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:

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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.

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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.

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Click here to access Corinium’s White Paper

RPA – A programmatic approach to intelligent automation to scale growth, manage risk, and drive enterprise value

Business leaders and chief information officers around the world are jumping on the robotic process automation (RPA) pilot bandwagon to start their companies on the automation journey. Some RPA pilots are evaluating software designed to stitch together known technology concepts—such as screen scraping and macrobased automation—through user-friendly tools to take process automation to the next level. Other pilots are venturing into the use of machine learning and cognitive automation to unleash new business insights.

These pilots—or proof-of-concept programs—help leaders set a foundation for their understanding of RPA, while at the same time introducing new ideas for how automation can help scale operations or define new business strategies. And now the pilot was successful, and leaders are seeing the possibilities. So what happens next?

When performing RPA pilots many companies get stuck in basic automation and stop there. Other companies have basic and cognitive automation pilots going on simultaneously.

Aligning the goals of basic RPA with cognitive computing and artificial intelligence can seem improbable. But are the objectives really that different? Leaders want to use all levels of automation to

  • drive business growth,
  • manage risk,
  • and increase value.

The trick is having a strategy for getting from pilot to program, and putting in place a comprehensive structure looking beyond the RPA pilots to intelligent automation (IA) as an across-the-board investment. This ensures IA ventures become more than speculation and remain significant to the business.

  • But how can leaders ensure that IA is more than a one-time cost play?
  • How are future automation opportunities identified and evaluated for both risk and benefit?
  • How is “electronic employee” service performance monitored?
  • How do leaders ensure the optimal mix of basic, enhanced, and cognitive automation?
  • How is business continuity maintained if the IA solution fails?
  • How is
    • system security,
    • change management,
    • system processing,
    • and authentication control
  • maintained as automation risk becomes more complex?
  • How will IA be used to transform the business?

Leaders know technology is changing rapidly, and IA is a moving target. Implementing a “bullet-proof” value-based program is critical to managing the automation revolution and ensuring it delivers positive business impacts over time. Robust program management balances risk and reward with structures driving sustainable IA value. An IA program model delivers these ideals.

An Intelligent Automation program can help enhance and expedite the implementation of IA throughout an organization. Here are four critical characteristics for success:

  1. It is strategically positioned – Positioning IA on par with other business strategies as integral to enterprise objectives is the best place to start. Similar to outsourcing (OS), these dependent IA vendor relationships are treated as strategic. Global processowners (GPO) use IA to transform end-to-end services. Global teams engage in IA opportunity evaluation to ensure bad processes are not automated.
  2. It uses a “center of excellence” service model – Establishing a center of excellence (CoE) demonstrates a commitment to IA success. Focus drives effectiveness, and CoEs drive transparency to IA results. CoEs have varied formats (virtual, centralized, regional, etc.) and engage cross-functional teams. CoE governance guides IA strategy and validates results. Clarifying decision rights balances governance and operations accountabilities. Incorporating IA support roles (e.g., HR, IT Security, Internal Audit, risk) in decision-making ensures change integration is well managed.
  3. It has a robust delivery framework – Integrating technologies, toolkits, and tactics into IA program execution safeguards sustainability. Including relevant designers, IT professionals, and operations teams in testing makes sure solutions work. Socializing and managing life cycle compliance (e.g., intake, approvals, testing) ensures team interaction is clear. Program management, repository, and workflow tools makes oversight effective.
  4. It incorporates a proactive risk management structure – Making IT risk and security control oversight a part of IA development ensures solutions are sound. Like any technology integration, change control is critical to implementation success. An IT security risk and control framework provides this support. Risk mitigation strategies linking security reviews to IA validation ensures business goals and technology risks are appropriately considered.

RPA

Click here to access KPMG’s detailed RPA report

2018 AI predictions – 8 insights to shape your business strategy

  1. AI will impact employers before it impacts employment
  2. AI will come down to earth—and get to work
  3. AI will help answer the big question about data
  4. Functional specialists, not techies, will decide the AI talent race
  5. Cyberattacks will be more powerful because of AI—but so
    will cyberdefense
  6. Opening AI’s black box will become a priority
  7. Nations will spar over AI
  8. Pressure for responsible AI won’t be on tech companies alone

Key implications

1) AI will impact employers before it impacts employment

As signs grow this year that the great AI jobs disruption will be a false alarm, people are likely to more readily accept AI in the workplace and society. We may hear less about robots taking our jobs, and more about robots making our jobs (and lives) easier. That in turn may lead to a faster uptake of AI than some organizations are expecting.

2) AI will come down to earth—and get to work

Leaders don’t need to adopt AI for AI’s sake. Instead, when they look for the best solution to a business need, AI will increasingly play a role. Does the organization want to automate billing, general accounting and budgeting, and many compliance functions? How about automating parts of procurement, logistics, and customer care? AI will likely be a part of the solution, whether or not users even perceive it.

3) AI will help answer the big question about data

Those enterprises that have already addressed data governance for one application will have a head start on the next initiative. They’ll be on their way to developing best practices for effectively leveraging their data resources and working across organizational boundaries. There’s no substitute for organizations getting their internal data ready to support AI and other innovations, but there is a supplement: Vendors are increasingly taking public sources of data, organizing it into data lakes, and preparing it for AI to use.

