Archive | April, 2020

Six Areas Where AI Is Improving Customer Experiences

30 Apr

Bottom Line: This year’s hard reset is amplifying how vital customer relationships are and how much potential AI has to find new ways to improve them.

  • 30% of customers will leave a brand and never come back because of a bad experience.
  • 27% of companies say improving their customer intelligence and data efforts are their highest priority when it comes to customer experience (CX).
  • By 2023, 30% of customer service organizations will deliver proactive customer services by using AI-enabled process orchestration and continuous intelligence, according to Gartner.
  • $13.9B was invested in CX-focused AI and $42.7B in CX-focused Big Data and analytics in 2019, with both expected to grow to $90B in 2022, according to IDC.

The hard reset every company is going through today is making senior management teams re-evaluate every line item and expense, especially in marketing. Spending on Customer Experience is getting re-evaluated as are supporting AI, analytics, business intelligence (BI), and machine learning projects and spending. Marketers able to quantify their contributions to revenue gains are succeeding the most at defending their budgets.

Fundamentals of CX Economics

Knowing if and by how much CX initiatives and strategies are paying off has been elusive. Fortunately, there are a variety of benchmarks and supporting methodologies being developed that contextualize the contribution of CX. KPMG’s recent study, How Much Is Customer Experience Worth? provides guidance in the areas of CX and its supporting economics. The following table provides an overview of key financial measures’ interrelationships with CX. The table below summarizes their findings:

The KPMG study also found that failing to meet customer expectations is two times more destructive than exceeding them. That’s a powerful argument for having AI and machine learning ingrained into CX company-wide. The following graphic quantifies the economic value of improving CX:

Where AI Is Improving CX

For AI projects to make it through the budgeting crucible that the COVID-19 pandemic has created, they’re going to have to show a contribution to revenue, cost reduction, and improved customer experiences in a contactless world. Add in the need for any CX strategy to be on a resilient, proven platform and the future of marketing comes into focus. Examples of platforms and customer-centric digital transformation networks that can help re-center an organization on data- and AI-driven customer insights include BMC’s Autonomous Digital Enterprise (ADE) and others. The framework is differentiated from many others in how it is designed to capitalize on AI and Machine Learning’s core strengths to improve every aspect of the customer (CX) and  employee experience (EX). BMC believes that providing employees with the digital resources they need to excel at their jobs also delivers excellent customer experiences.

Having worked my way through college in customer service roles, I can attest to how valuable having the right digital resources are for serving customers What I like about their framework is how they’re trying to go beyond just satisfying customers, they’re wanting to delight them. BMC calls this delivering a transcendent customer experience. From my collegiate career doing customer service, I recall the e-mails delighted customers sent to my bosses that would be posted along a wall in our offices. In customer service and customer experience, you get what you give. Having customer service reps like my younger self on the front line able to get resources and support they need to deliver more authentic and responsive support is key. I see BMC’s ADE doing the same by ensuring a scalable CX strategy that retains its authenticity even as response times shrink and customer volume increases.

The following are six ways AI can improve customer experiences:

