Top CX Trends for CIOs to Watch

These five emerging customer technology trends should inform CIOs’ customer experience (CX) strategy.


Chatbots, virtual assistants and robots drive seamless and effortless experience, and their usage is bound to increase. Gartner predicts that by 2022, 70% of customer interactions will involve emerging technologies such as machine learning (ML) applications, chatbots and mobile messaging, up from 15% in 2018.

However, CIOs and customer experience (CX) leaders wonder which CX technologies reduce overall customer effort. A recent survey of CX leaders highlighted five emerging technologies that will have the biggest impact on CX projects in the next three years.

Artificial intelligence

Organizations are struggling to keep up with the growth of customer insight data. The speed and precision of artificial intelligence (AI) technologies and analytics, in addition to human insight, will provide the intelligence needed for the CX of the future.

“AI interactions will enhance text, sentiment, voice, interaction and even traditional survey analysis,” says Don Scheibenreif, Distinguished Vice President Analyst, Gartner.

Identify pain points in the customer’s journey that could be addressed with AI technology such as chatbots that provide immediate, personalized responses 24/7 or virtual assistants that engage with shoppers in simple conversations. CIOs can also deploy augmented technologies to automate analysis and insights using machine learning and natural language generation.

Virtual customer assistants and chatbots

Virtual customer assistants (VCAs) reduce customer effort and enable better CX on company websites. Some organizations also opt for role-based assistants that can significantly improve interactions with employees, customers and partners. Examples include Erica from Bank of America or Amelia from IPsoft.

CIOs need to focus on simple, repetitive, factual interactions where customer needs are well-understood, and data and rules are not complex. To find the use cases and personas where the VCAs have the maximum impact, conduct co-creation sessions at which business counterparts discuss concepts and ideas in collaboration with their customers.

Omnichannel customer engagement

Customers use many channels — emails, chatbots, messengers and customer service agents — when they interact with organizations, and they expect a seamless experience as they switch from channel to channel. Understanding how customers use and experience the different channels is critical to deliver a personalized customer experience. A multichannel customer engagement center can provide a single source of truth about customer behaviors.

Real-time event-driven application architecture

Event-driven architecture (EDA) helps capture real-world business events, such as when a customer places an order. Organizations need to monitor sources like Internet of Things (IoT) devices, mobile applications, or social and business networks to be aware of an event occurring that could lead to a business opportunity.

“Monitoring event streams helps organizations stay aware of the current context and thereby make more intelligent decisions,” says Scheibenreif. “More intelligent decisions translate into better customer service and greater success for the business.”

Work with business teams to identify the key business events, opportunistic business moments, and associated business decisions and outcomes that define your customer experience.

Internet of Things

When you engage customers through internet-connected devices, you provide additional touchpoints for customers to engage with and for organizations to learn from.

Robots will steadily improve and drive engagement in more settings such as retail, healthcare and transportation hubs, and organizations may increasingly need to engage with customers who aren’t human. In this scenario, ask yourself what a machine will need to buy from your organization when its decisions are based on algorithms, not emotion, or what CX means for a robot or a machine.

Identify internet-connected devices that may be present along the customer journey. Understand the role of the device, how it can influence the decision-making process and what adjustments to customer engagement are needed to ensure the device can help you deliver a good customer experience.


Source: by Laurence Goasduff on February 27, 2020 –

4 Key Trends in the Gartner Hype Cycle for Human Capital Management Technology, 2019

Emerging human capital management (HCM) technologies will help HR overcome challenges around stakeholder engagement and talent acquisition and management.

Employee engagement and productivity are key priorities for organizations globally in today’s hypercompetitive labor markets, and designers of HCM applications are thus increasingly aiming to improve the candidate, worker and manager experiences, the Gartner Hype Cycle for Human Capital Management Technology, 2019, shows.

At the same time, continuous learning, listening, feedback and performance management are needed to sustain digital business and to thrive, despite the “turns” and disruption that organizations increasingly face.

The technologies featured in this Hype Cycle reflect this narrative, as well as more developed trends within the HCM market, such as the maturation of talent management and HCM suites.

“By 2022, 50% of large enterprises will have invested in a major initiative to improve their manager experience by automating multiple work-related HCM tasks”

“The 2019 HCM Hype Cycle highlights emerging technologies that will have a significant impact on administrative HR, talent management, workforce management, and integrated HR service management in coming years,” says Gartner VP Analyst Ron Hanscome.

The Hype Cycle can help HR leaders prioritize investment by providing insight into the maturity of important technologies and concepts. Among the 30 innovations profiled, Gartner clients are focusing their attention on four key areas.

Automation of the manager experience

By 2022, 50% of large enterprises will have invested in a major initiative to improve their manager experience by automating multiple work-related HCM tasks. Importantly, these technologies are meant to reduce the amount of time managers spend on administrative tasks, not replace managers’ role/authority by automation.

