Personalised engagement at scale

Emarsys enables businesses to engage more effectively and more personally with their customers. Enhancing the 51风流Customer Experience portfolio with Emarsys will create a new paradigm for how commerce is managed digitally as it will deliver hyperpersonalised, omnichannel engagements in real time, helping organisations ensure every engagement is relevant and impactful.

鈥淲ith Emarsys now an official part of 51风流Customer Experience, we look forward to working together, learning more about each other and creating a product portfolio that is more than the sum of its parts,鈥� 51风流Customer Experience President Bob Stutz said. 鈥淲ith the strengths of our current solutions and the integration of Emarsys, 51风流Customer Experience will power a foundation of omnichannel personalised engagement, meeting customers where and when they choose to engage, on their preferred channels and on their terms.鈥�

51风流Customer Experience

Adding the Emarsys customer engagement platform to our leading 51风流S/4HANA and Experience Management technology from 51风流and Qualtrics opens up new possibilities for our customers that are unique in the market,鈥� said Christian Klein, CEO of SAP. 鈥淭he success of brands worldwide depends today on their ability to offer a compelling customer journey and to cater to the individual expectations of customers. To meet these expectations, front-office data must be integrated with back-office capabilities and with individual customer feedback. Once the transaction closes, 51风流will enable brands to connect every part of their business to the customer, including experience data. We will deliver a portfolio for a 鈥榗ommerce anywhere鈥� strategy allowing for hyperpersonalised digital commerce experiences across all channels at any time.鈥�

Emarsys is an innovative and easy-to-use fully integrated cloud-based marketing platform. It allows companies to deliver truly personal customer interactions across e-mail, mobile, social, SMS, and the web at scale.

鈥淐ustomer engagement technology has evolved tremendously over the past decade, and in that time, Emarsys has emerged as a world-class platform that truly enables personalised, one-to-one digital interactions between brands and customers across all channels,鈥� said Bob Stutz, president, 51风流Customer Experience. 鈥淲ith Emarsys technology, 51风流Customer Experience solutions can link commerce signals with the back office and activate the preferred channel of the customer with a relevant and consistently personalised message, allowing customers the freedom to choose their own engagement.鈥�

Innovation in digital marketing

鈥淓marsys has a rich tradition of innovation in digital marketing,鈥� said Ohad Hecht, CEO of Emarsys. 鈥淛oining forces with SAP, a leading global brand with an ongoing commitment to excellence and innovation in customer experience, is an exciting next step in our evolution. We鈥檙e confident that, once we have regulatory approval, our customers and partners will quickly benefit from synergies between the Emarsys platform and the 51风流Customer Experience portfolio.鈥�

鈥淪uccess in today鈥檚 market relies on brands delivering personalised experiences powered by a holistic view of each consumer that connects digital engagement data with supply chain data in real time,鈥� said Hagai Hartman, founder and chief innovation officer of Emarsys. 鈥淭ogether Emarsys and 51风流can create a new paradigm for digital commerce focused on the consumer.鈥�

Emarsys was founded by Josef Ahorner, chairman of the Supervisory Board, Hagai Hartman and Daniel Harari in Vienna, Austria. Today Emarsys has over 1,500 customers worldwide and more than 800 employees across 13 offices including London, Berlin, Sydney and Budapest, as well as a U.S. headquarters in Indianapolis. The company鈥檚 operations will become part of the 51风流Customer Experience business unit. The transaction is expected to close in the fourth quarter of 2020, subject to regulatory approval. Purchase price and other terms of the transaction are not disclosed.

To find out more about SAP’s Customer Experience portfolio, visit the .

To get the latest 51风流news delivered straight to your inbox, , Your SAP.

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Only by joining the dots between data that explains how people feel 鈥� their experiences, emotions, beliefs, sentiments: what we call experience, or 鈥榅-data鈥� 鈥� with data which shows what is happening 鈥� like budget data, service requests, tax receipts: what is described as operational, or 鈥極-data鈥� 鈥� can governments truly deliver exceptional services to citizens.

