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Report: Automation in contact centers increases to 95%

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According to a new study by Replicant and Demand Metric, 95% of contact center leaders have either already adopted, are implementing, or plan to use automation within the next year. This figure shows how contact center automation is transforming a variety of industries. The “Automation in the Contact Center” report also explores the priorities and challenges of today’s contact centers, as well as the channels being deployed by contact center leaders. 

Consumers have grown to expect seamless and fast customer service, while at the same time contact centers have had to contend with hiring challenges and unpredictable call volumes. Based on surveys conducted by Demand Metric of more than 300 contact center leaders, 77% of respondents said that improving customer service is a top priority and 60% stated that addressing workforce issues is a chief concern.

The report also details budget and investment data surrounding contact center automation. The top challenges contact center leaders face are high or increasing costs (54%), followed by hiring enough agents (49%), agent turnover (39%) and handling call volume spikes (39%).

Indeed, the combination of dwindling customer patience and a shortage of workers has resulted in the perfect storm of hours-long wait times and employee turnover. But contact centers that have adopted automation are seeing immense value through reduced wait times, improved customer satisfaction and happier employees.

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For those still in the planning stages, a majority are looking to implement contact center automation soon — 80% of study participants say they’re evaluating automation and intend to invest in it within the next 12 months. Almost all contact center leaders (91%) report that automation is a critical or important priority in the next year.

While new automated customer service tools are now common, such as chatbots, a surprising 87% of study participants indicate that voice interaction remains the channel with the highest perceived value.

Understanding how contact center leaders are responding to automation is critical as organizations continue to seek a competitive advantage in customer service.

This 2022 Contact Center Automation survey was administered online from June 29th, 2022, until July 7th, 2022. During this period, 372 responses were collected, and 305 were qualified and complete enough for inclusion in the analysis.

Read the full report from Replicant.

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The emergence of the chief automation officer

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There definitely have been easier years than 2022 for trying to start a business. Compared to larger firms, smaller companies have a harder time absorbing shocks like inflation changes, supply chain disruptions, and changing demographics in the workplace. We see evidence that investors are starting to prefer to see proof of profits, rather than growth, an anathema to the startup founders of only a few years ago. At the same time, founders who embrace technological innovation have an immense opportunity. 

Through our work with companies of all sizes across industries around the world, we see that the convergence of these trends explains the increased focus on “intelligent automation” as organizations embark on digital transformation journeys. By applying artificial intelligence (AI) to IT operations (AIOps), robotic process automation (RPA), decision management, and business automation, companies can reduce costs and do more with less. Intelligent automation also helps to combat the global skills shortage by allowing employees to work on more engaging, value-adding tasks, as well as helps companies deliver exceptional customer experiences. 

Nine out of 10 employees who have used automation-based tools have improved their work-life balance. In short, automating processes makes companies healthier — with the critical caveat that they are applied thoughtfully, keep an eye on the user and employee experience, and provide a deliberate assessment of how the automation of a certain process impacts the organization as a whole. 

With this background as context, the role of the chief automation officer (CAO) becomes an important investment in a company’s digital transformation.  Not only is the role of the CAO rapidly emerging, but it is also growing in importance due to the positive impact automation is having on businesses across industries. The CAO will be responsible for implementing business process and IT operations decisions across the enterprise to determine when and what type of automation strategy is best suited for each business imperative while working with a wide range of leaders across all business pillars. 

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As part of a collaborative process, the CIO identifies areas for automation and modernization, the chief data officer (CDO) collects data insights from automating workflows, the chief AI officer (CAIO) implements advanced AI methods and algorithms in automation processes, and the COO align on change management. 

With the right automation processes and team in place, CAOs can measure success on the following indicators:

1. Every industry vertical and use case can benefit from AI and automation

AI-powered automation enables organizations to apply intelligence across their business, bridging gaps in workflows between business and IT. For example, IBM uses this approach of actionable intelligence to help organizations automate IT operations and business processes to lower costs and improve user experiences.

The CAO can use AI and automation to understand relationships and correlations, derive deep insights, and establish baseline KPIs. Without AI, data discovery associated with automation is mostly limited to structured processes and structured data. With AI, the discovery process is no longer blocked by a lack of structure. By utilizing AI, businesses can move from discovery to decision-making more naturally and collaboratively, increase employee engagement and productivity, and foster a more collaborative relationship between AI and employees.

There is no industry vertical where AI-powered automation’s relevance is not applicable today. Take manufacturing, for example. Automation supported by visualization algorithms can help detect defects in manufactured components on the assembly line. In electronics, automation can be used to detect the sounds of break-ins or automatic control of electrical appliances, in financial services to automate payments or customer behavior data, and in retail to transform the customer’s shopping experience. 

2. To combat the growing skills gap, a deeper focus on higher value work is needed

As baby boomers are leaving the market, approximately 2.4 million fewer workers are entering every year. The pandemic has also impacted many companies’ ability to recruit and overall values around work-life balance, impacting the skills available in the workforce. 

In fact, according to IBM’s recent Global AI Adoption Index 2022, the data shows steady AI adoption as organizations look to address skills shortages and automate processes. For example, by automating tasks for skilled workers so they can be more productive, or by using AI-assisted learning or employee engagement. Almost one-in-four companies are adopting AI because of labor or skills shortages, and 30% of global IT professionals say employees at their organization are already saving time with new AI and automation software abd tools. 

3. IT operations and core business processes are ripe for transformation

As I mentioned, AI and automation can transform IT and business processes to help improve efficiencies, save costs and enable people — employees — to focus on higher-value work. 