One example is workforce management (WFM), a suite of functions designed to help manage hourly paid workers. Core functions of WFM include time and attendance, scheduling, absence management and task management.

Next-generation (next-gen) WFM is the result of the following trends impacting the market: automation of the manager experience; employee experience; virtual assistants (VAs); new platforms; the flexible workforce.

“While the potential benefit of next-gen WFM is high, mainstream adoption is still 5-10 years away, and the technology is nearing the Peak of Inflated Expectations”

Next-gen WFM offers the ability to both augment and transform existing business processes for organizations with hourly paid workers. Properly executed, the benefits include improved employee engagement, reduced manager time spent on administrative tasks, improved usability of the WFM application, reduced cost of training, and easier management of employees and contingent workers.

While the potential benefit of next-gen WFM is high, mainstream adoption is still 5-10 years away, and the technology is nearing the Peak of Inflated Expectations on the Hype Cycle — a period during which publicity produces a number of success stories, often accompanied by scores of failures. Some companies take action; many do not.

Still, given the potential benefits, any application leader responsible for HCM, and senior HR leaders in organizations with hourly or shift-based workers, should place a high priority on creating a strategy around next-gen WFM.

Voice of the employee (VoE)

By 2020, 20% of organizations will include employee engagement improvement as a shared performance for HR and IT groups. To measure and monitor employee engagement, an increasing number of organizations are using engagement data beyond formal large-scale surveys to create a more holistic “voice of the employee.”

VoE technologies, such as employee sentiment and social analytics tools, collect and analyze opinions, perceptions and feelings of employees and workers, providing a way to harness multiple sources of information to understand the dynamics of the employee experience; they are not meant to enable covert surveillance of employees.

“By 2022, 35% of organizations will utilize conversational user experience and natural language processing interactions in their talent acquisition”

Gartner positioned VoE quite early in the Hype Cycle in 2017, but few VoE solutions in 2019 can yet be called “comprehensive” as most still fail to deliver all the needed data collection and analytical methods. Also, the market has yet to coalesce around a more standardized set of capabilities for VoE processes, enabling technologies and services. VoE needs more time (at least five years) to mature to reach widespread adoption.

Nevertheless, the potential benefit is high, and over the longer term, a VoE approach can contribute to improving an organization’s overall employee experience and employment value proposition. Potential employees are more likely to be attracted to a work environment in which their voice is heard. VoE also has the potential to improve engagement, performance and productivity among existing employees over time.

Analytics and AI in talent acquisition

By 2022, 35% of organizations will utilize conversational user experience and natural language processing interactions in their talent acquisition, which will turn the job application process into a simple conversation.

Artificial intelligence (AI) in talent acquisition is on the rise in the Hype Cycle, and the potential benefit is high. Gartner research shows steady demand from organizations to leverage these technologies, including capabilities across front-end talent processes such as sourcing, job targeting and distribution, and predictive analytics to improve time-to-hire and quality-of-hire metrics. A variety of “point” solutions are available, including chatbots, VAs and AI-enabled sources.

“VAs can also be deployed to improve a team’s search proficiency with AI, as well as potentially to overcome biases”

AI apps and chatbots support the operational side of talent software used by recruiters and candidates in the application process. Early adopters used these tools to support recruiters dealing with high-volume, high-transactional activities early in the recruitment process. As interest grows and the market matures, solutions are moving deeper into the hiring process to support both recruiters and applicants in more advanced activities.

By expanding sourcing and screening capacity, as well as increasing levels of initial front-line responsiveness, talent acquisition teams can gain a broader reach and higher levels of candidate interaction. VAs can also be deployed to improve a team’s search proficiency with AI, as well as potentially to overcome biases. By increasing efficiency, an AI-supported recruitment process can also help the recruitment team to focus on the right talent at the right time.

Rise of the internal talent marketplace

The gig economy and the need for business agility have opened up new ways of working, challenging established notions of employment and changing worker expectations, and preferences around how individuals manage their careers and build their portfolio of work experiences.

Marketplace-based platforms make it much easier to connect customers directly to suppliers. Internal talent marketplaces take advantage of the increased flexibility of the gig economy and marketplace-based platforms without requiring changes to employment categories. They match internal employees and, in some cases, a pool of contingent workers, to short-term project and work opportunities without recruiter involvement.

The internal talent marketplace is embryonic on the Hype Cycle, but the benefit is potentially transformational, enabling tomorrow’s more lean, agile and adaptive organizations.

Large enterprises looking to push innovation to the edge can be encumbered by heavy management and control structures. Internal talent marketplaces have the potential to change that. They establish trust through feedback mechanisms. They enable worker-led innovation and contribute to workers fully taking control of their own careers. They mean much better and more granular tracking of skills, competencies, knowledge and interest of individual workers — in turn allowing enterprises to have a much better view into their workforce and improve workforce planning.