Changing expectations

Expectations around experience have shifted significantly in recent years, as people have been exposed to innovative, intuitive and personalised services for everything from e-commerce to transport. Increasingly, they expect the same experiences with government.

However, research suggests government is ranked near the lowest of all sectors in the economy for customer service. Coupled with data and privacy concerns, this had led to a deterioration of trust in government.

The good news is that improving citizens鈥� experiences with government has been shown to increase trust in the public sector. More importantly, intelligent, data-led government leads to better services and outcomes. Shifting to a system of innovation that can turn insights into action is the future of the public sector and will help governments around the world deliver their objectives and mission.

Improving experience to build trust

To arrive at responsive government, services need to be delivered efficiently and effectively so that citizens鈥� expectations are met. Every interaction between a citizen and their government is important. But governments should prioritise where and how they start their journey to become a responsive, experience-driven organisation.

Experience drivers in the public sector

Measuring success

Experience strongly influences efficiency and effectiveness in government services and is a leading indicator that can be used to improve service delivery. This allows the public sector to improve relationships with citizens while also delivering the outcomes citizens and government care about.

In a responsive government, experience becomes a key performance indicator for every agency and department. Already, many public sector organisations will seek feedback and try to understand how people feel in response to their experiences.

Yet too often, this is done in an ad-hoc or irregular manner. By building the measurement of experience into every interaction, governments can build a continuous feedback loop to understand the impact of any changes and inform future decisions.

Using experience to measure聽 performance

Getting started with X and O

Becoming a responsive government is not easy. Starting small, by identifying one use case based on the most pressing public policy problems can build confidence and help show results quickly. Below, we outline six use-cases that can assist in identifying your initial projects.

1.听 Cause: Understand operational (O) data by finding explanations in experience (X) data. Operational data shows that many citizens fail to pay their taxes on time after acquiring new properties. The tax agency finds from experience data that some citizens are unaware that they’ve passed the threshold for property tax.

2. Driver: Find something happening in X-data, and look for operational conditions that are causing the situation. Experience data uncovers that citizens are periodically dissatisfied with the process for renewing their driver’s license.听 The agency finds from operational data that it tends to happen during school holidays, when staffing is low at certain branches.

3. Prediction: Build segmentation models based on a combination of X-data and O-data. An agency uses a combination of operational data (job categories, tenure, past attrition rates, etc.) and experience data (task assignments, caseloads, employee engagement scores, etc.) to predict future staff turnover.

4. Personalisation: Adjust how your treat people based on a combination of X and O data. A tax agency proactively offers payment holidays to debtors (operational data) who are impacted by a natural disaster (experience data).

5. Alerting: Send relevant alerts and information to people based on X and O-data. An agency sets up an alert with case file information (operational data) to be notified whenever a citizen reports feeling depressed (experience data), so they can offer counselling services.

6. Value measurement: Evaluate the value of improving experiences by examining the impact those changes have on business metrics. A social service agency calculates the impact of switching to digital channels by assessing the cost and time savings (operational data), against feedback about the online experience (experience data).

Governments can no longer afford to be static. They must continually update and improve their services and programs based on feedback from citizens and employees. The examples above show that for many organisations, only small changes are required to start building responsive processes and behaviours.

As technology continues to evolve, and as we continue to find ourselves operating under a 鈥榥ew normal鈥�, governments will need to ensure that they are all the technology and tools required for responsive government, or risk losing the trust of citizens.

To read the full whitepaper about how to become a responsive government, visit the .