Two of the most important areas of IT operations in the enterprise are issue avoidance and issue resolution because of the massive impact they have on cost, productivity, and brand reputation. The rapid digital expansion among enterprises has led to an immediate uptick in demand from IT leaders to embrace AIops tools to increase workflow productivity and ensure proactive, continuous application performance. With AIops, IT systems and applications are more reliable, and complex work environments can be managed more proactively, potentially saving hundreds of thousands of dollars. This can enable IT staff to focus on high-value work instead of laborious, time-consuming tasks, and identify potential issues before they become major problems.

In addition to applying AI and automation to help improve IT operations, business automation is also well-suited for streamlining processes across just about every area of an organization. A few examples include sending out marketing emails to a client distribution list on a pre-defined schedule, automating job application processing, interview scheduling, employment offers, onboarding, payroll management, and benefits administration in human resources or automating repetitive tasks like qualifying leads, assigning prospects and automating invoices in sales and accounting. 

As organizations of all sizes continue to digitize and modernize their workflows, the CAO can help guide how AI and automation are used to modernize legacy IT systems and streamline business processes, so employees can focus on projects that are truly impactful. 

The CAO is important because their experience is versatile. Not only can they use AI to power automation across many industry verticals and use cases to address the growing gap in skilled workers, but they can also work hand-in-hand with the CIO, the CDO, the CAIO and the COO to transform core business functions that impact the bottom line. 

Dinesh Nirmal is the general manager of data, AI and automation at IBM.

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How data and automation can help with sustainability

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The entire world is in the midst of a digital transformation, which has changed daily operations for countless businesses across industries. Technological advancements such as artificial intelligence (AI) and automation are helping company leaders operate at greater efficiency than ever before, generate revenue, and perhaps even make the world a better place in the process. But how?

Why sustainability makes sense

For years, companies of all sizes have recognized the inherent value of environmental, social and governance (ESG) initiatives when it comes to customer retention and smooth overall operations. Sustainability strategies are a smart business move that may foster company longevity and keep customers coming back.

However, while plenty of company leaders recognize the critical importance of sustainable initiatives, only about one-fourth of companies include sustainability as part of their business model, according to the International Institute for Management Development (IMD). For the greatest chance of long-term business success, the Switzerland-based organization encourages executives and company policymakers to first comply with local laws and regulations and then take a more proactive approach to sustainability. 

To that end, data and automation can help, by giving established companies and startups alike the necessary tools to meet their sustainability goals. 

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Breaking barriers and implementing green initiatives

Ideally, a company’s sustainable initiatives should be authentic and environmentally focused, rather than rooted in the hope of increased profits. Today’s tech-savvy consumers increasingly use their spending power to support environmentally conscious companies and are even willing to shell out a few extra dollars on sustainable products and brands. Forward-thinking companies can maintain transparency by disclosing their sustainability goals and initiatives publicly and by encouraging customer feedback. 

Yet, that feedback won’t amount to much without the ability to make sense of it all, and automation can be a game-changer in this regard. Automation software can help alleviate some of the burdens of data interpretation, enabling companies to speed up their green initiatives and saving time and money. For example, with automation software on hand, companies can quickly and easily track energy usage, amount of waste produced daily, consumer habits, carbon footprint and more, in an effort to streamline operations. 

Depending on the amount of data collected, it could take months for a human worker to properly organize and analyze the relevant information. Technology gets us there much faster, with greater accuracy. 

Data-powered insights to inform optimization

When it comes to a company’s sustainability goals, waste reduction tends to be at the forefront of the conversation and for good reason: while it’s difficult to determine the exact numbers in terms of industrial waste production, waste generation is a massive global problem that’s only expected to grow. What’s more, solid waste management is an inherently wasteful process in its own right, contributing some 1.6 billion tons of greenhouse gas emissions into the atmosphere in 2016 alone, according to The World Bank. 

A massively wasteful industry, manufacturing may benefit greatly from the data-automation-sustainability interplay, starting with mindful inventory management. Excess inventory can clog up the supply chain and landfills alike. Yet, through data-based insights and intelligent automation, businesses may be able to strike a balance between too much stock and not enough, significantly reducing waste, emissions and overall environmental impact. 

Increased efficiency of processes

Waste comes in many forms, and countless businesses are guilty of wasting time. The adage “time is money” comes into play here — inefficient processes and redundancies can significantly hinder day-to-day operations while wasting company time and money. The good news is that automation can help bridge some gaps, improving efficiency of processes in every corner of the supply chain. 

Human error contributes significantly to the problem of inefficiency and wasted company time, and company leaders across industries are taking note. Companies can decrease workplace stress and redundancies via workflow automation, allowing employees to focus on meaningful work and potentially make fewer mistakes. Companies looking to implement workflow automation into their sustainability plan should start small and identify the operations wherein automation will reap the biggest payout. That payoff could encompass financial goals, environmental goals, or another plan altogether. 

Weighing cost vs. benefit

For small business owners, implementing sustainability initiatives may seem more like a pipe dream than a tangible goal, as the technology can be costly to implement. What’s more, businesses that are using technology to drive sustainability must employ talented workers who can tap into those resources and streamline operations for the greatest economic and environmental benefit. 

However, as companies can leverage automation and data analytics to increase efficiency, adjust energy usage, reduce waste and otherwise help with sustainability, the cost of investing in automation is worth it. By giving company leaders the ability to see the big picture in terms of carbon footprint, data and automation can help optimize operations and improve a company’s bottom line. 