  • Improving contactless personalized customer care is considered one of the most valuable areas where AI is improving customer experiences. These “need to do” marketing areas have the highest complexity and highest benefit. Marketers haven’t been putting as much emphasis on the “must do” areas of high benefit and low complexity, according to Capgemini’s analysis. These application areas include Chatbots and virtual assistants, reducing revenue churn, facial recognition and product and services recommendations. Source:  Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. (PDF, 28 pp).
  • Anticipating and predicting how each customers’ preferences of where, when, and what they will buy will change and removing roadblocks well ahead of time for them. Reducing the friction customers face when they’re attempting to buy within a channel they’ve never purchased through before can’t be left to chance. Using augmented, predictive analytics to generate insights in real-time to customize the marketing mix for every individual Customer improves sales funnels, preserves margins, and can increase sales velocity.
  • Knowing which customer touchpoints are the most and least effective in improving CX and driving repurchase rates. Successfully using AI to improve CX needs to be based on data from all trackable channels that prospects and customers interact with. Digital touchpoints, including mobile app usage, social media, and website visits, all need to be aggregated into data sets ML algorithms to use to learn more about every Customer continually and anticipate which touchpoint is the most valuable to them and why. Knowing how touchpoints stack up from a customer’s point of view immediately says which channels are doing well and which need improvement.
  • Recruiting new customer segments by using CX improvements to gain them as prospects and then convert them to customers. AI and ML have been used for customer segmentation for years. Online retailers are using AI to identify which CX enhancements on their mobile apps, websites, and customer care systems are the most likely to attract new customers.
  • Retailers are combining personalization, AI-based pattern matching, and product-based recommendation engines in their mobile apps enabling shoppers to try on garments they’re interested in buying virtually. Machine learning excels at pattern recognition, and AI is well-suited for fine-tuning recommendation engines, which are together leading to a new generation of shopping apps where customers can virtually try on any garment. The app learns what shoppers most prefer and also evaluates image quality in real-time, and then recommends either purchase online or in a store. Source: Capgemini, Building The Retail Superstar: How unleashing AI across functions offers a multi-billion dollar opportunity.
  • Relying on AI to best understand customers and redefine IT and Operations Management infrastructure to support them is a true test of how customer-centric a business is. Digital transformation networks need to support every touchpoint of the customer experience. They must have AI and ML designed to anticipate customer needs and deliver the goods and services required at the right time, via the Customer’s preferred channel. BMC’s Autonomous Digital Enterprise Framework is a case in point. Source: Cognizant, The 2020 Customer Experience.

5G Experience in an Epidemic of Market Uncertainty

29 Apr

Telecom industry is facing an overarching demand for wireless bandwidth in presence of a burgeoning Internet of Things (IoT) industry that promises to accelerate the “experience economy” by bringing people, places and businesses together at scale.

recent report derived from Open Signal revealed how Airtel surged ahead of the market leader in subcontinent, Reliance Jio, in five out of six experience categories. This serves as a strong testimonial for laying an early foundation for 5G experience. With the onset of COVID-19 epidemic, telecom companies are further challenged to meet the surge and sudden shift in user experience for collaboration tools and high volume of video calls.

A next-generation ground truth mining solution providing KPIs for 5G experience planning

I led a small team to envision, design and implement a 5G experience planning project with applications of Deep Learning over modern data architectures like Snowflake or a cloud-native Data Lake powered by User Defined Functions (UDFs) that would allow for data curation and labeling. We aspired to build a Neural Net that could predict 5G hotspots in presence of fuzzy, uncertain and non-digitized information in a short period of time. The idea was to build a continuous intelligence rendering solution that would provide the necessary KPIs for movable placement and planning of expensive 5G infrastructure.

With the objective of achieving 5G experience consistency in shifting markets, the solution needed to have two key aspects. It should provide a way to save one or more location or consumer behavior-based calculations (e.g. distance to nearest infrastructure, income levels etc.) as ‘queryable’ tasks, aka UDFs. And it should be able to apply Deep Learning to suggest positional, economic and demographic attributes that would recommend next generation of locations hotspots; thereby providing the similarity in quality of 5G experience regardless of the subsequent metamorphic phase of market and consumer behavior.

Results were surprising and real.

This approach helped identify 6% additional hotspots where 5G experience gaps prevailed within just one county of the state of Texas that included 30,000 odd location points and more than 30 UDF-extracted or labeled attributes attached against each of them. The architecture, as described below, also provided room for consumption and labeling of new generation of information emerging from the rollout.

A modern data architecture is the hallmark for digital innovation and user experience planning where operational and business decision-making can be automated by artificial intelligence (AI) based extraction of inferences from raw information. This form of next generation ground truth mining in presence of unpredictable market conditions, will shape the future of “experience economy” in this new decade.

An executive primer on artificial general intelligence

29 Apr

While human-like artificial general intelligence may not be imminent, substantial advances may be possible in the coming years. Executives can prepare by recognizing the early signs of progress.