Source: by Jackie Wiles on October 25, 2019 –

Gartner Top Strategic Predictions for 2020 and Beyond

Technologies from AI to cryptocurrency and online shopping are changing how we live and what it means to be human. CIOs and IT leaders must help their organizations adapt in this changing world.

In Japan, one restaurant is exploring artificial intelligence (AI) robotics technology to enable paralyzed employees to remotely pilot robotic waiters. JPMorgan Chase, Microsoft and Ford are hosting virtual career fairs tailored to the needs of neurodiverse candidates. Enterprise Rent-A-Car integrated braille-reader technology into its reservations system for blind employees.

Using AI to increase accessibility at work is one of the Gartner Top 10 strategic predictions for 2020 and beyond. The predictions examine how technology is changing the definition of what it means to be human, and IT leaders must be prepared to adapt in a changing environment.

“Technology, and its applications, are poised to affect every aspect of what we call humanity”

“As the digital age progresses, assumptions around the fixed nature of ‘what’ humans are is beginning to be challenged,” said Daryl Plummer, Distinguished VP Analyst, & Gartner Fellow at Gartner IT Symposium/Xpo 2019 in Orlando, Florida. “Technology, and its applications, are poised to affect every aspect of what we call humanity and the conditions in which humans must live.”

BYOD becomes BYOE

Through 2023, 30% of IT organizations will extend BYOD policies with “bring your own enhancement” (BYOE) to address augmented humans in the workforce.

For IT, the temptation to assert control might increase as human augmentation technology becomes more prevalent, but the business opportunity lies in exploiting increased interest in BYOE. Currently, industries like automotive and mining are using wearables to increase worker safety, while travel and healthcare are using wearables to increase productivity. As these technologies evolve, organizations will begin to consider how physical augmentations can be leveraged in personal and professional lives. Balance security with the organizational benefits of BYOE.

AI increases accessibility

By 2023, the number of people with disabilities employed will triple due to AI and emerging technologies reducing barriers to access.

In the U.S., only 30% of labor force participants with disabilities are employed. This represents a huge untapped talent pool during a time when hiring managers are warning about the availability of talent and its effect on the future of organizations.

Necessary changes will range from the cultural (i.e., removing the term “stand-up” meeting) to the technical (i.e., adjusting legacy systems to be more accessible.) Organizations that actively employ people with disabilities experience 89% higher retention rates, a 72% increase in employee productivity and a 29% increase in profitability. Plus, added diversity means added perspectives. Employees with disabilities bring a new lens to product development, increasing the potential for a product that appeals to a new client base.

Online shopping identified as addiction

By 2024, the World Health Organization will identify online shopping as an addictive disorder as millions abuse digital commerce and encounter financial stress.

With the increasing availability of consumer data, marketers are able to pinpoint exactly which consumer will buy their product and at what point in the buyer journey. As technology grows more sophisticated, marketers will be able to more accurately predict what consumers want, how to price products and where to position them.

But this comes at a price. As consumers purchase more products they don’t need and can’t afford, businesses will need to take responsibility to warn potential buyers, similar to how U.S. casinos must promote responsible gambling. Businesses may also see increased pressure by governments and consumer groups to take responsibility for exploitative or irresponsible practices.

AI emotions drive ads

By 2024, AI identification of emotions will influence more than half of the online advertisements you see.

With the increasing popularity of sensors that track biometrics and the evolution of artificial emotional intelligence, businesses will be able to detect consumer emotions and use this knowledge to increase sales. Along with environmental and behavior indicators, biometrics enable a deeper level of hyperpersonalization. Brands should be transparent and educate consumers about how their data is being collected and used.

Internet of Behavior links people and actions

By 2023, individual activities will be tracked digitally by an “Internet of Behavior” to influence benefit and service eligibility for 40% of people worldwide.

The Internet of Behavior (IoB) will be used to link a person digitally to their actions. For example, linking your image as documented by facial recognition with an activity such as purchasing a train ticket can be tracked digitally.

IoB will also be used to encourage or discourage a particular set of behaviors. For example, Allstate’s Drivewise and State Farm’s HiRoad programs track driver behavior in exchange for higher (i.e. speeding, unsafe driving) or lower (i.e. safe driving, reasonable speed). However, concerns exist around the ethical implications of expanding this technology to reward or punish certain behaviors with access to social services such as schools or assisted housing.

Workers orchestrate business applications

By 2023, 40% of professional workers will orchestrate their business application experiences and capabilities like they do their music streaming services

Historically, organizations have offered employees a “one-size-fits-all” application solution. Regardless of job description or needs, each employee operated within the same business application. Employees fit their job to the application — sometimes to the detriment of their own job. In the future, business units or central IT will receive capabilities in building block form, enabling them to create individual “playlists” of applications customized to specific employee needs and jobs.