Charlie Fletcher is a freelance writer passionate about workplace equity, and whose published works cover sociology, technology, business, education, health, and more.

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How Automation Hero uses accurate AI to process documents

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Before Dr. Alan Turing designed the first computer, people merely dreamed of intelligent machines that could read paperwork and do most of their grunge work for them. Science-fiction movies depict advanced software processing large amounts of documents to find hidden insights that save the day. Today this is available in real life from progressive-thinking software providers. 

One of them, San Francisco-based Automation Hero, today launched v6.0 of its Hero Platform, a SaaS service the company claims takes a quantum leap in OCR (optical character recognition) document-processing accuracy. It also can read handwriting with 70-80% accuracy. 

Yes, this includes doctors’ notoriously bad handwriting, Automation Hero founder and CEO Stefan Groschupf told VentureBeat. 

The Hero Platform has shown, both in beta trials and in actual production, that it can unlock millions of dollars in return on investment (ROI) for companies such as MarkerStudy, turning documents into actionable insights in claims processing, supply chain optimization, fraud detection and customer-interaction automation.

Faster new AI engine

The latest version of the AH platform provides a faster new AI engine for document processing and automation, Groschupf told VentureBeat. The company’s deep learning-based approach together with its fast-reading OCR process can turn structured or unstructured documents – such as contracts, invoices, receipts, prescriptions, doctor notes and purchase orders – immediately into highly accurate and actionable data, Groschupf said. 

Modeled on cognitive science, Automation Hero’s OCR engine turns scans of documents into data similar to how humans read even the most difficult handwriting with contextual cues. The patent-pending technology is combined with an intuitive-to-use natural language understanding engine, all within a single end-to-end platform, Groschupf said. 

While this seems far-fetched, the goal is to deliver the highest ROIs in the industry within weeks without any data science or large training data required, Groschupf said. 

“Our job at Automation Hero is to accelerate health care, get insurance claims paid out faster, and find bad actors that over-invoice or bring to the surface contracts that can be renegotiated,” Groschupf told VentureBeat. “Our mission is to provide overwhelmed employees with the AI superpowers to work faster through the mountain of documents that keeps stacking up on their desk or in their inbox.

“We have AI that goes to a document management tool and processes the data to make it actionable to automate certain business processes.”

Faster than Google’s OCR, Automation Hero claims

Based on new benchmarking data, the technology outperformed handwriting OCR alternatives ABBYY and Google Vision by approximately 280% and 40% respectively, Groschupf said. It also outperformed ABBYY and Rossum in AI-based invoice processing in a separate benchmark by 60% and 100% respectively, he said.

Insurance claims processing is Automation Hero’s best business right now, Groschupf told VentureBeat. 

“Eighty percent of all data in companies is in documents. This is a treasure chest that historically was not tapped,” Groschupf said. “And if you think about all of this business intelligence and ETL, it’s a multi-billion dollar market in which we are all operating on 20% structured data. Historically, humans have stored so much unstructured data – emails, contracts, images, invoices, infrastructure data, etc. – that it took days or weeks to get through previously.

“We can do things that you couldn’t do before, such as constantly mining hundreds of thousands of contracts, automating 60,000 claims a day, and so on. We really want to be a bicycle for people’s minds, mining way more documents than we could do before.”

Automation Hero’s unique approach to OCR has decreased tedious claims administration work by hundreds of hours, transforming the way we handle customer claims and manage employee satisfaction, Eoin Grace, deputy head of IT at Markerstudy, said in a media advisory. 

“Previously, we were manually deciphering doctors’ diagnoses, stamped addresses, and handwritten notations, across a wide variety of claims forms,” Grace said. “Automation Hero’s intelligent OCR technology delivers above-human accuracy.”

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Automation is not enough: Buildings need AI-powered smarts

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Buildings have been one of the most voracious users of IoT devices. Smart buildings, in particular, use connected devices to measure everything from temperature, lighting, air quality, noise, vibration, occupancy levels and energy consumption — and that’s just the very tip of the iceberg.

Building automation is big and getting bigger, with well over 6 million commercial buildings in the U.S. alone and an estimated 2.2 billion connected devices deployed. The global market for building automation systems in 2022 will reach about $80 billion. 

This type of automation relies on fleets of IoT devices. Many condition-action responses are automated; if a fire is detected, alarms are automatically triggered, often with voice instructions and fire departments are notified. That was true before the IoT; now fire alarms are connected by the Internet and secondarily via cellular communication. 

The value of IoT, in building automation specifically, is realized in two main areas:

  • The data generated by in-building devices and how it is analyzed and leveraged. 
  • The actions and management performed by building automation systems

Rich, ongoing data streams provide valuable insights into building operations, but there’s an issue: large device fleets create large volumes of data that humans alone cannot properly parse and understand. To realize the potential payoff from deploying these sensors (and cameras), artificial intelligence (AI) and machine learning (ML) is needed to continuously monitor and assess the data streams. 

Automation can’t do the job alone

Until 2020, the emphasis of smart buildings systems, including building automation, was the responsibility of facilities’ management. Then, the focus shifted to employee health and ESG initiatives, in addition to facilities management. This opened up demand for capabilities that ML enables.

An AI system can observe air quality and find correlations with occupancy limits, for instance. It can also learn how to reassign conference rooms and cubicles, relating to occupancy and ventilation, with the goals of maximizing the physical distance between employees and improving air quality, to reduce the chance of employee illness. 

