AI: Taking Relationship Between Companies and Customers to the Next Level

Placing the customer first by using a powerful customer relationship management (CRM) tool is key to the success of any business. Artificial Intelligence (AI) and its subfields such as machine learning and deep learning are emerging as key technologies to power next-generation CRM platforms. AI can sift through volumes of data from the web, social media, mobile phones, and Internet of Things (IoT) devices.

Innovating customer experience is essential to a client-based and consumer-oriented business. Customer experience is the key competitive differentiator for 85% of senior executives, Salesforce reports. AI solutions are beginning to transform customer experiences in significant ways, paving way for widespread adoption of AI-based solutions. Global revenue is expected to grow by $ 1.1 trillion in 2021, thanks to AI-based efficiencies. By 2018, 40 percent of companies are likely to adopt AI in 2018. Enterprises are also looking to utilize AI in CRM functions such as lead generation, targeted marketing campaigns, and increasing sales cycles.

Smart, sustainable, and profitable customer service isn’t just about speed and seven-figure sales. Research supports that future-proof customer service is also consistent, personalized, real-time, and omnichannel. AI technologies can ensure all these qualities and more with its predictive capabilities, data volume capacities and analysis, as well as through its automation and recommendation engines.

“AI is expected to make an impact on all kinds of industries and businesses. Twenty percent of companies are expected to employ workers in 2020 for the monitoring of neural networks. CIOs are recommended to look into departments that require large data sets yet don’t have the proper platforms for analytics.”

Driving Smarter Customer Relationship Management

Every company requires constant customer retention and acquisition to increase its customer base and revenues. Businesses, big and small, are always on the lookout to capture new markets; this always begins with correctly understanding the consumer. AI can generate more specific leads in this aspect, going beyond basic information such as demographics, location, and buying power. An AI app could provide valuable details such as specific shopping patterns, a target market’s lifestyle habits, and family-specific data.

Amazon was among the first in using technology-driven data, with 35% of sales coming from data-generated recommendations given to consumers. Salesforce Einstein—the built-in AI in the Salesforce platform—is taking data gathering a notch higher as it analyzes information from emails, social media, sales, IoT-generated statistics, and e-commerce. Jason Pontin, Editor in Chief and Publisher of MIT Technology Review predicts that Einstein will help executives predict business performance way in advance, almost acting like a new management team member.

Kate Leggett of Forrester stresses the importance of predictive decisioning in customer-facing organizations. She opines that in spite of using analytics to personalize offers to customers, companies fail to leverage the true power of analytics for customer intelligence. Leggett further says that AI-powered analytics will soon prescribe the right action for customer-facing employees for CRM applications.

CRM applications’ data can lead to an informed sales profile, generated through the AI’s analysis of client patterns. Apart from demographics, the customer relationship technology can sift through volumes of transactions. It can then analyze how to develop products for specific customers. A smart sales team can utilize the data to help them sustain their current market and engaging new prospects. The team has a higher chance of closing sales thanks to AI-based CRM’s data analysis.

Enabling Operational Efficiency

Companies can also enable higher skill growth among employees by transferring automated tasks and direct customer engagement to AI chatbots. Customer support can spend more time on complex issues and serve more customers while chatbots deal with answering simple questions. The Amelia chatbot has a 55 percent success rate and addresses this need by providing answers based on previous interactions. If the bot is unable to answer or identifies a hostile customer, it immediately transfers the concern to an individual.

“Customer support can spend more time on complex issues and serve more customers while chatbots deal with answering simple questions. The Amelia chatbot has a 55% success rate and addresses this need by providing answers based on previous interactions. If the bot is unable to answer or identifies a hostile customer, it immediately transfers the concern to an individual.”

Even Taco Bell and Sephora have improved their services using chatbots. The TacoBot provides more organized orders for customers using Slack Chat, while Kik’s computer-driven bot responds to makeup inquiries.

AI is also improving small-but-important details that enhance a customer’s experience. Mya Systems’ AI is streamlining recruitment processes such as resume approval, interview scheduling, and gathering candidate information. This technology isn’t replacing HR employees but improving the hiring process. Using AI, recruitment companies can provide better-selected candidates without going through the time-consuming processes.