Mobile cryptocurrency increases

By 2025, 50% of people with a smartphone but without a bank account will use a mobile-accessible cryptocurrency account.

As marketplaces and social media platforms begin supporting cryptocurrency payments, much of the world will shift to mobile-accessible cryptocurrency accounts; Africa is expected to have the highest growth rates. These cryptocurrency accounts will also drive e-commerce as trading partners emerge in areas previously unable to access capital markets.

Blockchain authenticates content

By 2023, up to 30% of world news and video content will be authenticated as real by blockchain, countering deep fake technology.

Although fake news has existed for centuries, social media bots have rapidly accelerated the rate at which this deliberate disinformation can be spread. In addition to traditional news stories, technology is being used to create convincing fake audio and video. However, organizations and governments are now turning to technology to help counter fake news, for example, by using blockchain technology to authenticate news photographs and video, as the technology creates an immutable and shared record of content that ideally is viewable to consumers.

G7 establishes AI oversight

By 2023, a self-regulating association for oversight of AI and machine learning designers will be established in at least four of the G7 countries.

AI technology is vulnerable to implicit and explicit bias, logic gaps and general algorithm complexities. At scale, these biases can affect entire groups of people and, depending on the AI’s purpose, the impact can be severe. Regulating AI is challenging, but industries will need to create standardization around development and certifications, as well as a set of professional standards for ethical AI usage.

Digital innovation timelines double

Through 2021, digital transformation initiatives will take large traditional enterprises, on average, twice as long and cost twice as much as anticipated.

Large organizations will struggle with digital innovation as they recognize the challenges of technology modernization and the costs of simplifying operational interdependence. Smaller, more agile organizations, by contrast, will have an opportunity to be first to market as larger organizations exhibit lackluster immediate benefits.


Source: by Kasey Panetta on October 22, 2019 –

Gartner Top 10 Strategic Technology Trends for 2020

Hyperautomation, blockchain, AI security, distributed cloud and autonomous things drive disruption and create opportunities in this year’s strategic technology trends. 

Human augmentation conjures up visions of futuristic cyborgs, but humans have been augmenting parts of the body for hundreds of years. Glasses, hearing aids and prosthetics evolved into cochlear implants and wearables. Even laser eye surgery has become commonplace.

But what if scientists could augment the brain to increase memory storage, or implant a chip to decode neural patterns? What if exoskeletons became a standard uniform for autoworkers, enabling them to lift superhuman weights? What if doctors could implant sensors to track how drugs travel inside a body?

Technology is now on the cusp of moving beyond augmentation that replaces a human capability and into augmentation that creates superhuman capabilities.

How these changes will impact the world and business makes human augmentation one of Gartner’s top 10 strategic technology trends that will drive significant disruption and opportunity over the next five to 10 years.

The trends are structured around the idea of “people-centric smart spaces,” which means considering how these technologies will affect people (i.e., customers, employees) and the places that they live in (i.e., home, office, car).

“These trends have a profound impact on the people and the spaces they inhabit,” said Brian Burke, Gartner Research VP, at Gartner 2019 IT Symposium/Xpo™ in Orlando, Florida. “Rather than building a technology stack and then exploring the potential applications, organizations must consider the business and human context first.”

These trends don’t exist in isolation; IT leaders must decide what combination of the trends will drive the most innovation and strategy.

For example, artificial intelligence (AI) in the form of machine learning (ML) with hyperautomation and edge computing can be combined to enable highly integrated smart buildings and city spaces. In turn, these technology combinations enable further democratization of the technology.

Trend No 1. Hyperautomation

Automation uses technology to automate tasks that once required humans.

Hyperautomation deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans. Hyperautomation extends across a range of tools that can be automated, but also refers to the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, reassess.)

Hyperautomation often results in the creation of a digital twin of the organization

As no single tool can replace humans, hyperautomation today involves a combination of tools, including robotic process automation (RPA), intelligent business management software (iBPMS) and AI, with a goal of increasingly AI-driven decision making.

Although not the main goal, hyperautomation often results in the creation of a digital twin of the organization (DTO), allowing organizations to visualize how functions, processes and key performance indicators interact to drive value. The DTO then becomes an integral part of the hyperautomation process, providing real-time, continuous intelligence about the organization and driving significant business opportunities.

Trend No. 2: Multiexperience

Multiexperience replaces technology-literate people with people-literate technology. In this trend, the traditional idea of a computer evolves from a single point of interaction to include multisensory and multitouchpoint interfaces like wearables and advanced computer sensors.

For example, Domino’s Pizza created an experience beyond app-based ordering that includes autonomous vehicles, a pizza tracker and smart speaker communications.