Download article


Headlines sounding the alarms that artificial intelligence (AI) will lead humanity to a dystopian
future seem to be everywhere. Prominent thought leaders, from Silicon Valley figures to legendary
scientists, have warned that should AI evolve into artificial general intelligence (AGI)—AI that is as
capable of learning intellectual tasks as humans are—civilization will be under serious threat.
Few seeing these warnings, stories, and images could be blamed for believing that the arrival of AGI
is imminent. Little surprise, then, that so many media stories and business presentations about machine
learning are accompanied by unsettling illustrations featuring humanoid robots.
Many of the most respected researchers and academics see things differently, however. They
argue that we are decades away from realizing AGI, and some even predict that we won’t see AGI in
this century. With so much uncertainty, why should executives care about AGI today? The answer
is that, while the timing of AGI is uncertain, the disruptive effects it could have on society cannot be
understated. Much has already been written about the likely impact of AI and the importance of carefully
managing the transition to a more automated world. The purpose of this article is to provide an AGI primer to help executives understand the path to machines achieving human-level intelligence, indicators by which to measure progress, and actions the reader can take to begin preparations today.

Source: 29 04 20

Multicloud deployments become go-to strategy as AWS, Microsoft Azure, Google Cloud grab wallet share

28 Apr

How multicloud is becoming a default strategy for many enterprises and the big three providers are grabbing more of your cloud budget.


Enterprises are all-in on multicloud as 93% of companies have a strategy to use multiple providers as Microsoft Azure, Amazon Web Services and Google Cloud all vie for customers and grab more wallet share, according to Flexera’s State of the Cloud report.

The findings highlight how multicloud is becoming the primary architecture choice as companies plan to mix clouds to avoid lock-in. Flexera had 750 respondents with 554 enterprises and 196 small and medium sized businesses. Of the respondents, 53% were advanced cloud users.

Flexera found that the top three public cloud providers remain AWS, Azure and Google, which has the fastest adoption in growth compared to a year ago. Flexera also found that Azure is narrowing the gap with AWS in the percentage of enterprises using it as well as the number of virtual machines.

Forty percent of AWS users spend at least $1.2 million annually with 36% spending that amount on Azure, according to Flexera.


To move their multicloud strategies along, Flexera found enterprises are betting heavily on containers. According to Flexera:

  • 65% of organizations are using Docker for containers with 58% using Kubernetes.
  • Container-as-a-service offerings from AWS, Azure and Google are all seeing strong growth.
  • Enterprises say they are lacking in resources and expertise to deal with container challenges.
  • And 33% of organizations use multicloud management tools.

COVID-19 may also accelerate shifts to the cloud. Flexera found at least half of companies are accelerating their cloud plans amid the COVID-19 pandemic and move to remote work. Indeed, 59% of enterprises say cloud usage will exceed prior plans due to the pandemic, according to Flexera.


Among other key findings:

  • Respondents said more than 50% of enterprise workloads and data are expected to be in the public cloud within 12 months.
  • SMBs are moving to the cloud at a faster rate with 70% of smaller enterprises saying data and workloads will be in the cloud in the next 12 months.
  • 87% have a hybrid cloud strategy.
  • 79% of respondents plan to optimize existing use of cloud for cost savings, with 61% focused on cloud migration.
  • IoT is the top growing cloud platform as a service offering followed by container as a service, machine learning and AI, data warehouse and serverless.
  • 73% of enterprises have a cloud team that is centralized and 51% use managed service providers to manage usage.
  • 83% of respondents said security is their top challenge with 82% citing costs.
  • 56% of organizations said understanding the cost implications of software licenses is a big cloud challenge. Respondents said 30% of cloud spend is wasted.
  • Understanding app dependencies was the top challenge for cloud migrations followed by technical feasibility and assessing on-prem vs. cloud costs.
  • Ansible and Terraform were the two most widely adopted configuration tools.
  • 63% of enterprises are adopting relational DBaaS.
  • VMware vSphere leads in private cloud adoption with Azure Stack and AWS Outpost showing strongest growth.
  • 22% of enterprises spend more than $12 million a year on public cloud.