A Customer-Centric Future

AI is expected to make an impact on all kinds of industries and businesses. Twenty percent of companies are expected to employ workers in 2020 for the monitoring of neural networks. CIOs are recommended to look into departments that require large data sets yet don’t have the proper platforms for analytics.

Contrary to popular belief, AI will further personalize customer service and even customer relationships. “AI is becoming the new user interface, underpinning the way we transact and interact with systems,” according to Accenture’s 2017 Technology Vision Report. With AI becoming part of the user experience, its capabilities and purposes will go beyond the intelligent interface. Each customer interaction will eventually become more personalized and natural as it deals with more data and executing specific services.

Business leaders continue to agree that AI’s biggest potential is still in connecting with customers.

Leveraging AI capabilities, companies are looking forward to taking customer relationship and customer service to a different level altogether. Artificial intelligence and its constituent technologies such as machine learning, deep learning, and NLP are making a powerful impact; they are disrupting businesses to increase brand loyalty, revenue, and duly improve customer service.

Even service delivery can expect better efficiencies, as technologies strive to limit waiting times and increase human agent availability. All these innovations won’t be replacing primary human roles but will augment their capabilities. With AI to aid, sales teams, marketing managers, and other customer relation related employees can provide targeted and strategic services. Top managers can focus on building strategy based on most current and relevant data.

Going further, businesses are going to be even bigger, more complex, and more competitive. And AI is unleashing capabilities to help companies build a robust, agile, intelligent CRM and customer service platforms to power ahead of the competition.

(c) istockphoto.com/ monsitj | pobytov & Botlist.co

 

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Why AI is the Catalyst of IoT

Businesses across the world are rapidly leveraging the Internet-of-Things (IoT) to create new products and services that are opening up new business opportunities and creating new business models. The resulting transformation is ushering in a new era of how companies run their operations and engage with customers. However, tapping into the IoT is only part of the story [6].

For companies to realize the full potential of IoT enablement, they need to combine IoT with rapidly-advancing Artificial Intelligence (#AI) technologies, which enable ‘smart machines’ to simulate intelligent behavior and make well-informed decisions with little or no human intervention [6].

Artificial Intelligence (AI) and the Internet of Things (IoT) are terms that project futuristic, sci-fi, imagery; both have been identified as drivers of business disruption in 2017. But, what do these terms really mean and what is their relation? Let’s start by defining both terms first:

IoT is defined as a system of interrelated Physical Objects, Sensors, Actuators, Virtual Objects, People, Services, Platforms, and Networks [3] that have separate identifiers and an ability to transfer data independently. Practical examples of #IoT application today include precision agriculture, remote patient monitoring, and driverless cars. Simply put, IoT is the network of “things” that collects and exchanges information from the environment [7].

IoT is sometimes referred to as the driver of the fourth Industrial Revolution (Industry 4.0) by industry insiders and has triggered technological changes that span a wide range of fields. Gartner forecasted there would be 20.8 billion connected things in use worldwide by 2020, but more recent predictions put the 2020 figure at over 50 billion devices [4]. Various other reports have predicted huge growth in a variety of industries, such as estimating healthcare IoT to be worth $ 117 billion by 2020 and forecasting 250 million connected vehicles on the road by the same year. IoT developments bring exciting opportunities to make our personal lives easier as well as improving efficiency, productivity, and safety for many businesses [2].

AI, on the other hand, is the engine or the “brain” that will enable analytics and decision making from the data collected by IoT. In other words, IoT collects the data and AI processes this data in order to make sense of it. You can see these systems working together at a personal level in devices like fitness trackers and Google Home, Amazon’s Alexa, and Apple’s Siri [1].

With more connected devices comes more data that has the potential to provide amazing insights for businesses but presents a new challenge for how to analyze it all. Collecting this data benefits no one unless there is a way to understand it all. This is where AI comes in. Making sense of huge amounts of data is a perfect application for pure AI.

By applying the analytic capabilities of AI to data collected by IoT, companies can identify and understand patterns and make more informed decisions. This leads to a variety of benefits for both consumers and companies such as proactive intervention, intelligent automation, and highly personalized experiences. It also enables us to find ways for connected devices to work better together and make these systems easier to use.

