and input basic information like name and email. Brockbank said the company has an AI strategy in place and will be implementing another chatbot to help direct customer inquiries to the correct chat agent. « We haven’t done that yet, » she acknowledged, « but it will be helpful and useful for our team. »

This blending of product and experience has created an important need for AI technologies, according to Mika Yamamoto, chief digital marketing officer at SAP. « The technology is only as good as the strategy that goes with it, » Yamamoto said. « Companies have to understand how they want to show up for their customers and what type of customer engagement or experience they’re trying to enable. »

One of the impediments to implementing AI is employee adoption, according to a recent Forrester survey. Among CRM professionals, 28% said that one of the largest challenges to improving CRM last year was gaining user acceptance of new technologies, compared to 20% in 2015, a 40% increase. However, the CRM professionals thought it was easier working with IT to adopt new technologies last year (19%) than it was in 2015 (31%), a near 40% drop.

Still, the increased importance of the customer experience and knowing the customer is the main objective driving an AI strategy and the departmental changes that requires. In the Forrester survey, 64% of CRM professionals said creating a single view of customer data and information is the largest challenge they face when improving CRM capabilities, up from 47% in 2015.

BI0618_AI-impact

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Is Your Company Ready for Artificial Intelligence?

Overview

Companies are rushing to invest in and pursue initiatives that use artificial intelligence (AI). Some hope to find opportunity to transform their business processes and gain competitive advantage and others are concerned about falling behind the technology curve. But the reality is that many AI initiatives don’t work as planned, largely because companies are not ready for AI.

However, it is possible to leverage AI to create real business value. The key to AI success is ensuring the organization is ready by having the basics in place, particularly structured analytics and automation. Other elements of AI readiness include

  • executive engagement and support,
  • data excellence,
  • organizational capabilities,
  • and completion of AI pilots.

Key Takeaways

There is tremendous AI hype and investment. Artificial intelligence is software that can make decisions without explicit instructions for each scenario, including an ability to learn and improve over time. The term “machine learning” is often used interchangeably with AI, but machine learning is just one approach to AI, though it is currently the approach generating the most attention. Today in most business situations where AI is relevant, machine learning is likely to be employed.

The hype around AI is tremendous and has accelerated in the last few years. It is rare to read a business-related article these days that doesn’t mention AI.

The AI hype is being accompanied by massive investments from corporations (like Amazon, Google, and Uber), as well as from venture capital firms.

Because organizations often pursue AI without fully understanding it or having the basics in place, many AI initiatives fail. The AI fervor is causing companies to hurriedly pursue AI. There is a rush to capitalize on AI, but significant frustration when it comes to actually delivering AI success. AI initiatives are often pursued for the wrong reasons and many AI initiatives experience pitfalls. Some key pitfalls are:

  • Expensive partnerships between large companies and startups without results.
  • Impenetrable black box systems.
  • Open source toolkits without programmers to code.

The root cause for these failures often boils down to companies confusing three different topics:

  • automation,
  • structured analytics,
  • and artificial intelligence.

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Despite the challenges, some organizations are experiencing success with AI. While the hype around AI is overblown, there are organizations having success by leveraging AI to create business value, particularly when AI is used for customer support and in the back office.

The key to AI success is first having the basics in place. In assessing AI successes and failures, the presenters drew three conclusions:

  1. There is a huge benefit from first getting the basics right: automation and structured analytics are prerequisites to AI.
  2. The benefits from AI are greater once these basics have been done right.
  3. Organizations are capable of working with AI at scale only when the basics have been done at scale.

GETTING THE BASICS RIGHT

The most important basics for AI are automation and structured analytics.

  • Automation: In most businesses there are many examples of data processes that can be automated. In many of these examples, there is no point having advanced AI if the basics are not yet automated.
  • Structured analytics means applying standard statistical techniques to well-structured data. In most companies there is huge value in getting automation and structured analytics right before getting to more complicated AI.