In the future, this trend will become what’s called an ambient experience, but currently multiexperience focuses on immersive experiences that use augmented reality (AR), virtual (VR), mixed reality, multichannel human-machine interfaces and sensing technologies. The combination of these technologies can be used for a simple AR overlay or a fully immersive VR experience.

Trend No. 3: Democratization

Democratization of technology means providing people with easy access to technical or business expertise without extensive (and costly) training. It focuses on four key areas — application development, data and analytics, design and knowledge — and is often referred to as “citizen access,” which has led to the rise of citizen data scientists, citizen programmers and more.

For example, democratization would enable developers to generate data models without having the skills of a data scientist. They would instead rely on AI-driven development to generate code and automate testing.

Trend No. 4: Human augmentation

Human augmentation is the use of technology to enhance a person’s cognitive and physical experiences.

Physical augmentation changes an inherent physical capability by implanting or hosting a technology within or on the body. For example, the automotive or mining industries use wearables to improve worker safety. In other industries, such as retail and travel, wearables are used to increase worker productivity.

Physical augmentation falls into four main categories: Sensory augmentation (hearing, vision, perception), appendage and biological function augmentation (exoskeletons, prosthetics), brain augmentation (implants to treat seizures) and genetic augmentation (somatic gene and cell therapy).

“AI and ML are increasingly used to make decisions in place of humans”

Cognitive augmentation enhances a human’s ability to think and make better decisions, for example, exploiting information and applications to enhance learning or new experiences. Cognitive augmentation also includes some technology in the brain augmentation category as they are physical implants that deal with cognitive reasoning.

Human augmentation carries a range of cultural and ethical implications. For example, using CRISPR technologies to augment genes has significant ethical implications.

Trend No. 5: Transparency and traceability

The evolution of technology is creating a trust crisis. As consumers become more aware of how their data is being collected and used, organizations are also recognizing the increasing liability of storing and gathering the data.

Additionally, AI and ML are increasingly used to make decisions in place of humans, evolving the trust crisis and driving the need for ideas like explainable AI and AI governance.

This trend requires a focus on six key elements of trust: Ethics, integrity, openness, accountability, competence and consistency.

Legislation, like the European Union’s General Data Protection Regulation (GDPR), is being enacted around the world, driving evolution and laying the ground rules for organizations.

Trend No. 6: The empowered edge

Edge computing is a topology where information processing and content collection and delivery are placed closer to the sources of the information, with the idea that keeping traffic local and distributed will reduce latency. This includes all the technology on the Internet of Things (IoT). Empowered edge looks at how these devices are increasing and forming the foundations for smart spaces, and moves key applications and services closer to the people and devices that use them.

By 2023, there could be more than 20 times as many smart devices at the edge of the network as in conventional IT roles.

Trend No. 7: The distributed cloud

Distributed cloud refers to the distribution of public cloud services to locations outside the cloud provider’s physical data centers, but which are still controlled by the provider. In distributed cloud, the cloud provider is responsible for all aspects of cloud service architecture, delivery, operations, governance and updates. The evolution from centralized public cloud to distributed public cloud ushers in a new era of cloud computing.

Distributed cloud allows data centers to be located anywhere. This solves both technical issues like latency and also regulatory challenges like data sovereignty. It also offers the benefits of a public cloud service alongside the benefits of a private, local cloud.

Trend No. 8: Autonomous things

Autonomous things, which include drones, robots, ships and appliances, exploit AI to perform tasks usually done by humans. This technology operates on a spectrum of intelligence ranging from semiautonomous to fully autonomous and across a variety of environments including air, sea and land.

While currently autonomous things mainly exist in controlled environments, like in a mine or warehouse, they will eventually evolve to include open public spaces. Autonomous things will also move from stand-alone to collaborative swarms, such as the drone swarms used during the Winter Olympic Games in 2018.

However, autonomous things cannot replace the human brain and operate most effectively with a narrowly defined, well-scoped purpose.

Trend No. 9: Practical blockchain

Blockchain is a type of distributed ledger, an expanding chronologically ordered list of cryptographically signed, irrevocable transactional records shared by all participants in a network.

Blockchain also allows parties to trace assets back to their origin, which is beneficial for traditional assets, but also paves the way for other uses such as tracing food-borne illnesses back to the original supplier. It also allows two or more parties who don’t know each other to safely interact in a digital environment and exchange value without the need for a centralized authority.

The complete blockchain model includes five elements: A shared and distributed ledger, immutable and traceable ledger, encryption, tokenization and a distributed public consensus mechanism. However, blockchain remains immature for enterprise deployments due to a range of technical issues including poor scalability and interoperability.