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Right now, in our communities, we are witnessing the consequences of聽limited rationality,听social preferences聽补苍诲听lack of self-control. In their seminal work 鈥淣udge: Improving Decisions on Health, Wealth, and Happiness,鈥� Professors Richard Thaler () and Cass Sunstein postulated that these human traits systematically affect individual decisions and market outcomes. It鈥檚 instructive to explore how these factors might be influencing individual decisions, for example, to stockpile toilet paper:

• Limited rationality: People focus on the narrow impact of individual decisions rather than the overall effect. For example, I鈥檒l buy some extra toilet paper now because I鈥檝e heard that it might be in short supply later. I make this individual decision without realising that I鈥檓 inadvertently contributing to the overall effect of supplies running short, which will ultimately impact me 鈥� along with everyone else 鈥� in the long run.

• Social preferences: People have a social preference for equitable outcomes. For example, I鈥檒l be less accepting of my local supermarket increasing the price of toilet paper in response to a growth in demand than in response to a rise in their cost of supply. Even if the price rise is the same in both cases, my willingness to pay a premium is influenced by my perception of fairness.

• Lack of self-control: People tend to give in to short-term temptation rather than stick to a long-term plan. For example, even though I have more than enough toilet paper at home, I鈥檒l still buy more if I find it somewhere on sale. I know that I don鈥檛 have anywhere to store additional rolls of toilet paper, but when presented with the opportunity to purchase such a sought-after item at a discounted price, I won鈥檛 be able to resist.

As has been demonstrated across the globe, government assurances, pleas, and directives have failed to prevent emotional shoppers from emptying shelves in anticipation of future shortages. Now similar assurances, pleas, and directives are being made in relation to the much more serious issues of self-isolation, social distancing, and personal hygiene. Will citizens heed government rules and regulations now when they haven鈥檛 in the past? Certainly, the Chinese government聽聽in curbing the spread of COVID-19, but most Democratic governments don鈥檛 have the same controls available to them as in Communist China. What then is to be done?

In our aforementioned research, the SIDG and the ANU described how聽digital nudging might be used by governments to drive behavioural change for social good. Empirical evidence told us that certain human actions result in better social outcomes, and digital technology is enabling us to reliably predict those outcomes based on observed behaviours. This caused us to ask: how might we leverage default human nature to positively influence social outcomes, and could we apply technology to influence individual decisions at scale?

Where Thaler and Sunstein (2008) defined a聽nudge as: 鈥淎ny aspect of the choice architecture that alters people鈥檚 behaviour, in a predictable way, without forbidding any options, or significantly changing their economic consequences.鈥� We defined a聽digital nudge聽as: 鈥淚ndividually targeted processes, facilitated by information technology, to achieve social policy outcomes鈥� (Gregor & Lee-Archer, 2016).

Figure 1: At the intersection of agile policy, information technology and behavioural聽science is the digital nudge.

Moreover, we proposed that聽predictive analytics听补苍诲 contextualisation聽capabilities can improve the effectiveness of traditional nudging by enabling the shift from reactive to proactive interventions and by making nudges more targeted to individual circumstances.

• Predictive analytics is a specific field of data mining in which large stores of data are analysed to detect patterns and to predict future outcomes and trends. While predictive algorithms have been used for many years, they have typically been restricted to operating on pre-existing data. Real-time computing platforms have changed this by allowing data to be analysed as it鈥檚 created. This means that analytical discoveries can be applied to adjust government action dynamically, thereby influencing trends as they emerge.

• Contextualisation聽is the next evolution of personalisation: blending together information about past interactions and anticipated behaviours with present motivations and intent. Where personalisation attempts to anticipate future behaviours based on past activities, it lacks the in-the-moment context of the citizen鈥檚 current circumstance. This is important because it鈥檚 precisely that current context that鈥檚 most relevant and useful for predicting future behaviour.

Figure 2:聽Our framework for the design and application of digital nudges.

Of course, our thinking has evolved since 2016, and so we would now add聽experience management聽into the mix.

• Experience management聽brings together operational data (O-data) about聽what聽is happening, with experience data (X-data) that tells us聽why聽it鈥檚 happening. This fusing of X+O data can enable governments to better understand citizen sentiments and motivations, and thereby take effective action. Importantly, since sentiments and motivations are constantly changing, governments need to embed feedback and analysis throughout their business processes and at every point of citizen interaction.