This, in turn, leads to even higher adoption rates. That’s exactly why; we need to improve the speed and accuracy of data analysis with AI in order to see IoT live up to its promise. Collecting data is one thing, but sorting, analyzing, and making sense of that data is a completely different thing. That’s why it’s essential to develop faster and more accurate AIs in order to keep up with the sheer volume of data being collected as IoT starts to penetrate almost all aspects of our lives.

Examples of IoT data [4]:

-Data that helps cities predict accidents and crimes

-Data that gives doctors real-time insight into information from pacemakers or biochips

-Data that optimize productivity across industries through predictive maintenance on equipment and machinery

-Data that creates truly smart homes with connected appliances

-Data that provides critical communication between self-driving cars

It’s simply impossible for humans to review and understand all of this data with traditional methods, even if they cut down the sample size, simply takes too much time. The big problem will be finding ways to analyze the deluge of performance data and information that all these devices create. Finding insights in terabytes of machine data is a real challenge, just ask a data scientist.

But in order for us to harvest the full benefits of IoT data, we need to improve:

  • Speed of big data analysis
  • Accuracy of big data analysis

AI and IoT Data Analytics

There are six types of IoT Data Analysis where AI can help [5]:

Six types of IoT Data Analysis where AI can help

1. Data Preparation: Defining pools of data and clean them which will take us to concepts like Dark DataData Lakes.

2. Data Discovery: Finding useful data in the defined pools of data

3. Visualization of Streaming Data: On the fly dealing with streaming data by defining, discovering data, and visualizing it in smart ways to make it easy for the decision-making process to take place without delay.

4. Time Series Accuracy of Data: Keeping the level of confidence in data collected high with high accuracy and integrity of data

5. Predictive and Advance Analytics: a Very important step where decisions can be made based on data collected, discovered and analyzed.

6. Real-Time Geospatial and Location (logistical Data): Maintaining the flow of data smooth and under control.

AI in IoT Applications [1]:

-Visual big data, for example – will allow computers to gain a deeper understanding of images on the screen, with new AI applications that understand the context of images.

-Cognitive systems will create new recipes that appeal to the user’s sense of taste, creating optimized menus for each individual, and automatically adapting to local ingredients.

-Newer sensors will allow computers to “hear” gathering sonic information about the user’s environment.

-Connected and Remote Operations- With smart and connected warehouse operations, workers no longer have to roam the warehouse picking goods off the shelves to fulfill an order. Instead, shelves whisk down the aisles, guided by small robotic platforms that deliver the right inventory to the right place, avoiding collisions along the way. Order fulfillment is faster, safer, and more efficient.

-Prevented/Predictive Maintenance: Saving companies millions before any breakdown or leaks by predicting and preventing locations and time of such events.

These are just a few promising applications of Artificial Intelligence in IoT. The potential for highly individualized services are endless and will dramatically change the way people lives.

Challenges facing AI in IoT [4]

1. Compatibility: IoT is a collection of many parts and systems they are fundamentally different in time and space.

2. Complexity: IoT is a complicated system with many moving parts and non –stop stream of data making it a very complicated ecosystem

3. Privacy/Security/Safety (PSS): PSS is always an issue with every new technology or concept, how far IA can help without compromising PSS? One of the new solutions for such problem is using Blockchain technology.

4. Ethical and legal Issues: It’s a new world for many companies with no precedents, untested territory with new laws and cases emerging rapidly.

5. Artificial Stupidity: Back to the very simple concept of GIGO (Garbage In Garbage Out), AI still needs “training” to understand human reactions/emotions so the decisions will make sense.

Conclusion

While IoT is quite impressive, it really doesn’t amount to much without a good AI system. Both technologies need to reach the same level of development in order to function as perfectly as we believe they should and would. Scientists are trying to find ways to make more intelligent data analysis software and devices in order to make safe and effective IoT a reality. It may take some time before this happens because AI development is lagging behind IoT, but the possibility is, nevertheless, there.