AI can also help analyze the usage of water supply pipes and water temperature to warn when there is an elevated risk of legionella and other harmful pathogens. Legionella thrives in specific temperature ranges of warm water. 

The relevance of new AI-enabled capabilities does not rule out the traditional functions such as tracking and managing energy consumption. With an AI-driven platform, a building can power down areas that are not in use and try different window shade settings at different times, to minimize energy usage. Experiment and learn as it goes. This is a bottom-line issue and will become more important in 2022 due to energy prices. 

AI can even play a role in cleaning efficiency, identifying which desks have been used and which toilets have seen increased usage. In the age of COVID-19, facilities managers are focused on cleanliness. 
AI can greatly enhance systems that support physical security, too. Once a system learns what constitutes normal access and movement behavior, it can identify anomalous behavior and alert security. Other AI-driven applications can detect duress situations, abandoned objects,  recognize weapons, pinpoint shots fired—and carry out emergency lockdowns.

An intelligent infectious disease control system can learn to leverage data on local infection rates. AI systems can do things people cannot, like staring at a wall for 20 years and looking for signs of change in the concrete that could herald a pending structural collapse.

Applying AI for smart buildings

The standard starting point for a new AI-driven system is, of course, teaching it. That process begins with a foundation of data that represents the realities that the system will confront. Many will find, however, that good base training data for smart-building systems does not exist. The answer can be to create the training data by running ‘experiments’ in the physical building. 

In energy consumption, for example, you can train a system by experimentally adjusting window shades and AC based on the time of day and office occupancy, to lower AC bills without triggering a manual override. Such a system could rely on temperature sensors and occupancy readings, as well as sunlight detection. 

There are basic best practices to follow. Be scientific and rigorous when collecting ground truth datasets and collect data from multiple sources to increase confidence that your samples are representative. 

AI-driven systems can learn from the occupancy patterns of specific office areas and help reduce human error in space planning. Upgrading space is costly and preserving flexibility is vital. Space utilization and occupancy obviously became a health issue during the pandemic. Employees may now prefer to gather for conversation and coffee on an open-air balcony or patio, not in a small break room. 

Where AI-driven building management is heading

AI-powered systems can recommend changes to facilities management and allow building management to be more predictive. When it comes to reactivity, they enable a more effective response to surprise challenges as well. A recent example; before 2020, identifying employees who are running hot (fever) and reducing the probability of infection probability was not a thing, but it is within current capabilities to address this problem.

It takes careful thought and putting in the time, to get the ground truth right. Many commercial buildings have a digital twin; a virtual replica delivered by the architect to the building owner or manager. The digital twin, as a starting point, may well be a testing ground for AI-driven facilities management and smart building management.

We expect that IT, facilities management, HR and security will become more integrated and make increased use of AI. There is a range of likely benefits from joining their information silos to create data streams for AI applications. 

The importance of healthy workplaces, physical security and energy conservation makes it urgent to go beyond simple automation and develop reliable AI-based building operating systems that are founded on robust, up-to-date data. Any of these applications support a strong business case; taken together, they make a persuasive argument that facilities’ management should look at AI-driven applications for operating smart buildings and making buildings smarter. 

William Cowell de Gruchy is the founder and CEO of Infogrid.

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Report: Data and enterprise automation will drive tech and media spending to $2.5T

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According to a new report released by Activate Consulting, the global technology and media spend will balloon to $2.5 trillion by 2025.  This analysis comes as 2021 netted a spend of more than $2 trillion.

The report indicates that one of the major drivers of this tech boom will be data solutions and enterprise automation.  According to the report, “Activate Technology and Media Outlook for 2022,” a set of new companies are paving the way for the future, delivering infrastructure, tools, and applications that will enable all enterprises to operate and innovate as if they were major technology companies.

Businesses and consumers can expect to see accelerated development of customer experiences, better (faster, less bureaucratic) employee experiences, improved intelligence and decision-making, and improved operational and financial efficiency as a result.  Technology like autonomy (self-driving cars, home automation), voice recognition, AR/VR, gaming and more will enable end-user experiences while enterprises will become more productive in their marketing effectiveness, IT service management, cross-planning and forecasting, and more.

New data startups are spurring the next era of innovation.  They’re focusing on leveraging data and information, improving end-user experience, and improving storage and connectivity — all of which will drive the business-to-business and business-to-consumer experiences of the future.

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According to the report, more than 80% of the companies driving this innovation are U.S.-based, half of which are headquartered in the Bay Area.  They’re growing fast thanks to large venture capital infusions – and many of these startup companies have scaled at an unprecedented pace.  Fifteen of them have raised more than $1 billion since their launch.

In order for the next generation of companies to reach their full potential, the report indicates they must zero in on three specific areas of focus: strategy and transformation, go-to-market pricing, as well as their sales and marketing approach.

Read the full report by Activate Consulting.

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Posh Technologies fuels call center automation with $27.5M funding

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As call volumes skyrocketed during the pandemic, contact centers turned to AI to help distribute the workload. But even before the pandemic, customer service departments were experimenting with automation solutions, including chatbots and transcribers, to streamline operations. A 2019 Deloitte survey found that 76% of contact centers were planning to invest in AI in the next two years. According to that same survey, 57% of companies were testing the use of AI in assisting customer service agents. 

Anticipating the trend, Karan Kashyap founded Posh Technologies, a Boston, Massachusetts-based conversational AI and natural language processing technology development company, in 2018. Today, Posh announced that it raised $27.5 million in series A funding led by Canapi Ventures. Kashyap, who serves as CEO, says that the proceeds will be put toward supporting additional investment in product research and development and the expansion of Posh’s platform.