Source: 28 04 20



The role of Wi-Fi in a 5G World

28 Apr

wi-fi 5G

Google trends is a fascinating tool that provides unparalleled insight into what people across the world are thinking and doing. A quick glance at the search trend for the term “5G” reveals a growing interest in this wireless connectivity technology  (in case you are curious, here is the comparison against the search trend for “WiFi” and here it is against the trend for “4G”). At CES 2020, Lenovo announced Yoga 5G, the world’s first 5G laptop. Although it has yet to ship, its technical specs list 5G and Bluetooth 5.0 as the only two supported connectivity technologies. Wi-Fi is conspicuously absent on this laptop, which has a starting price of $1499. Is this a precursor of what’s to come or does the Yoga 5G merely address a small market segment? Several other questions arise: Is Wi-Fi going to be replaced by 5G? Is 5G superior to Wi-Fi? What is Wi-Fi’s role in a 5G world? Before we answer these questions, let us start with a quick primer on 5G.

What is 5G?

Over the last 40 years, the world has witnessed a new generation of mobile communication technologies every decade. The first-generation technologies (1G), which emerged around 1980, were based on analog transmission and limited to voice services. The first major upgrade to mobile communication arrived in the early 1990s with the introduction of second generation (2G) technologies based on digital transmission. The target service was still voice, although the use of digital transmission allowed 2G systems to support limited data services – and almost accidentally created text messaging. The third generation (3G) was introduced in 2001 to facilitate greater voice and data capacity, thereby laying the foundations for mobile broadband. While the first two generations were designed to operate in paired spectrum based on Frequency Division Duplex (FDD), 3G introduced operation in unpaired spectrum based on Time Division Duplex (TDD), although this was rarely implemented. We are currently in the 4G era, which began in 2010. 4G technologies leverageOFDM and MIMO techniques to achieve higher efficiency and higher end-user data rates – enabling mobile broadband and harmonizing the fractured ecosystem.

5G is the fifth and the latest generation mobile communication technology that  supports three primary use cases: enhanced mobile broadband (higher speeds to current users), low latency with high reliability (to enable services such as safety systems and automatic control), and massive machine to machine communication (the ability to concurrently connect a lot more devices – IoT). 5G operates in many different frequency bands — from 600 MHz to 39 GHz — to service a wide variety of use cases. Signal propagation and bandwidth availability at mmWave (24 – 39 GHz) is very different from signals below 6 GHz. While mmWave can achieve 10+ Gbps data rates by leveraging as much as 800 MHz bandwidth, its range is limited because of the higher path loss at higher frequencies. On the other hand, sub 6 GHz has good range, but the data rate is less since the bandwidth is limited to 100 MHz.

Is Wi-Fi going to be replaced by 5G? 

We often debate whether 5G will replace Wi-Fi. Ultimately, we concluded that both Wi-Fi and cellular technologies will continue to be strong complements to each other for the foreseeable future.

  1. Total Ownership Cost: IP licensing costs associated with cellular technologies make cellular infrastructure and clients more expensive than their Wi-Fi counterparts. Unlike Wi-Fi, each new cellular generation is typically accompanied by new, and often expensive, spectrum. In addition, cellular services typically come with subscription fees paid to the network operator who owns the infrastructure and spectrum.
  2. Installed Base: Wi-Fi is ubiquitous. There are more than 13 billion Wi-Fi devices in active use worldwide and many of them have a long replacement cycle. Every new generation of Wi-Fi ensures that these devices can continue to connect to the new Wi-Fi infrastructure just as they did with the older ones, thereby protecting the existing investment in legacy devices. On the other hand, cellular chips don’t provide complete backwards compatibility and typically support only one or two generations.
  3. Ease of deployment: Wi-Fi uses free unlicensed spectrum and does not require any complex backend infrastructure such as a packet core. It can be deployed in minutes without requiring a skilled technician. Cloud management has further simplified Wi-Fi deployment, making it as simple as plug and play. Now that the Wi-Fi calling feature is natively supported on most smart phones, Wi-Fi is a good alternative to deploying dual systems for calling.
  4. In-building coverage: We spend most of our time indoors, yet outdoor cellular signals have trouble penetrating buildings. While there are several ways to bring cellular services into a building, this has not proven economical for wireless service providers. Thus, Wi-Fi remains the preferred choice and offers an additional benefit for the tenant, as the spectrum is unlicensed and can be controlled entirely.

In the next section, we will see that the latest generation of Wi-Fi performs on par with 5G for most use cases.

Is 5G superior to Wi-Fi?