Integrating AI into IoT networks is becoming a prerequisite for success in today’s IoT-based digital ecosystems. So businesses must move rapidly to identify how they’ll drive value from combining AI and IoT—or face playing catch-up in years to come.

The only way to keep up with this IoT-generated data and gain the hidden insights it holds is using AI as the catalyst of IoT.

Originally published here. (c) istockphoto.com/  franckreporter

References:

  1. https://aibusiness.com/ai-brain-iot-body/
  2. http://www.creativevirtual.com/artificial-intelligence-the-internet-of-things-and-business-disruption/
  3. https://www.computer.org/web/sensing-iot/contentg=53926943&type=article&urlTitle=what-are-the-components-of-iot-
  4. https://www.bbvaopenmind.com/en/the-last-mile-of-iot-artificial-intelligence-ai/
  5. http://www.datawatch.com/
  6. https://www.pwc.es/es/publicaciones/digital/pwc-ai-and-iot.pdf
  7. http://www.iamwire.com/2017/01/iot-ai/148265

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Are IoT Technologies Making the Grade? (Part 1)

In the past few years, the Internet of Things (IoT) has swiftly moved from the era of pilot projects and proofs-of-concept to mainstream production. Companies are starting to run their businesses on IoT, not just experiment with it. While IoT is picking up steam, adoption is still limited by business structures, organizational culture, changing talent needs, integration with legacy systems, security, and fragmented standards. But what about the underlying IoT technologies? Are they keeping up with the accelerating demands of IoT?

Let’s look at some of the technology transitions IoT needs to grow:

    • Analytics and Artificial Intelligence (AI) are the “secret sauce” of IoT. We are moving swiftly from the traditional model of centralized batch analytics to the real-time processing of data in motion. AI and IoT are emerging as perfect partners. IoT is both the source of real-time data for AI applications, and the means of executing AI decisions. While many AI applications are still in the proof-of-concept stage, it is already a transformative part of many production IoT applications. In fact, AI technology is the backbone of predictive analytics and predictive maintenance, two of four well-proven “fast-paths to IoT payback” I have identified. The Cloud-Fog Continuum is where data analytics does most of its work. In the traditional model, batch analytics took place in the cloud. Today, fog computing extends cloud capabilities to the edge of the network, where the data is generated. To save bandwidth and ensure real-time data processing, fog nodes can sort through mountains of data and send just exceptions back to the cloud for further analysis. In cases where latency is a problem, fog nodes can send real-time alerts—“drill bit is running hotter than normal”—so you can take immediate action. AI systems are moving in this direction as well. Once the logic is set, AI systems can run in specialized fog notes using FPGA, or even ASICs. This will reduce costs and accelerate adoption of driverless vehicles and other real-time AI solutions.

“AI and IoT are emerging as perfect partners. IoT is both the source of real-time data for AI applications, and the means of executing AI decisions. While many AI applications are still in the proof-of-concept stage, it is already a transformative part of many production IoT applications. In fact, AI technology is the backbone of predictive analytics and predictive maintenance..”

 

  • IoT Security burst into public consciousness last year when a distributed denial of service (DDOS) attack shut down major websites around the globe. That was a wake-up call for the industry. Today, all major vendors are investing in IoT security on par with other security domains. Security companies and industry groups are accelerating work on standards, interoperability, certification, and security education. Businesses are rapidly moving from “security by obscurity” (my plant is not connected and thus secure) into comprehensive policy-based security architectures. These must be built into every part of IoT operations, focusing not only on before (how I can prevent hackers to enter my systems), but also during (how quickly I can identify I have been hacked and what data has been compromised), and after (how I can remediate the problem). Chief Security Officers now own these architectures for both IT and operations, and the industry is actively developing solutions for new security use cases, such as vehicle-to-vehicle communication or new security paradigms for 24/7 operations.

 

In addition, IoT is becoming the foundation for the growing adoption of other groundbreaking technologies such as blockchain and drones.