“Posh’s growth accelerated during the pandemic amid the increasingly digital world which we continue to live in. Just as the pandemic started, we were already getting ready to hit the gas pedal. The pain points and needs of financial institutions changed from the pandemic to our benefit, including needing to better manage customer service on a 24/7 basis, managing increased call volumes from closed branches, and doubling down on self service solutions,” Kashyap told VentureBeat via email. “There was also high turnover for those customer service jobs — the ‘great resignation’ took a toll on call center jobs too. While Posh’s aim is not to replace human agents, our technology helps our customers address higher volumes and augment their current service models.”

Augmenting customer service

Kashyap, who has a bachelor’s degree in computer science and a master’s in AI, developed Posh’s technology while studying at MIT. The platform provides chatbots that automate customer questions and workflows on the web, SMS, and messaging apps for tasks like checking hours and making payments. A separate IVR bot replaces traditional dialpad menus with natural, voice-driven conversations with customers.

On the backend, Posh automates contact center and help desk FAQs and workflows, leveraging machine learning and natural language processing to give chatbots “memory persistence.” Concretely, Posh’s systems train on domain-specific data so that its chatbots understand some of the nuances of a given industry’s — and company’s — language.

Posh integrates with live chats as well as other “API-friendly systems” (e.g., digital banking databases and telephony) and escalates to human reps if need be. Customers get metrics showing how conversations went and where areas for improvement might exist.

“Our AI can easily manage routine inquiries without requiring staff involvement. We see it as the first line of defense to get people out of queues while also enabling round-the-clock self service,” Kashyap said. “Credit unions and banks are often able to answer customers’ questions directly on their website through the Posh chatbot feature. In cases where the chatbot doesn’t have the right answer, it can intelligently escalate the request to a call center or in-person representative, significantly improving both the amount of money spent on customer service as well as the customer experience.”

Competition

Beyond incumbents like Google, Microsoft, Salesforce, and Amazon, Posh competes with a number of startups in the expanding call center automation space. Yellow.ai, a chatbot platform headquartered in Bangalore, India, recently raised $78 million in venture capital to expand its platform globally. There’s also Ada, a Toronto-based startup developing AI-imbued customer service chatbots.

Grand View Research anticipates that the global contact center software market will be worth $90.6 billion by 2028, if the current trend holds.

Posh Technologies | Enterprise Conversational AI

Kashyap argues that Posh’s focus on the financial services industry gives it an advantage over rivals targeting a broader range of segments. To date, Posh has partnered with more than 50 financial institutions to deploy web-based and mobile-based digital agents, and the company’s software handles tens of thousands of chats per day and reaches over 5.5 million people.

“We serve approximately 50 community financial institutions — banks and credit unions — across the U.S. and their end users and members. Our digital assistants and voice banking assistants handle tens of thousands of requests a day on behalf of these financial institutions,” Kashyap said. “We are very focused on financial services and thus train our AI models to be very domain-focused. Not only are we focused on training models with the goal of automating routine banking inquiries and workflows, we’re also using AI to glean insights from conversations that pass through our system — for example, uncovering operational root causes or detecting anomalies.”

Going forward, Canapi Ventures partner Neil Underwood expects that 40-employee Posh will benefit from expanded access to credit unions, banks, and prospective talent through its other backers Curql Collective, CMFG Ventures, JAM Fintop, Human Capital, and Piedmont. In the coming months, Posh plans to ramp up hiring to keep pace with what it describes as “surging” demand.

“Beyond answering questions, Posh has developed a competency in helping banks complete simple banking transactions. Especially for credit unions, who are highly focused on member experience, this can be a meaningful value add,” Underwood told VentureBeat via email. “Over time, we anticipate that the Posh platform will be used by credit unions and banks to drive entire banking interactions.”

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Natural language processing is shaping intelligent automation

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This article was contributed by Pascal Bornet & Rachael Churchill. The content of this article is inspired by Pascal’s book Intelligent Automation.

Natural language processing is the name usually given to computers’ ability to perform linguistic tasks — although in practice it includes more than just language processing (understanding text and speech) but also includes language generation (creating text and speech).

Natural language processing (NLP) is one component of intelligent automation, a set of related technologies that enable computers to automate knowledge work and augment the productivity of people who work with their minds. The other components of intelligent automation are computer vision (interpreting images and videos, such as in self-driving cars or medical diagnostics), thinking & learning (for example, evolving strategies and making decisions based on data), and execution (interacting with the physical world or with existing software, and chaining the other capabilities together into automated pipelines).

Below are just some applications of natural language processing that are being deployed today and how they can help your business.

Natural language processing technologies

Chatbots and cognitive agents

Chatbots and cognitive agents are used to answer questions, look up information, or schedule appointments, without needing a human agent in the loop.

Simple chatbots can be programmed with a basic set of rules (“if the user says X, you say Y”); more advanced chatbots or “cognitive agents” use deep learning to learn from conversations and improve themselves, and can be mistaken for humans.

Many chatbots are text-based, interacting with users via instant messaging or SMS, but some use voice and even video. Notable examples are ANZ Bank’s “Jamie” chatbot, which guides customers through the bank’s services, and Google Duplex, which can make phone calls to book hair appointments or restaurant tables, even speak to unsuspecting receptionists who don’t know it’s a bot.

Unstructured information management

Unstructured information management (UIM) platforms are used to process large amounts of unstructured data and extract meaning from them without the need for lots of manual keyword search queries, which are time-consuming and error-prone. They are a vital component of natural language processing and process unstructured documents such as journal articles, patents, contracts, and health records, and build a structured, searchable knowledge base. They can also classify the data and look for clusters and trends within it.