As with cellular, Wi-Fi has gone through several generations of evolution over the last three decades. Client and infrastructure products supporting the sixth generation of Wi-Fi, commonly referred to as Wi-Fi 6, have been shipping since 2018. Notably, all models of Samsung Galaxy S10 and all models of iPhone 11 ship with Wi-Fi 6 connectivity.

Both Wi-Fi 6 and 5G use OFDM and OFDMA for PHY layer signaling and support up to 8 MIMO streams. While Wi-Fi 6 supports peak data rate of 9.6 Gbps, smartphone clients with two transmit and two receive chains can achieve over 1.7 Gbps TCP throughput in both uplink and downlink. This is comparable to the performance achievable with 5G. Wi-Fi 6 achieves a spectral efficiency of 62.5 bps/Hz, which exceeds the 5G requirement of 30 bps/Hz. It also includes several new features that enable AR, VR, and IoT applications through higher data rates, reduced latency, increased range, and extended battery life (similar to many of the features of 5G).

Wi-Fi 6 is optimized for extremely dense environments, with a single Wi-Fi 6 access point capable of serving a whopping 1024 clients concurrently. The trigger frame feature of Wi-Fi 6 enables scheduled access, similar to cellular, resulting in improved reliability of transmissions due to the elimination of collisions.

With the introduction of Passpoint, network discovery and selection have been fully automated rendering Wi-Fi roaming as seamless as cellular roaming. The latest security protocols, such as WPA3 and Enhanced Open supported on all Wi-Fi 6 devices have made Wi-Fi as secure as cellular. These protocols provide more secure and individualized encryption, making it difficult for hackers to snoop traffic even in an “open” network. Furthermore, features such as Rogue Detection supported on Wi-Fi access points protect users from “man-in-the-middle” attacks.

One of the areas where Wi-Fi falls short is mobility, as it is not specifically designed for high speed mobility. While cellular systems avoid interference by using different set of licensed frequencies from neighboring cells and provide guaranteed service quality, this is not the case, especially for unmanaged Wi-Fi networks.

The bottom line: Wi-Fi 6 is widely deployed today and measures up well against 5G.

What is Wi-Fi’s role in the world of 5G?

Given the favorable economics and high performance of Wi-Fi 6, Wi-Fi will remain a very attractive choice for indoor and enterprise applications. While cellular has its origins outdoors, we expect Wi-Fi and 5G to co-exist both indoors and outdoors.

Moreover, Wi-Fi continues to evolve faster than cellular with new Wi-Fi technology introduced once every 5 years – compared to the 10-year cadence of cellular technologies. Work has already started on the seventh generation of Wi-Fi, based on IEEE 802.11be. Wi-Fi 7 is targeting a peak throughput of at least 30 Gbps and strives to reduce the worst-case latency and jitter.

Recent efforts by Federal Communications Commission and OFCOM to open up in excess of 500 MHz of spectrum in the 6 GHz band for unlicensed use is expected to be another major game changer for Wi-Fi. This clean spectrum will double the number of lanes on the Wi-Fi superhighway and turbocharge the user base with added capacity for existing and new applications. This spectrum is expected to bring significant reductions in latency, since it will be occupied only by highly efficient Wi-Fi 6 devices (also known as Wi-Fi 6E devices), further enabling latency sensitive applications.

There has been cross fertilization of ideas between Wi-Fi and cellular, and this trend will continue as the two technologies move closer and closer together. For example, Wi-Fi introduced OFDM as part of its third-generation technology ratified in 1999, while cellular leveraged OFDM as part of its fourth-generation technology introduced in 2010. The latest sixth generation of Wi-Fi (2018) supports OFDMA, which cellular has supported since 4G (2010). Wi-Fi 6 introduced scheduled access, in addition to the traditional Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), bringing the Wi-Fi and cellular channel access methods closer. While Wi-Fi has always restricted itself to unlicensed bands, cellular dabbled with deployments in the unlicensed 5 GHz spectrum using LTE-U (although it wasn’t as successful).

In summary, Wi-Fi and 5G will move closer together and coexist as complementary technologies for the foreseeable future.