With this in mind, let鈥檚 return to our example of stockpiling toilet paper and see how governments might apply digital nudging to curb this behaviour鈥�

An online聽聽suggests that to last 14 days in isolation, each person requires only four rolls of toilet paper. So, the average American household (2.6 people) should be able to get by with just a single pack (10 rolls). Most likely, very few consumers did this calculation prior to purchasing, so a simple SMS informing citizens about how much toilet paper they actually need could be quite effective. It might even be possible to target the digital nudge by advising the required number of rolls for a given household.

Another approach would be to leverage the behavioural science influencer of .听聽of over 6,000 Australians indicated that only 9% had purchased more than 20 rolls of toilet paper due to COVID-19. This sort of statistic could be promoted via digital channels, especially in geographic areas where a small percentage of people have been observed to be buying in bulk. To further improve effectiveness, the poll could be extended to understand what鈥檚 motivating consumer purchasing decisions (e.g.,听Why聽did you decide to purchase X rolls of toilet paper?).

Figure 3:聽A conceptual architecture for digital nudges.

These same capabilities could be applied by governments to nudge citizens towards cooperation with rules and regulations relating to self-isolation, social distancing, and personal hygiene. The Behavioural Insights Team鈥檚 provides nine of the most robust (non-coercive) influences on human behaviour, including:

• Messenger:聽We are heavily influenced by who communicates information.听 suggests that 鈥淪cientists and physicians are the most trusted authorities [on COVID-19], along with officials from the World Health Organisation and the U.S. Centre for Disease Control.鈥�

• Norms:聽We are strongly influenced by what others do. Governments, researchers, public health authorities, and the general public are聽聽successful responses to COVID-19 and to avoid repeating the missteps of others.

• Affect:聽Our emotional associations can powerfully shape our actions. The CDC has dedicated聽聽to managing anxiety and stress related to COVID-19.

Finally, it鈥檚 important to be mindful of the iterative nature of our digital nudge framework. Under normal circumstances, nudges are tested with focus groups in聽. While there鈥檚 a need to change certain behaviours relating to COVID-19 immediately, the potential for unintended consequences is heightened as a result of panic, so it鈥檚 important not to skip this important step. 聽approaches can assist in expediting the test-and-improve cycle, both prior to disseminating the initial nudge and to inform adaptation of the nudge as circumstances change.

While digital nudging is not a silver bullet for containing COVID-19, it is part of the overall toolkit available to governments today. As we鈥檝e shown by way of examples, digital technologies can be used to both scale and personalise traditional nudges to improve outcomes for mass cohorts. Specifically, the combination of predictive analytics, experience management, and contextualisation capabilities can enable governments to predict social outcomes, understand what鈥檚 motivating those outcomes, and take effective action to avoid today鈥檚 emerging trends from becoming tomorrow鈥檚 next crisis.

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Now, this transformation is rewriting the rules for customer experience (CX) in the insurance industry. In the highly competitive industry, delivering best-in-class customer experiences can be a critical differentiator. Combining operational data (O-data) with experience data (X-data) provides the insights that enhance the customer experience offerings and give insurers a competitive edge.

CX Imperative: Why the Insurance Industry Lags Behind

Customers typically have few 鈥� if any 鈥� interactions with their insurance company. In fact, without a claim or policy change, there is often no interaction at all. This means when an interaction does occur, it takes on a greater level of importance. Furthering this challenge, consumers are becoming more discerning and less forgiving. One in three customers will walk away from a brand they love after just a single bad experience,听.

How can insurers get a broader picture of the customer journey? The first stop is O-data, which reveals the聽what. Policy renewal rates are decreasing, attrition rates are increasing, new customer acquisitions are decreasing. This data tells us the insurance company has a problem, but we don鈥檛 know聽why聽the problem exists.