Sentiment analysis

Sentiment analysis uses natural language processing to extract sentiments, such as approval or disapproval of a brand, from unstructured text such as tweets.

Speech analytics

Speech analytics is a component of natural language processing that combines UIM with sentiment analysis. It’s used by call centers to turn text chats and transcriptions of phone conversations into structured data and analyze them using sentiment analysis. This can all be done in real-time, giving call center agents live feedback and suggestions during a call, and alerting a manager if the customer is unhappy.

Machine translation

Machine translation is an enormously powerful application of NLP. Currently, it is usually not powerful enough to produce fully grammatical and idiomatic translations, but it can give you the gist of a web page or email in a language you don’t speak. 500 million people each day use Google Translate to help them understand text in over 100 languages.

Information classification

Information classification or categorization is used for spam filtering, among other things. It works using the same kind of machine-learning model that’s used to classify X-rays and other medical images into healthy and diseased, or used by self-driving cars to decide whether something is a stop sign. Rather than being programmed with explicit rules, the computer is given a large amount of training data in the form of known spam emails and known legitimate emails, and it extracts its own evidence-based rules from them for classifying new emails.

Components of natural language processing that can help your business

Chatbots and cognitive agents

Chatbots and cognitive agents can improve your bottom line by replacing call center staff for straightforward customer queries, and augmenting human call center agents for more complex queries, allowing you to expand your customer base and market share and improve customer satisfaction without needing to employ and train more agents.

Unstructured information management

Unstructured information management platforms allow you to automate a lot of research work: for example, lawyers can use them to run intelligent queries over existing patents or case law, and medical researchers can use them in drug discovery or look for relevant gene interactions in the literature. Rather than spending time poring over reams of documents, a human researcher can quickly review the suggestions and insights provided by the UIM platform, making them more productive overall and freeing up their time and mental energy for the more creative and high-level aspects of the job.

Sentiment analysis

You can use sentiment analysis to perform automatic real-time monitoring of consumer reactions to your brand, especially in response to a new product launch or ad campaign, which will help you to tailor your future products and services accordingly. It can also automatically alert you to any eruptions of criticism or negativity about your brand on social media, without the need for human staff actively monitoring channels 24/7,  so that you can respond in time to avert a PR crisis.

Speech analytics

Speech analytics can augment the skills of your call center staff, improving customer satisfaction without the expense and opportunity cost of additional training. You can also use speech analytics to detect conversation patterns that lead to successful sales, or opportunities for cross-selling or up-selling based on customer behavior. This can help elevate mediocre telesales agents into star salespeople, enabling them to share and deploy the talents of their more skilled colleagues, making a significant impact on your top line without any expenditure on recruitment or training.

Machine translation

Machine translation can allow you to read relevant articles which your competitors might not have seen if they’re published in a minority language, to share knowledge internationally across your business, and to communicate with international colleagues or suppliers without the overhead of a human translator (although for communicating with customers it may still be advisable to employ one in order to make a good impression).

Information classification

Information classification has a variety of useful applications. As well as saving you time and irritation by filtering out spam, this technology can be used to automate domain-specific classification tasks. For example, it could categorize and tag the products in a catalog, making it easier for customers to browse and purchase them; or it could filter social media posts for hate speech, mitigating legal and reputational risks without needing a large team of human moderators; or it could categorize support tickets and automatically forward them to the correct person, saving manual effort and improving overall response times.

Natural language processing: a case study

This is an example from my own experience of the benefits of using cognitive agents to improve customer satisfaction and reduce employee turnover.

A hotel chain employed a team of 240 customer care agents to deal with over 20,000 customer interactions per day, including phone calls, email, and social media. The team’s morale was low due to the high pressure and workload, and employee turnover was 40%. This had a knock-on effect on the quality of customer service, which was rated less than five out of 10.

The company deployed an omnichannel cognitive agent to interact with customers across email, social media, and voice calls. The cognitive agent was designed to look and behave similarly to human agents, and used machine learning to improve itself and learn from its previous conversations. It could also recognize users based on biometric information, such as voice or facial recognition, and it could autonomously process changes in systems.

After three months, the customer satisfaction rating had improved from five out of 10 to nine out of 10, employee turnover had decreased by over 70%, and the human team members were under less pressure and were able to focus on more complex and higher value-add interactions requiring greater relational skills.

Language is how humans naturally communicate, so computer interfaces that can understand natural language are more powerful and easier to use than those that require clicking buttons, typing commands, or learning to program, and it’s important to understand the components of natural language processing. Natural language interfaces are the next step in the evolution of human-computer interaction, from simple tools to machines capable of event-driven and automated processes, potentially even leading to a kind of symbiosis between humans and machines.

This article was contributed by Pascal Bornet & Rachael Churchill. The content of this article is inspired by Pascal’s book on Amazon, Intelligent Automation. 

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AI

SnapLogic seeks to accelerate digital transformation with enterprise automation

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more


Following a pandemic that halted the world in 2020 and forced organizations to develop new ways to do things, more companies are now leveraging cloud-based technologies for their business operations. With Gartner forecasting the global hyperautomation-enabling software market to reach nearly $600 billion by 2022, application management and data integration are playing a key part in the increased automation that enterprise technologies are now being built for.