  • Blockchain allows a secure exchange of value between entities in distributed networks. Bitcoin is perhaps the most famous application of blockchain technology. However, enterprise-grade blockchain offers a wealth of applications that go far beyond any digital currency. For example, an energy company is looking at blockchain to manage the interactions between solar panels and the power grid. Automakers are considering the technology to authenticate the interactions among connected vehicles. Blockchain creates a tamper-proof record of transactions, so it’s ideal for tracing the source of goods throughout production and distribution. It can document food and drug safety, create smart contracts, and perform audits. Blockchain technologies (especially private, consensus protocol-based) are maturing quickly. We should see IoT production deployments later this year.

“Blockchain creates a tamper-proof record of transactions, so it’s ideal for tracing the source of goods throughout production and distribution. It can document food and drug safety, create smart contracts, and perform audits. Blockchain technologies (especially private, consensus protocol-based) are maturing quickly. We should see IoT production deployments later this year..”
  • Drones have been over-hyped for their commercial possibilities, denigrated for their clandestine applications, and dismissed as high-tech playthings. But the Internet of Things makes drones business worthy, especially when combined with AI and fog computing. AI-powered autonomous drones can work longer and more efficiently than piloted drones. They can choose the most efficient flight path automatically, and can change it on the fly to avoid bad weather, trees, power lines, and other obstacles. Surveyors and map-making companies can use drones to document remote, rugged terrain. The scope of drone use is expanding rapidly from pipeline or cell tower inspection to warehouse inventory management.

The whole point of these technologies—and IoT itself—is to work together for business benefits. That’s why standards are so important. Without standards, there cannot be interoperability. And without interoperability, benefits will be hard to find. The industry has been evolving rapidly from a collection of overlapping standards, semi-standards, specialized and proprietary technologies into true interoperable standards.

Such efforts have been focusing on three standardization thrusts:

-Interest groups in IEEE, IETF and other horizontal standards bodies are working to evolve existing horizontal standards to meet IoT requirements. Time Sensitive Networking in IEEE is a great example of evolving the Ethernet standard to meet manufacturing motion and safety requirements. This effort also meets in-car network requirements for level 3+ driverless vehicles.

-Vertical industry groups are migrating specialized or proprietary technologies to open standards. They are also standardizing foundational data fields essential for scalable data collection—for example, they are establishing a standard way to express “temperature” or “pressure” values. This effort is starting with controller-specific data and then moving to telemetry and diagnostics.

-Various consortia are developing frameworks and driving interoperability across their members’ implementations. One example is the OpenFog Consortium, which released the OpenFog Reference Architecture earlier this year.

Bottom line: I would give the state of the IoT technology a B-. On the plus side, technologies are maturing, solutions are becoming interoperable, and we see a lot of scalable production applications. On the down side, IoT security adoption by both businesses and vendors is lagging, as is migration to open standards. Both of these are slowing down and increasing the costs of implementations. Time to study up!

What do you think?

Join lively discussions in the new Building the Internet of Things community. For more IoT insights from industry thought and business leaders, sign up for my newsletter.

(c) istockphoto.com/ phive2015 | pobytov | wolv

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Model-Driven vs Data Driven methods for Working with Sensors and Signals

There are two main paradigms for solving classification and detection problems in sensor data: Model-driven, and Data-driven.

Model-Driven is the way everybody learned to do it in Engineering School. Start with a solid idea of how the physical system works — and by extension, how it can break . Consider the states or events you want to detect, and generate a hypothesis about what aspects of that might be detectable from the outside and what the target signal will look like. Come collected samples in the lab and try to confirm a correlation between what you record and what you are trying to detect. Then engineer a detector by hand to find those hard won features out in the real world, automatically.

Data-Driven is a new way of thinking, enabled by machine learning. Find an algorithm that can spot connections and correlations that you may not even know to suspect. Turn it loose on the data. Magic follows. But only if you do it right.
Both of these approaches have their pluses and minuses:

Model-Driven approaches limit complexity
Model-driven approaches are powerful because they rely on a deep understanding of the system or process, and can benefit from scientifically established relationships. Models can’t accommodate infinite complexity and generally must be simplified. They have trouble accounting for noisy data and unincluded variables. At some level they’re limited by the amount of complexity their inventors can hold in their heads.