Examples of how businesses use structured analytics and automation include:

  • Competitor price checking. A retailer created real-time pricing intelligence by automatically scraping prices from competitors’ websites.
  • Small business cash flow lending product. Recognizing the need for small business customers to acquire loans in days, not weeks, a bank created an online lending product built on structured analytics.

BENEFITS WHEN THE BASICS ARE IN PLACE

Once the basics of structured analytics and automation are in place, organizations see more value from AI—when AI is used in specific situations.

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Examples of how adding AI on top of the basics helps improve business results are:

  • New product assortment decisions. Adding AI on top of structured analytics allowed a retailer to predict the performance of new products for which there was no historic data. With this information, the retailer was able to decide whether or not to add the product to the stores.
  • Promotions forecasting. A retailer was able to improve forecasting of promotional sales using AI. Within two months of implementation, machine learning was better than the old forecasts plus the corrections made by the human forecasting team.
  • Customer churn predictions. A telephone company used AI and structured analytics to identify how to keep at-risk customers from leaving.
  • Defect detection. An aerospace manufacturer used AI to supplement human inspection and improve defect detection.

AI AT SCALE AFTER THE BASICS ARE AT SCALE

Once an organization proves it can work with automation and structured analytics at scale, it is ready for AI at scale. Readiness for AI at scale goes beyond completing a few AI pilots in defined but isolated areas of capability; the basics need to be in use across the business.

Before undertaking AI, organizations need to assess their AI readiness. To be successful, organizations need to be ready for AI. Readiness consists of multiple elements, including

  • executive engagement and support,
  • data excellence,
  • organizational capabilities,
  • and an analytical orientation.

Organizations often struggle with data excellence and organizational capabilities.

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The Innovation Game – How Data is Driving Digital Transformation

Technology waits for no one. And those who strike first will have an advantage. The steady decline in business profitability across multiple industries threatens to erode future investment, innovation and shareholder value. Fortunately, the emergence of artificial intelligence (AI) can help kick-start profitability. Accenture research shows that AI has the potential to boost rates of profitability by an average of 38 percent by 2035 and lead to an economic boost of US$14 trillion across 16 industries in 12 economies by 2035.

Driven by these economic forces, the age of digital transformation is in full swing. Today we can’t be “digital to the core” if we don’t leverage all new data sources – unstructured, dark data and thirty party sources. Similarly, we have to take advantage of the convergence of AI and analytics to uncover previously hidden insights. But, with the increasing use of AI, we also have to be responsible and take into account the social implications.

Finding answers to the biggest questions starts with data, and ensuring you are capitalizing on the vast data sources available within your own business. Thanks to the power of AI/machine learning and advanced algorithms, we have moved from the era of big data to the era of ALL data, and that is helping clients create a more holistic view of their customer and more operational efficiencies.

Embracing the convergence of AI and analytics is crucial to success in our digital transformation. Together,

  • AI-powered analytics unlock tremendous value from data that was previously hidden or unreachable,
  • changing the way we interact with people and technology,
  • improving the way we make decisions, and giving way to new agility and opportunities.

While businesses are still in the infancy of tapping into the vast potential of these combined technologies, now is the time to accelerate. But to thrive, we need to be pragmatic in finding the right skills and partners to guide our strategy.

Finally, whenever we envision the possibilities of AI, we should consider the responsibility that comes with it. Trust in the digital era or “responsible AI” cannot be overlooked. Explainable AI and AI transparency are critical, particularly in such areas as

  • financial services,
  • healthcare,
  • and life sciences.

The new imperative of our digital transformation is to balance intelligent technology and human ingenuity to innovate every facet of business and become a smarter enterprise.

The exponential growth of data underlying the strategic imperative of enterprise digital transformation has created new business opportunities along with tremendous challenges. Today, we see organizations of all shapes and sizes embarking on digital transformation. As uncovered in Corinium Digital’s research, the primary drivers of digital transformation are those businesses focused on addressing increasing customer expectations and implementing efficient internal processes.

Data is at the heart of this transformation and provides the fuel to generate meaningful insights. We have reached the tipping point where all businesses recognize they cannot compete in a digital age using analog-era legacy solutions and architectures. The winners in the next phase of business will be those enterprises that obtain a clear handle on the foundations of modern data management, specifically the nexus of

  • data quality,
  • cloud,
  • and artificial intelligence (AI).