Hyperautomation is no longer an option but a condition of survival for organizations, according to Fabrizio Biscotti, research vice president at Gartner. “Organizations will require more IT and business process automation as they are forced to accelerate digital transformation plans in a post-COVID-19, digital-first world,” said Biscotti in a report.

SnapLogic, a San Mateo, California-based company, offers an application management and data integration platform for on-premises or cloud-based data and process flow acceleration. SnapLogic’s chairman and CEO, Gaurav Dhillon, told VentureBeat that SnapLogic’s technology uses AI-powered algorithms to provide enterprises moving huge volumes of data with high-level automation — enabling them to be seven times more productive than when they use traditional batch-based reporting.

Dhillon says enterprise automation is the future, adding that enterprises are going to be hybrid and multicloud for the foreseeable future, as they operate with a combination of the technologies they already have while exploring newer, more effective technologies.

Latest trends in digital integration

Commenting on the latest trends in digital integration, Dhillon noted there’s no question that the future is going to be highly automated. The big difference between the old world and now is that today, Dhillon said, it’s not just about business intelligence and reporting — AI also needs data.

Gartner's Top Strategic Technology Trends for 2022. Topics include: data fabric, cybersecurity mesh, privacy-enhancing computation, cloud-native platforms, composable applications, decision intelligence, hyperautomation, AI engineering, distributed enterprise, total experience, autonomic systems, and generative AI.

Gartner’s top strategic technology trends for 2022 highlights data fabric, which involves data integration across platforms and users, cloud-native technologies, AI, decision intelligence, and hyperautomation as part of the “12 trends that will accelerate digital capabilities and drive growth for technology executives in 2022.”

“In the old days, the consumers of data — who are usually humans — needed the data to make decisions. Today, the consumer of data is likely to be an algorithm, which needs data to do a better job of running the business, as well as the analytics to understand process flows and make smart decisions like taking customer orders, fulfilling each order level, tackling shipping logistics, and more,” said Dhillon.

With so many SaaS applications in the enterprise today, Dhillon noted that the future will have a high degree of automation. “We can have autonomous cars. Why can’t we have autonomous integration? We call that enterprise automation — one platform that connects your apps, data, APIs, and more,” said Dhillon.

Dhillon claims many enterprises like Schneider Electric, AstraZeneca, Adobe, Box, Yelp, Kaplan, and others are procuring SnapLogic’s product because they see it as bringing the best of both worlds — connecting legacy technologies and the cloud in a way that nobody else can.

Transitioning from relational data to unstructured data

Today’s data integration requirement is more informed in real-time business processes and automation of mundane tasks, Dhillon explained. SnapLogic’s continued forward momentum in the data integration landscape saw the company named the only visionary in Gartner’s Magic Quadrant for Data Integration Tools, with other companies like Informatica — where Dhillon was once cofounder and CEO — as well as Microsoft, Oracle, Talend, IBM, and others.

Image of scatterplot graph showing various enterprises measured by completeness of vision in the X-axis and their ability to execute in the Y-axis. All findings can be found in the original copy of the article.

While the company moves about 2.7 trillion JSON documents through its system monthly, up from 10 million documents monthly about four to five years ago, Dhillon said the real headline is the change in data. The transition from relational data with rows and columns like an Excel spreadsheet to unstructured data like the web, is the real story in the future of data integration, Dhillon said.

“There’s so much unstructured data in the world: web browsing, data from machines, data from routers and firewalls, and more. This type of data will become more important this year to next year, and even at the end of the decade. Therefore, companies that are built from the ground up to handle unstructured data are more likely to provide value to their customers.”

Key competitors and differentiation in the industry

Most enterprises, particularly service industries like banking and insurance, are most likely to have one-third of their employees moving data for the other two-thirds. This is a big problem that Dhillon claims is becoming bigger because of the amount of SaaS applications and unstructured data in the enterprise today.

SnapLogic’s first competitors are people writing codes by hand and those who get some open-source technology from GitHub to try to accomplish tasks. SnapLogic also has strong competition in legacy products from companies like Informatica, Microsoft, and IBM, as well as some new cloud products coming up and focusing on more cloud-based applications.

According to Dhillon, SnapLogic was built for hybrid-multicloud solutions providers. He claims SnapLogic is differentiated in the industry because of its capability to provide multicloud productivity in a way that both legacy companies and the new companies cannot.

Dhillon claims SnapLogic’s technology enables technical decision-makers to move beyond the frustrating process of data siloing. “Pillar applications like CRM, marketing apps, data warehouses all go to the cloud, and during this process, the legacy interconnectivity existing between these apps breaks. The most important thing for technical decision-makers today is that SnapLogic provides the connective tissues that connect data, application, supplies, and API management capabilities in one suite of products,” he concluded.

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Simpro raises $350M as demand grows for field service automation software

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Field service management, which refers to the management of jobs performed in the field (e.g., telecom equipment repair), faces continued challenges brought on by the pandemic. While the number of customer service inquiries has increased as enterprises have adopted remote work arrangements, worker availability has decreased. Forty-seven percent of field service companies say that they’re having trouble finding enough quality technicians and drivers to meet business goals, according to a Verizon survey. The shortfall in the workforce has increased the burden on existing staff, who’ve been asked to do more with fewer resources.

Against this backdrop, Simpro, a field service management software company based in Brisbane, Australia, today announced that it raised $350 million from K1 Investment Management with participation from existing investor Level Equity. The new funding brings Simpro’s total capital raised to nearly $400 million, which CEO Sean Diljore says will be put toward product development and customer support with a particular focus on global trade and construction industries.