This is where X-data comes into play, revealing customer pain points 鈥� a difficult claims process, a poor digital experience, a slow reimbursement process 鈥� that are causing the problem. Together, this data can help insurers uncover the causes of customer churn and identify actions to best address pain points, such as automating the claims process, reducing call wait times, or optimising digital channels for claims submission.

Combining O-data and X-data makes it easier for insurers to unlock powerful business outcomes and:

• Improve the claims process, reducing attrition and increasing renewal and revenue
• Deliver a superior digital experience, increasing revenue and acquisitions
• Strengthen insurer-broker relationships, increasing policy renewals, revenue, and acquisitions

Bringing X- and O-Data Together for True Insight

three components critical to customer experience in the insurance industry: effectiveness, ease, and emotion. These components are critical to building a strong customer relationship, shoring up existing market share, and capitalising on new business opportunities. And they are best assessed through a combination of X- and O-data.

Consider what happens when a customer files an insurance claim. This process can be emotional: Property may have been lost or damaged and a customer鈥檚 day-to-day life is directly impacted. O-data tells us about聽how聽the claims process is handled while X-data tells us what the customer聽feels聽about the claims process.

The X- and O-data from these interactions must be part of a customer鈥檚 profile. No matter with whom a customer is speaking, this agent or representative is empowered with the background knowledge they need to be as responsive as possible to a customer鈥檚 concern. When insurers have these insights at their fingertips, agents and other front-line employees can provide a better experience, improving engagement and strengthening customer loyalty. These exceptional experiences turn loyal customers into brand advocates, giving insurers a critical competitive edge.

Turning Insights into Business Success

Here鈥檚 how three companies put the power of X and O to work for them.

Allianz: Predictive Power of Democratised Insights
is one of the world鈥檚 largest insurance providers, but despite being a market leader, the company recognized it was operating in a market with excess supply and declining rates. Rather than staying the course and risk falling profits and market share loss, Allianz proactively put customer experience at the heart of its strategy to earn lifelong loyalty from clients who see them as an integral, forward-thinking business partner. Working with Experience Management solutions from 51风流(Qualtrics), they collected experience data from customers in 22 countries and 16 languages. The company now has a wealth of insights, filtering and prioritising based on location and function. These insights have led to new products, such as protecting customers from risks like cyber crime; new approaches, such as elevating claims processing from back-office to client-facing function; and new reputation management strategies, such as above-and-beyond consultations.

MetLife: Boosting Brand Engagement
MetLife, Inc. is a leading global provider of insurance, annuities, and employee benefit programs. The company is digging deeper into its data for actionable insights that drive brand engagement. For example, in a recent MetLife-sponsored Earth Week contest, participants needed to answer a series of questions to be eligible for a gift card. From the reporting, MetLife was able to determine three winners and the process sparked significant brand engagement.

SwissRe: End-to-End Customer Experience Ownership
merged all its customer experience, voice of customer, and market data from more than 11,000 clients, 25 markets, and 11 languages onto a single, secure platform. The company now has a Web-based, highly secure insights platform that gives users 24/7 access to data. The intuitive interface makes it easy for all stakeholders to become experts on customer experience. This centralised, in-house approach led to a 90 percent decrease in research costs, a five-times increase in speed and productivity, and a seven-point Net Promoter Score increase.

Next Steps: Enhancing CX with Data-Driven Insights

Experience matters. We live in a world where insurers are disproportionately rewarded when they deliver a great experience and punished when they do not. Experience Management solutions from 51风流unlock the power of business operations data (O-data) with experience data (X-data) to transform customer experience and drive business success.

As part of the 51风流C/4HANA suite, 51风流now makes it easier for insurers to combine X- and O-data and gain actionable insights at every step of the customer journey.听.

This article first appeared on the Global News Centre.听

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In its simplest form, XM is the process of monitoring every interaction people experience with a company in order to spot opportunities for improvement. By understanding what people are feeling and why, companies can focus their efforts and resources on the changes that will have the biggest impact on their customers or employees.