“Blockchain, which is already appearing in experimental and small-scope projects, will be fully scalable by 2023”

Enterprise blockchains today take a practical approach and implement only some of the elements of a complete blockchain by making the ledger independent of individual applications and participants and replicating the ledger across a distributed network to create an authoritative record of significant events. Everyone with permissioned access sees the same information, and integration is simplified by having a single shared blockchain. Consensus is handled through more traditional private models.

In the future, true blockchain or “blockchain complete” will have the potential to transform industries, and eventually the economy, as complementary technologies such as AI and the IoT begin to integrate alongside blockchain. This expands the type of participants to include machines, which will be able to exchange a variety of assets — from money to real estate. For example, a car would be able to negotiate insurance prices directly with the insurance company based on data gathered by its sensors.

Blockchain, which is already appearing in experimental and small-scope projects, will be fully scalable by 2023.

Trend No. 10: AI security

Evolving technologies such as hyperautomation and autonomous things offer transformational opportunities in the business world. However, they also create security vulnerabilities in new potential points of attack. Security teams must address these challenges and be aware of how AI will impact the security space.

AI security has three key perspectives:

Protecting AI-powered systems: Securing AI training data, training pipelines and ML models.

Leveraging AI to enhance security defense: Using ML to understand patterns, uncover attacks and automate parts of the cybersecurity processes.

Anticipating nefarious use of AI by attackers: Identifying attacks and defending against them.


Source: by Kasey Panetta on October 21, 2019 –

5 trends to expect for artificial intelligence in 2019

Expect to see major changes in how organizations use AI, the rise of new ‘digital workers’ and increased competition for data professionals with AI skills, says Forrester Research.

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With AI efforts, most organizations find ‘no pain, no gain’

Forrester Research has released its top predictions for artificial intelligence in 2019, in the report “Predictions 2019: Artificial Intelligence No Pain, No Gain With Enterprise AI.” Authored by analysts Michele Goetz, Brandon Purcell, Craig Le Clair, Diego Lo Giudice and Mike Gualtieri, the report sees major changes in how organizations use AI, the rise of new ‘digital workers’ and increased competition for data professionals with AI skills. Here are the five top trends Forrester expects to see.

Data doldrums will continue to drown the majority of firms embarking on AI

“The No. 1 challenge for AI adopters is quality data,” the Forrester analysts write. “Regardless of your beginner or expert AI status, data is the Debbie Downer of any AI project. While enterprise aspirations for AI run high, in 2019, we predict continued investments in good ol’ information architecture (IA). The tables will turn from AI to IA in the majority of firms that have already dabbled in some form of AI, as they’ll quickly realize that their irrational exuberance for AI adoption must be equally met with solid efforts on an AI-worthy data environment.”
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Enterprises will compete to use AI in the race for AI talent

“Two-thirds of AI decision makers struggle with finding and acquiring AI talent, and 83 percent struggle with retention,” the Forrester analysts note. “The talent shortage goes beyond technical and data science chops. Firms need industry, social, legal, customer experience, and operational expertise to train, manage, and trust AI systems. Consultancies such as EY and PwC prize demonstrated qualities of creative problem solving, resiliency, and adjacent job experience over degrees, coding, and statistical skills. This shows that traditional recruiting practices fail, causing companies to seek out new approaches and tools. In 2019, we’ll see firms start to tackle the AI shortage by applying AI to recruitment.”
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RPA and AI will join forces to create digital workers for more than 40 percent of enterprises

“The robotic process automation (RPA) momentum started way before AI piqued the interest of enterprises,” The Forrester analysts explain. “Until now, firms have been treating these set of technologies distinctly; i.e., RPA for automation, AI for intelligence. But to create breakthrough opportunities, we believe that an RPAplus-AI technology innovation chain will turbocharge your innovation efforts. Firms are already combining AI building block technologies such as ML and text analytics with RPA features to drive greater value for digital workers in four use cases: analytics that solves nagging platform issues; chatbots that boss around RPA bots; internet-of-things (IoT) events that trigger digital workers; and text analytics that lifts RPA’s value.”
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A fledgling supply-side market will surface for explainable AI

“When it comes to explainability and auditability, not all AI is created equal,” the Forrester analysts write. “Some ML algorithms yield transparent and easily understandable models; others, most notably neural networks, are opaque. Regulations like the GDPR’s Recital 71, which states that subjects of automated decision-making have the right ‘to obtain an explanation of the decision reached,’ will spur interest in explainability in 2019 from both enterprises as well as vendors. Business users will increasingly demand it from their data science counterparts to trust the models that impact customers.”
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Ten percent of firms leveraging AI will bring human expertise back into the loop

“While ML is exceptional at analyzing data to create models that make predictions, recognize patterns, and automate decisions, it lacks human reasoning capabilities,” the Forrester analysts write. “In 2019, enterprise AI mavericks will rediscover knowledge engineering and digital decisioning platforms to extract and encode inferencing rules and build knowledge graphs from their expert employees and customers. ML’s strength is data. Knowledge engineering’s strength is human wisdom. Used together, enterprises can dramatically accelerate the development of AI applications.”