Source: 28 04 20

Everything Gets Smarter When 5G and AI Combine

28 Apr
Electronicdesign 26395 Link Ni Ai5g Promo

To be clear, 5G is a set of new technologies, while AI is not. The basic algorithms used by machine learning to create AI have been relatively unchanged for the last 30 years. The concept, called backpropagation, is fairly simple. Data sets and the expected outcomes associated with them are input into a processor, and it outputs a pattern. The more data sets and outputs that are used as inputs to the processor, the more accurate the resulting pattern. Machine learning thrives with massive amounts of data, and 5G will create massive amounts of data.


One of the defining characteristics of 5G that makes it dramatically different than previous standards is the fact that it has a spec for latency built in (the target is 1 ms). Figure 1 from GSMA Intelligence presents a visual diagram of various applications that rely on cellular networks. The applications in gray are the ones that require some combination of the massive bandwidth and throughput promised by 5G in addition to tight latency requirements.

Electronicdesign Com Sites Electronicdesign com Files Link Ni Ai5 G Fig1

1. The diagram shows the various applications that rely on cellular networks. (Source: GSMA Intelligence)

While some applications, like augmented reality, will require very deterministic and low latency, others such as streaming video and making a phone call do not. The ability to provide truly low latency and high reliability communications will require proper priority of network trafficking.

Network Slicing

The idea of network slicing, using a single shared physical network with multiple fully virtualized networks running on top of it, is a popular solution. A factory, for example, could pay for a slice with a guaranteed latency and percent reliability to connect smart machines and factory equipment. It could then have a separate slice for employee communications, such as cell phones and tablets (Fig. 2). Or a 5G connected car could have a slice for autonomous driving and other mission-critical functions and a separate slice for infotainment.

Electronicdesign Com Sites Electronicdesign com Files Link Ni Ai5 G Fig2

There’s no debate about the appeal of network slicing. It’s already beginning to be rolled out to the market on some scale. Significant limitations hamper a network providers’ ability to offer this type of service, though. Currently, these slices all must be manually configured. And, as networks grow more complex with the addition of 5G, so too does the amount of configuration needed to set up a slice. AI is perfectly suited for this task. Bob Cai, chief marketing officer for Huawei carrier business group, says that “intelligence” and AI will facilitate network optimization, so that backend traffic is routed based on device needs and engineered configuration settings.

AI will be present in other aspects of 5G as well. AI already plays a significant role in our daily interactions with our cell phones. Voice-activated assistants like Siri, Alexa, and Google Assistant already use AI to process our requests and return their best guess at an answer. But anyone who has used these tools knows they are far from perfect.

Bob Rogers, chief data scientist for analytics and AI in Intel’s Data Center Group, sees 5G as the way to give these AI assistants what they are lacking to be successful—contextual awareness. Similar to the other cases mentioned, by having access to more data and having that access at significantly faster speeds than are available with today’s LTE networks, devices will have a better ability to understand their surroundings.

AI also offers some appealing benefits when combined with the Internet of Things. As more devices become connected, more and more data about human patterns will be available for machine learning to take advantage of.

On a grand scale, this could completely revolutionize medicine. Medical studies strive to track as many patients as possible over a period of time to see how certain life choices, lifestyles, or locations may have an effect on their long-term health. Imagine a time in the future when a significant portion of the population wears smart health monitors. Statistics can be geotagged, timestamped, and sent to the cloud to be aggregated and processed. If medical records are also available for AI to process, correlations between different types of exercise and overall life expectancy, or more radically, specific locations and cancer could be determined.

On a simpler scale, AI and health related IoT devices could be used to monitor patients and make recommendations for treatment of disease much earlier than if a patient waits for symptoms to become overwhelming before visiting a doctor.

Side Effects?

Using AI to make cellular networks smarter and more efficient will almost certainly happen. Whether AI will be used for some of the other applications mentioned above remains to be seen. The possibilities of these new technologies is enticing and desirable. The cost, however, may not be one that people are willing to pay.

Recently, a large debate has raged on about users’ privacy and how data is controlled. There’s a certain amount of distrust in society for corporations collecting our personal data. Even if the end result is desirable, the possibility for corruption on various levels is high. Finding ways to better secure personal data or new business models that allow it to be collected without exploitation are also important challenges that must be solved to make 5G successful.