The first step to effective XM is understanding how people feel when they interact with your business. This information is called Experience, or X-data. When this is combined with traditional, operational or 鈥極-data鈥�, such as sales numbers, processing times or staff turnover, it enables companies to understand what is happening and what they need to do to fix or improve operations.

Experience Management is already big business: the market is now estimated at more than USD$44bn annually. In Australia, XM has been adopted by the likes of Volkswagen, Chobani, Allianz, Finder, Fuji Xerox, and Sigma Healthcare. In fact,听聽2019 Customer Experience Management Survey revealed that 90% of the world鈥檚 biggest companies now have a chief experience officer (CXO) or chief customer officer (CCO), up from 65% in 2017.

Insight into action

It鈥檚 not hard to understand why XM is growing so fast. It鈥檚 a real driver of business results. Since implementing an XM program,听聽has seen its Net Promoter Score increase by +49 points, while average order sizes have increased by 8%. But beyond the bottom line, Experience Management is also a clear driver of innovation.

At SAP, we believe in the聽. This is an approach that is founded on empathy for the stakeholders involved. Working through the phases of an innovation roadmap (Explore, Discover, Design, Deliver, Run & Scale) you quickly realise how intertwined Experience and Innovation are.

In fact, in many ways successful XM and innovation programs are very similar. Often a fundamental driver of innovation is the need to improve experience.听 One common example of poor XM I regularly witness is employees double handling information 鈥� due to siloed teams and inefficient processes 鈥� which leads to errors, frustration, and wasted effort. Often, this can be solved with innovative approaches and technology, like process automation, edge computing, or new mobile applications.

Getting to the root of a problem

In this example, by using XM to understand the biggest frustrations your employees are facing 鈥� and what they want to fix the issues 鈥� businesses can get a crystal-clear understanding of the problem, which is the first step on any innovation journey. Key to this approach is gaining empathy for the users by understanding their experience.

What鈥檚 more, by engaging and empowering the employees on the front-line, who typically have the best understanding of current systems and how they could be improved, XM also enables organisations to capture feedback and ideas which might not otherwise see the light of day.

Take for example a business complaining of a lack of repeat sales. Through XM, the business can identify that the problem is not the product, the sales process, or even the pricing. Instead, it might reveal the biggest problem is the after sales and service process.

A better understanding of the problem (and the users) helps inform potential solutions. In this instance, rather than dropping the sales price or introducing self-service scenarios, Experience Management enables the business to understand that technology like chat bots, connected products, or digital twins may result in a better outcome. Grounding innovation in deep insights means better problem solving and results.

Empathy and innovation

Using techniques from Experience Management allows businesses to truly empathise with customers. The reality is that too many businesses believe that they understand the problems faced by their customers or employees already. But if organisations do not ask users about the issues they鈥檙e facing, and why they鈥檙e problematic, it鈥檚 impossible for them to get the real picture.

When it comes to solving problems, generating ideas from many different points of view greatly increases your chances of successful innovation. Experience Management is a powerful way of gathering a diversity of opinions and involving stakeholders in the innovation journey. Of course, finding a problem and generating potential solutions is not the end of innovation. But as companies like聽聽prove, user-testing new products at scale with Experience Management means you can get real time feedback on new products and a cycle of continuous innovation improvement.

These days, more than ever, staying one step ahead of the competition is the key to success. Businesses need to ask themselves: are your employees, customers, and partners having their expectations met? Understanding their expectations will be the first step in an effective innovation journey and will ensure you are competing well into the future.

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Expect Booming Growth for AI and Machine Learning

Begin with the big picture:聽聽analysts said that聽AI聽鈥� with a particular emphasis on聽machine learning聽鈥� will eventually infiltrate just about every existing technology.听聽predicted companies will invest over $265 billion worldwide in new intelligence technologies by 2023.