Gartner Identifies the Top 10 Strategic Technology Trends for 2019

The top 10 strategic technology trends for 2019 are:

Autonomous Things

Autonomous things, such as robots, drones and autonomous vehicles, use AI to automate functions previously performed by humans. Their automation goes beyond the automation provided by rigid programing models and they exploit AI to deliver advanced behaviors that interact more naturally with their surroundings and with people.

“As autonomous things proliferate, we expect a shift from stand-alone intelligent things to a swarm of collaborative intelligent things, with multiple devices working together, either independently of people or with human input,” said Mr. Cearley. “For example, if a drone examined a large field and found that it was ready for harvesting, it could dispatch an “autonomous harvester.” Or in the delivery market, the most effective solution may be to use an autonomous vehicle to move packages to the target area. Robots and drones on board the vehicle could then ensure final delivery of the package.”

Augmented Analytics

Augmented analytics focuses on a specific area of augmented intelligence, using machine learning (ML) to transform how analytics content is developed, consumed and shared. Augmented analytics capabilities will advance rapidly to mainstream adoption, as a key feature of data preparation, data management, modern analytics, business process management, process mining and data science platforms. Automated insights from augmented analytics will also be embedded in enterprise applications — for example, those of the HR, finance, sales, marketing, customer service, procurement and asset management departments — to optimize the decisions and actions of all employees within their context, not just those of analysts and data scientists. Augmented analytics automates the process of data preparation, insight generation and insight visualization, eliminating the need for professional data scientists in many situations.

“This will lead to citizen data science, an emerging set of capabilities and practices that enables users whose main job is outside the field of statistics and analytics to extract predictive and prescriptive insights from data,” said Mr. Cearley. “Through 2020, the number of citizen data scientists will grow five times faster than the number of expert data scientists. Organizations can use citizen data scientists to fill the data science and machine learning talent gap caused by the shortage and high cost of data scientists.”

AI-Driven Development

The market is rapidly shifting from an approach in which professional data scientists must partner with application developers to create most AI-enhanced solutions to a model in which the professional developer can operate alone using predefined models delivered as a service. This provides the developer with an ecosystem of AI algorithms and models, as well as development tools tailored to integrating AI capabilities and models into a solution. Another level of opportunity for professional application development arises as AI is applied to the development process itself to automate various data science, application development and testing functions. By 2022, at least 40 percent of new application development projects will have AI co-developers on their team.

“Ultimately, highly advanced AI-powered development environments automating both functional and nonfunctional aspects of applications will give rise to a new age of the ‘citizen application developer’ where nonprofessionals will be able to use AI-driven tools to automatically generate new solutions. Tools that enable nonprofessionals to generate applications without coding are not new, but we expect that AI-powered systems will drive a new level of flexibility,” said Mr. Cearley.

Digital Twins

A digital twin refers to the digital representation of a real-world entity or system. By 2020, Gartner estimates there will be more than 20 billion connected sensors and endpoints and digital twins will exist for potentially billions of things. Organizations will implement digital twins simply at first. They will evolve them over time, improving their ability to collect and visualize the right data, apply the right analytics and rules, and respond effectively to business objectives.

“One aspect of the digital twin evolution that moves beyond IoT will be enterprises implementing digital twins of their organizations (DTOs). A DTO is a dynamic software model that relies on operational or other data to understand how an organization operationalizes its business model, connects with its current state, deploys resources and responds to changes to deliver expected customer value,” said Mr. Cearley. “DTOs help drive efficiencies in business processes, as well as create more flexible, dynamic and responsive processes that can potentially react to changing conditions automatically.”

Empowered Edge

The edge refers to endpoint devices used by people or embedded in the world around us. Edge computing describes a computing topology in which information processing, and content collection and delivery, are placed closer to these endpoints. It tries to keep the traffic and processing local, with the goal being to reduce traffic and latency.

In the near term, edge is being driven by IoT and the need keep the processing close to the end rather than on a centralized cloud server. However, rather than create a new architecture, cloud computing and edge computing will evolve as complementary models with cloud services being managed as a centralized service executing, not only on centralized servers, but in distributed servers on-premises and on the edge devices themselves.

Over the next five years, specialized AI chips, along with greater processing power, storage and other advanced capabilities, will be added to a wider array of edge devices. The extreme heterogeneity of this embedded IoT world and the long life cycles of assets such as industrial systems will create significant management challenges. Longer term, as 5G matures, the expanding edge computing environment will have more robust communication back to centralized services. 5G provides lower latency, higher bandwidth, and (very importantly for edge) a dramatic increase in the number of nodes (edge endoints) per square km.