While most enterprises have invested in on-premises data warehouses as the backbone of their analytic data management practices, many are shifting their new workloads to the cloud. The proliferation of new data types and sources is accelerating the development of data lakes with aspirations of gaining integrated analytics that can accelerate new business opportunities. We found in the research that over 60% of global enterprises are now investing in a hybrid, multi-cloud strategy with both data from cloud environments such as Microsoft Azure along with existing on-premises infrastructures. Hence, this hybrid, multicloud strategy will need to correlate with their investments in data analytics, and it will become imperative to manage data seamlessly across all platforms. At Paxata, our mission is to give everyone the power to intelligently profile and transform data into consumable information at the speed of thought. To empower everyone, not just technical users, to prepare their data and make it ready for analytics and decision making.

The first step in making this transition is to eliminate the bottlenecks of traditional IT-led data management practices through AI-powered automation.

Second, you need to apply modern data preparation and data quality principles and technology platforms to support both analytical and operational use cases.

Thirdly, you need a technology infrastructure that embraces the hybrid, multi-cloud world. Paxata sits right at the center stage of this new shift, helping enterprises profile and transform complex data types in highvariety, high-volume environments. As such, we’re excited about partnering with Accenture and Microsoft to accelerate businesses with our ability to deliver modern analytical and operational platforms to address today’s digital transformation requirements.

Artificial intelligence is causing two major revolutions simultaneously among developers and enterprises. These revolutions will drive the technology decisions for the next decade. Developers are massively embracing AI. As a platform company, Microsoft is focused on enabling developers to make the shift to the next app development pattern, driven by the intelligent cloud and intelligent edge.

AI is the runtime that will power the apps of the future. At the same time, enterprises are eager to adopt and integrate AI. Cloud and AI are the most requested topics in Microsoft Executive Briefing Centers. AI is changing how companies serve their customers, run their operations, and innovate.

Ultimately, every business process in every industry will be redefined in profound ways. If it used to be true that “software was eating the world,” it is now true to say that “AI is eating software”. A new competitive differentiator is emerging: how well an enterprise exploits AI to reinvent and accelerate its processes, value chain and business models. Enterprises need a strategic partner who can help them transform their organization with AI. Microsoft is emerging as a solid AI leader as it is in a unique position to address both revolutions. Our strength and differentiation lie in the combination of multiple assets:

  • Azure AI services that bring AI to every developer. Over one million developers are accessing our pre-built and customizable AI services. We have the most comprehensive solution for building bots, combined with a powerful platform for Custom AI development with Azure Machine Learning that spans the entire AI development lifecycle, and a market leading portfolio of pre-built cognitive services that can be readily attached to applications.
  • A unique cloud infrastructure including CPU, GPU, and soon FPGA, makes Azure the most reliable, scalable and fastest cloud to run AI workloads.
  • Unparalleled tools. Visual Studio, used by over 6 million developers, is the most preferred tool in the world for application development. Visual Studio and Visual Studio Code are powerful “front doors” through which to attract developers seeking to add AI to their applications.
  • Ability to add AI to the edge. We enable developers, through our tools and services, to develop an AI model and deploy that model anywhere. Through our support for ONNX – the open source representation for AI models in partnership with Facebook, Amazon, IBM and others – as well as for generic containers, we allow developers to run their models on the IoT edge and leverage the entire IoT solution from Azure.

But the competition to win enterprises is not only played in the platform battlefield, enterprises are demanding solutions. Microsoft AI solutions provide turnkey implementations for customers who want to transform their core processes with AI. Our unique combination of IP and consulting services address common scenarios such as business agents, sales intelligence or marketing intelligence. As our solutions are built on top of our compelling AI platform, unlike ourcompetitors, our customers are not locked in to any one consulting provider, they remain in full control of their data and can extend the scenarios or target new scenarios themselves or through our rich partner ecosystem.

AI Analytics

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