Simpro also revealed today that it acquired ClockShark, a time-sheeting and scheduling platform, as well as AroFlo, a job management software provider. The leadership teams of Simpro, ClockShark, and AroFlo will operate independently, Diljore says, including continued work on existing services.

How to Set Up Maintenance Planner | simPRO

Above: Simpro’s maintenance-planning dashboard.

“This investment marks the next stage of Simpro’s exciting growth journey. Our mission is to build a world where field service businesses can thrive,” Diljore said in a statement. “We’re thrilled to welcome ClockShark and AroFlo to the Simpro family. Both companies are leaders in their spaces and have incredibly valuable product offerings that will benefit our combined customer bases and help our customers increase revenue. We look forward to growing together and building a range of solutions for the field service and construction industries.”

Managing field service workers

Field service workers feel increasingly overwhelmed by the amount of tasks employers are asking them to complete. According to a study by The Service Council, 75% of field technicians report that work has become more complex and that more knowledge — specifically more technical knowledge — is needed to perform their jobs now versus when they started in field service. Moreover, 70% say that both customer and management demands have intensified during the health crisis.

Simpro, which was founded in 2002 by Curtis Thomson, Stephen Bradshaw, and Vaughan McKillop, claims to offer a solution in software that eases the burden on field workers and their managers. The company’s platform provides quoting, job costing, scheduling, and invoicing tools in addition to capabilities for reporting, billing, testing assets, and planning preventative maintenance.

Bradshaw, a former electrical contractor, teamed up with McKillop, an engineering student, in the early 2000s to build the prototype for Simpro in the early 2000s. Working out of Bradshaw’s garage, they started with the development of job list functionality before adding new features, including a scheduling tool for allocating resources.

Today, Simpro supports over 5,500 businesses in the security, plumbing, electrician, HVAC, and solar and data networking industries. It has more than 200,000 users worldwide and more than 400 employees in offices across Australia, New Zealand, the U.K., and the U.S.

An expanding product

With Simpro, which integrates with existing software including accounting and HR analytics software, customers can use digital templates to build estimates and convert quotes into jobs. From a dashboard, they can schedule field service workers based on availability and job status, plus perform inventory tracking, connect materials to jobs, and send outstanding invoices.

Diljore expects the purchases of ClockShark and AroFlo to bolster Simpro’s suite in key, strategic ways. ClockShark, a Chico, California-based company founded by brothers Cliff Mitchell and Joe Mitchell in 2013, delivers an app that lets teams clock in and out while recording the timesheet data needed for payroll and job costing. Ringwood, Australia-based AroFlo, on the other hand, provides job management features including field service automation, work scheduling, geofencing, and GPS tracking.

Reece is now available for Automatic Catalog and Invoice Sync | simPRO

AroFlo and ClockShark claim to have over 2,200 and 8,000 customers, respectively. AroFlo’s business is largely concentrated in Australia and New Zealand, where it says that over 25,000 workers use its platform for asset maintenance, compliance, and inventory across plumbing, electrical, HVAC, and facilities management.

Somewhat concerningly from a privacy standpoint, AroFlo offers what it calls a “driver management” feature that uses RFID technology as a way of logging which field service worker are driving which work vehicles. Beyond this, AroFlo allows companies to track the current and historical location of devices belonging to their field workers throughout the workday.

While no federal U.S. statutes restrict the use of GPS by employers nor force them to disclose whether they’re using it, workers have mixed feelings. A survey by TSheets showed that privacy was the third-most important concern of field service workers who were aware that their company was tracking their GPS location.

In its documentation, AroFlo suggests — but doesn’t require — employers to “speak to [field] users about GPS tracking.”

Aroflo GPS lets you monitor your field technicians across the entire day,” the company writes on its website. “You’ll always know where they are, what they’re working on, and when they finish.”

A spokesperson told VentureBeat via email: “Simpro will continue offering GPS services and also has its own vehicle GPS tracking add-on, SimTrac. Implementation of GPS fleet tracking can help reduce risks, remain compliant with licenses and vehicle upkeep, and reduce costs in the business. It also benefits the technicians by improving their safety, spending less time in traffic and improving time management. Overall, GPS tracking provides improved visibility of staff and understanding of their location, introduces opportunities to reduce costs associated with travel, schedule smarter and even improve driver safety (by limiting their need to race across to another side of town to complete a job).”

A growing field

The field service management market is rapidly expanding, expected to climb from $2.85 billion in value in 2019 to $7.10 billion in 2026. While as many as 25% of field service organizations are still using spreadsheets for job scheduling, an estimated 48% were using field management software as of 2020, Fieldpoint reports. Customer demand is one major adoption driver. According to data from ReachOut, 89% of customers want to see “modern, on-demand technology” applied to their technician scheduling, and nearly as many would be willing to pay top dollar for it.

“The pandemic made many business owners realize how crucial it is to have the right technology in place for remote work. Trades businesses couldn’t afford to abandon projects or lose out on service and maintenance calls because of delayed response times or drawn-out time to complete,” Diljore told VentureBeat via email. “For these businesses, cloud-based software became a necessity for survival when previously it was a ‘nice to have.’”

Simpro competes with Zinier, which last January raised $90 million to automate field service management via its AI platform. The company has another rival in Workiz, a field service management and communication startup, as well as augmented reality- and AI-powered work management platform TechSee.

According to Tracxn, of the over 3,400 companies developing “field force automation” solutions (which include customer service tracking, order management, routing optimization, and work activity monitoring), more than 700 attracted a cumulative $5.8 billion from investors from 2018 to 2020.

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