“Data-Driven approaches based on machine learning require a good bit of data to get decent results. AI tools that discover features and train-up classifiers learn from examples, and there needs to be enough examples to cover the full range of expected variation and null cases.”

Model-Driven is expensive and takes time
Who builds models? The engineers that understand the physical, mechanical, electronic, data flow, or other appropriate details of the complex system — in-house experts or consultants that work for a company and develop its products or operational machinery. These are generally experienced experts, very busy, and are both scarce and expensive resources.
Furthermore, modeling takes time. It is inherently a trial-and-error approach, rooted in the old scientific method of theory-based hypothesis formation and experiment-based testing. Finding a suitable model and refining it until it produces the desired results is often a lengthy process.

Data-Driven is Data Hungry
Data-Driven approaches based on machine learning require a good bit of data to get decent results. AI tools that discover features and train-up classifiers learn from examples, and there needs to be enough examples to cover the full range of expected variation and null cases. Some tools (like our Reality AI) are powerful enough to generalize from limited training data and discover viable feature sets and decision criteria on their own, but many machine learning approaches require truly Big Data to get meaningful results and some demand their own type of experts to set them up.

Reality AI tools are data-driven machine learning tools optimized for sensors and signals. To learn more about our data-driven methods and to request a free trial, please visit www.reality.ai.

Author: Reality AI. Originally published here.
(c) istockphoto.com/ alex_west | artisteer

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The CIO outlook for 2017: Fulfilment high while digital adoption continues to go up

CIOs who say they are ‘very fulfilled’ in their roles is at a three year high, while more than half of organisations are investing in more nimble technology platforms.

These are among the key highlights of the annual CIO Survey from KPMG and Harvey Nash. The study, which analysed responses from almost 4,500 CIOs across 86 countries, found that in a large amount of cases, the CIO’s role remains important.

For more than seven in 10 respondents, the CIO role is becoming more strategic – the first time in a decade this has been the case – while the vast majority (92%) say they have been involved in board discussions over the past 12 months.

More than three in five (61%) of those polled in larger organisations say they are already investing, or plan to invest, in digital labour, although only one in five (18%) say their digital strategies are ‘very effective’.

The figures show a sense that the tide may be turning amidst choppy waters. As Dr Jonathan Mitchell, non-executive chair of the Global CIO Practice at Harvey Nash puts it in the report’s introduction, CIOs are ‘no strangers to rapidly changing environments and perhaps the odd crisis or two’.

“Layered on top of astonishing advances in technology is a political and economic landscape that is dynamic and changing fast, sometimes in surprising ways,” said Albert Ellis, CEO of the Harvey Nash Group. “However, what is very clear is that many technology executives are turning this uncertainty into opportunity, driving their organisation to become more nimble and digital.

“Layered on top of astonishing advances in technology is a political and economic landscape that is dynamic and changing fast, sometimes in surprising ways,” said Albert Ellis, CEO of the Harvey Nash Group. “However, what is very clear is that many technology executives are turning this uncertainty into opportunity, driving their organisation to become more nimble and digital.

“CIOs are becoming increasingly influential as CEOs, and boards turn to them for help in navigating through the complexity, and the threat and opportunity, which a digital world presents,” Ellis added.

So what does this digital world look like? Maribel Lopez, founder of Lopez Research, wrote of her experiences around enterprise transformation at the ET6 Transformation Exchange last month. “The best technology strategies will leverage a combination of mobile, cloud, big data and analytics to deliver new insight and new experiences,” Lopez wrote.

“A company simply can’t run a competitive business if it doesn’t support mobile. It can’t scale and shift models easily without cloud computing. And a business can only be competitive if it embraces new ways of collecting and analysing data to deliver new actionable insights.

“To thrive in the new digital era, we’ll all need to push beyond our existing boundaries.”

IT projects continue to be more complex for organisations, the report notes, with primary reasons given for failing initiatives including weak ownership, unclear objectives, and an overly optimistic approach. 61% say IT projects are more complex than they were five years ago. The fastest growing demand for a technology skill was enterprise architecture, while big data and analytics remains the most in-demand skill.

Originally published here. (c) istockphoto.com/ rawpixel | sensay

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