Immersive Experience

Conversational platforms are changing the way in which people interact with the digital world. Virtual reality (VR), augmented reality (AR) and mixed reality (MR) are changing the way in which people perceive the digital world. This combined shift in perception and interaction models leads to the future immersive user experience.

“Over time, we will shift from thinking about individual devices and fragmented user interface (UI) technologies to a multichannel and multimodal experience. The multimodal experience will connect people with the digital world across hundreds of edge devices that surround them, including traditional computing devices, wearables, automobiles, environmental sensors and consumer appliances,” said Mr. Cearley. “The multichannel experience will use all human senses as well as advanced computer senses (such as heat, humidity and radar) across these multimodal devices. This multiexperience environment will create an ambient experience in which the spaces that surround us define “the computer” rather than the individual devices. In effect, the environment is the computer.”


Blockchain, a type of distributed ledger, promises to reshape industries by enabling trust, providing transparency and reducing friction across business ecosystems potentially lowering costs, reducing transaction settlement times and improving cash flow. Today, trust is placed in banks, clearinghouses, governments and many other institutions as central authorities with the “single version of the truth” maintained securely in their databases. The centralized trust model adds delays and friction costs (commissions, fees and the time value of money) to transactions. Blockchain provides an alternative trust mode and removes the need for central authorities in arbitrating transactions.

”Current blockchain technologies and concepts are immature, poorly understood and unproven in mission-critical, at-scale business operations. This is particularly so with the complex elements that support more sophisticated scenarios,” said Mr. Cearley. “Despite the challenges, the significant potential for disruption means CIOs and IT leaders should begin evaluating blockchain, even if they don’t aggressively adopt the technologies in the next few years.”

Many blockchain initiatives today do not implement all of the attributes of blockchain — for example, a highly distributed database. These blockchain-inspired solutions are positioned as a means to achieve operational efficiency by automating business processes, or by digitizing records. They have the potential to enhance sharing of information among known entities, as well as improving opportunities for tracking and tracing physical and digital assets. However, these approaches miss the value of true blockchain disruption and may increase vendor lock-in. Organizations choosing this option should understand the limitations and be prepared to move to complete blockchain solutions over time and that the same outcomes may be achieved with more efficient and tuned use of existing nonblockchain technologies.

Smart Spaces

A smart space is a physical or digital environment in which humans and technology-enabled systems interact in increasingly open, connected, coordinated and intelligent ecosystems. Multiple elements — including people, processes, services and things — come together in a smart space to create a more immersive, interactive and automated experience for a target set of people and industry scenarios.

“This trend has been coalescing for some time around elements such as smart cities, digital workplaces, smart homes and connected factories. We believe the market is entering a period of accelerated delivery of robust smart spaces with technology becoming an integral part of our daily lives, whether as employees, customers, consumers, community members or citizens,” said Mr. Cearley.

Digital Ethics and Privacy

Digital ethics and privacy is a growing concern for individuals, organizations and governments. People are increasingly concerned about how their personal information is being used by organizations in both the public and private sector, and the backlash will only increase for organizations that are not proactively addressing these concerns.

“Any discussion on privacy must be grounded in the broader topic of digital ethics and the trust of your customers, constituents and employees. While privacy and security are foundational components in building trust, trust is actually about more than just these components,” said Mr. Cearley. “Trust is the acceptance of the truth of a statement without evidence or investigation. Ultimately an organization’s position on privacy must be driven by its broader position on ethics and trust. Shifting from privacy to ethics moves the conversation beyond ‘are we compliant’ toward ‘are we doing the right thing.’”

Quantum Computing

Quantum computing (QC) is a type of nonclassical computing that operates on the quantum state of subatomic particles (for example, electrons and ions) that represent information as elements denoted as quantum bits (qubits). The parallel execution and exponential scalability of quantum computers means they excel with problems too complex for a traditional approach or where a traditional algorithms would take too long to find a solution. Industries such as automotive, financial, insurance, pharmaceuticals, military and research organizations have the most to gain from the advancements in QC. In the pharmaceutical industry, for example, QC could be used to model molecular interactions at atomic levels to accelerate time to market for new cancer-treating drugs or QC could accelerate and more accurately predict the interaction of proteins leading to new pharmaceutical methodologies.

“CIOs and IT leaders should start planning for QC by increasing understanding and how it can apply to real-world business problems. Learn while the technology is still in the emerging state. Identify real-world problems where QC has potential and consider the possible impact on security,” said Mr. Cearley. “But don’t believe the hype that it will revolutionize things in the next few years. Most organizations should learn about and monitor QC through 2022 and perhaps exploit it from 2023 or 2025.”

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Source: by Gartner on October 15, 2018