4) Functional specialists, not techies, will decide the AI talent race

Enterprises that intend to take full advantage of AI shouldn’t just bid for the most brilliant computer scientists. If they want to get AI up and running quickly, they should move to provide functional specialists with AI literacy. Larger organizations should prioritize by determining where AI is likely to disrupt operations first and start upskilling there.

5) Cyberattacks will be more powerful because of AI—but so will cyberdefense

In other parts of the enterprise, many organizations may choose to go slow on AI, but in cybersecurity there’s no holding back: Attackers will use AI, so defenders will have to use it too. If an organization’s IT department or cybersecurity provider isn’t already using AI, it has to start thinking immediately about AI’s short- and long-term security applications. Sample use cases include distributed denial of service (DDOS) pattern recognition, prioritization of log alerts for escalation and investigation, and risk-based authentication. Since even AI-wary organizations will have to use AI for cybersecurity, cyberdefense will be many enterprises’ first experience with AI. We see this fostering familiarity with AI and willingness to use it elsewhere. A further spur to AI acceptance will come from its hunger for data: The greater AI’s presence and access to data throughout an organization, the better it can defend against cyberthreats. Some organizations are already building out on-premise and cloud-based “threat lakes,” that will enable AI capabilities.

6) Opening AI’s black box will become a priority

We expect organizations to face growing pressure from end users and regulators to deploy AI that is explainable, transparent, and provable. That may require vendors to share some secrets. It may also require users of deep learning and other advanced AI to deploy new techniques that can explain previously incomprehensible AI. Most AI can be made explainable—but at a cost. As with any other process, if every step must be documented and explained, the process becomes slower and may be more expensive. But opening black boxes will reduce certain risks and help establish stakeholder trust.

7) Nations will spar over AI

If China starts to produce leading AI developments, the West may respond. Whether it’s a “Sputnik moment” or a more gradual realization that they’re losing their lead, policymakers may feel pressure to change regulations and provide funding for AI. More countries should issue AI strategies, with implications for companies. It wouldn’t surprise us to see Europe, which is already moving to protect individuals’ data through its General Data Protection Regulation (GDPR), issue policies to foster AI in the region.

8) Pressure for responsible AI won’t be on tech companies alone

As organizations face pressure to design, build, and deploy AI systems that deserve trust and inspire it, many will establish teams and processes to look for bias in data and models and closely monitor ways malicious actors could “trick” algorithms. Governance boards for AI may also be appropriate for many enterprises.

AI PWC

Click here to access PWC’s detailed predictions report

 

What’s now and next in Analytics, AI, and Automation

Over the past few years, rapid technological advances in digitization and data and analytics have been

  • reshaping the business landscape,
  • supercharging performance
  • and enabling the emergence of new business innovations
  • and new forms of competition
  • and business disruption.

Yet progress has been uneven. While many companies struggle to harness the power of these technologies, companies that are fully leveraging the capabilities are capturing disproportionate benefits, transforming their businesses and outpacing—and occasionally disrupting—the rest.

At the same time the technology itself continues to evolve rapidly, bringing new waves of advances in

  • robotics,
  • analytics,
  • and artificial intelligence (AI),
  • and especially machine learning.

Together they amount to a step change in technical capabilities that could have profound implications for business, for the economy, and more broadly for society as a whole. Machines today increasingly match or outperform human performance in a range of work activities, including ones that require cognitive capabilities, learning, making tacit judgments, sensing emotion, and even driving—activities that used to be considered safe from automation. Adoption of these technologies could bring significant new performance and transformational benefits to companies that go beyond simply substituting labor and lead to previously unimagined breakthrough performance and outcomes. Moreover, they have the potential to boost the productivity of the global economy at a time when it is sorely needed for growth and the share of the working-age population is declining.

Yet their advent raises difficult questions about how companies can best prepare for and harness these technologies, the skills and organizational reinvention that will be required to make the most of them, and how the leaders in the private and public sector as well as workers will adapt to the impact on jobs, capability-building and the nature of work itself.

Disruption

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The Essential CIO Guide to Artificial Intelligence

The topic of AI has reached such a fever pitch in the media with the coverage of driverless cars, conversational bots and even movies made by AI that it’s only a matter of time before every CEO starts asking their CIO “What’s our AI strategy.

For many CIOs this will be a “deer in the headlights” moment since the topic of AI is so multi-faceted it’s hard to know where to start. We put together this e-book as a primer for CIOs wanting to get to grips with the topic of AI.
We start by giving some insight and context into why your CEO is asking this question, why now, and why you. Then, we will give you a foundational framework to think about AI so you can give your CEO a thoughtful response. Finally, we will discuss how you as CIO, can engage the business on the topic of AI and important considerations when evaluating AI vendors.
So, why is the CEO asking you this now? CEOs are humans too and they react to their environment. Their environment is often dominated by other CEOs, their board, and the outside world. AI as a topic has risen to the boardroom and the popular press with even Vanity Fair recently publishing an article titled “Suddenly Everyone is Obsessed with AI.” So if your CEO hasn’t broached the AI topic yet, they soon will.ai_breakthrough