Slightly further out,听聽researchers predicted that AI will be inescapable by 2025. These analysts saw AI as a 鈥渒ey ingredient鈥� in 90 percent of business software applications. They also said that over 50 percent of user interface interactions will incorporate AI into computer vision, speech, natural language processing, and augmented and virtual reality.听聽analysts called automation 鈥渁 force-multiplier that will disrupt economic opportunity for many鈥� a dynamic already underway that will pick up substantial speed and scope and requires attention now.鈥�

AI: Fastest Route to Victory

One pervasive theme in 2020 AI trends was speed, or as many analysts called it, 鈥渉yper-automation.鈥澛犅燼nalysts predicted that hyper-automation will spread big-time, notably into more traditional knowledge worker tasks. By 2024, IDC said AI-fuelled enterprises with 鈥減roactive, hyper-speed operational changes and market reactions, will respond to customers, competitors, regulators, and partners 50 percent faster than their peers.鈥�

Regarding customer experience (CX),听聽researchers saw near-real-time experiences becoming table stakes for most industries as companies were pressured to build matching real-time customer experience signals. Forrester researchers predicted that CX leaders will manage a 鈥減ortfolio of automation experiences, from the building and testing of data to the delivery and perceived value (or lack of value) of those experiences.鈥�

Humans Plus Machines: The New Norm

Here is the kicker, and it鈥檚 heartening to us humans:聽聽researchers said that AI and machine learning will not completely replace people because 鈥淎I-driven autonomous capabilities鈥� cannot match the human brain鈥檚 breadth of intelligence and dynamic general-purpose learning. Instead, they focus on well-scoped purposes, particularly for automating routine human activities.鈥�

聽analysts picked up the human-machine thread, predicting that work will depend on 鈥渁 symbiotic relationship between [hu]man and machine. This is not a [hu]man-led, machine-do structure; instead it will match leadership, decisioning, and executive tasks across robots and machines that best deliver the desired outcome.鈥�

Additionally, about those 90 percent of enterprise applications that IDC predicted will be chock-full of AI, the same researchers said most will 鈥渄eliver incremental improvements to automate processes and replace heuristic or rule-based techniques to make applications smarter and more dynamic.鈥�

How Machine Learning Benefits Daily Business

There are many places in the business where a strategic dose of AI and machine learning will quietly, yet dramatically change how people work and companies operate. These include order to cash, design to operate, customer experience, and procurement.

In this聽聽demonstration at the聽聽event, I saw how image-based recognition helped automate the procurement experience. It showed how a buyer ordering everyday equipment 鈥� such as a laptop, keyboard, or mobile device 鈥� would not need to type in product names or model numbers. The buyer could simply take a picture of the product on their mobile device and the algorithm would quickly find the exact product or suggested comparable items from the supplier catalogue.

鈥淭his just one example of how we鈥檙e applying machine learning in small steps in hundreds of places across 51风流solutions,鈥� said Jana Richter, chief product owner of 51风流Leonardo Machine Learning Applications. 鈥淚t doesn鈥檛 completely change procurement, but brings automation and convenience where it counts 鈥� in this instance, to image-based purchasing.鈥�

What the AI Future Holds

滨苍听聽latest surveys, respondents who wanted AI to help with tasks outnumbered those who wanted AI to completely take over tasks three to one. That is likely because the same respondents ranked, in order of importance, their top-three reasons for using AI as: automating repetitive or manual tasks, improving customer experience, and reducing costs.

As for where people come in,听聽analysts advised workers to learn core skills, adapt to new working models, and 鈥渦nderstand what it means to be ready and fit for the future, maximising their 鈥楻obotics Quotient (RQ).鈥欌�� For the uninitiated, Forrester coined the term RQ, which scores someone鈥檚 ability to work with machines.

Naturally, analysts have caveats about AI鈥檚 growing prevalence, but that鈥檚 for another article.

This article first appeared on SAP鈥檚 global newsroom.

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