Source: 28 04 20

Top 25 Machine Learning Startups To Watch In 2020

27 Apr
  • There are 9,216 startups and companies listed in Crunchbase today who are relying on machine learning for their main and ancillary applications, products, and services, a 6% increase from 2019’s 8,705 startups & companies.
  • Artificial Intelligence-related companies raised $16.5B in 2019, driven by 695 deals according to PwC/CB Insights MoneyTree Report, Q1 2020.
  • Artificial intelligence deals decreased in Q1, 2020, down to 148 deals from 164 in Q4, 2019, according to PwC/CB Insights MoneyTree Report, Q1 2020.

One in ten enterprises now uses ten or more AI applications, with leading use cases being chatbots, process optimization, and fraud analysis, according to MMC Ventures. Open jobs requiring TensorFlow experience is a useful way to quantify how prevalent machine learning is becoming in business today. There are 3,602 open positions in the U.S. on LinkedIn that require TensorFlow expertise and 10,345 open positions worldwide as of today. Open jobs on LinkedIn requesting machine learning expertise in the U.S. further reflect its growing dominance in all businesses. There are 37,492 jobs in the U.S. today, according to LinkedIn, that list machine learning as a required skill, and 81,385 worldwide. Please see the latest roundup of machine learning forecasts and market estimates, 2020, for more market data on machine learnings’ exponential growth.

Top 25 Machine Learning Startups To Watch In 2020

  • AI.Reverie – AI.Reverie is a simulation platform that trains AI to understand the world. They offer a suite of synthetic data and vision APIs to help businesses across different industries train their machine learning algorithms and improve their AI accuracy and repeatability. Key industries AI.Reverie has solutions for including Agriculture, Industrial, including managing construction sites, Smart Cities, and Smart Homes.  AI.Reverie has raised a total of $5.6M in funding over three rounds. Their latest funding was raised on Apr 14, 2020.
  • Anodot –  Anodot applies AI to deliver autonomous analytics in real-time, across all data types, at enterprise scale. Unlike the manual limitations of traditional Business Intelligence, Anodot provides analysts greater control over their business with a self-service AI platform that runs continuously to eliminate blind spots, alert incidents and investigate root causes. Anodot has nearly 100 customers in digital transformation industries, including e-commerce, FinTech, AdTech, Telco, Gaming, including Microsoft, Lyft, Waze, and King. Founded in 2014, Anodot is headquartered in Silicon Valley and Israel, with sales offices worldwide. Anodot has raised a total of $62.5M in funding over five rounds. Their latest funding was raised on Apr 16, 2020, from a Series C round. The following screen from their app is an example of how Anodot provides real-time anomaly detection.
  • Arturo, Inc. –Arturo is a deep learning spin-out from American Family Insurance focused on delivering highly accurate measurement and predictive data for the Property & Casualty (P&C) Insurance, Reinsurance, REIT, and PERE markets. The company is headquartered in Chicago, IL, and has representation across North America to support both the Insurance and Re-Insurance Industry. Arturo has recently added to its locations a presence in Ottawa, Canada. Arturo, Inc. has raised a total of $8M in funding over two rounds. Their latest funding was raised on Apr 7, 2020, from a Series A round.
  • – Providing data scientists with a scalable platform that can automatically track their datasets, code changes, experimentation history, and production models is the’ssion. Their goal is to bring greater efficiency, transparency, and reproducibility into AI and ML development. is the first platform built for ML that enables engineers and data scientists to efficiently maintain their preferred workflow and tools, while easily tracking previous work and collaborating throughout the iterative process. also optimizes models with bayesian hyperparameter optimization – a type of algorithm – which saves time typically spent on manual tuning ML models. As a result, users have increased visibility of data science, ML results, and progress throughout an organization. Notable customers include AutoDesk, Boeing, Google, Uber, and the majority of Fortune 100 enterprises. has raised a total of $6.8M in funding over four rounds. Their latest funding was raised on Apr 22, 2020.
  • – Eightfold AI’s mission is there is a “Right career for everyone in the world,” which is why they immediately stepped up to help flatten the unemployment curve post the health crises created by COVID-19. Within a matter of weeks, Eightfold AIs’ engineers created the Eightfold Talent Exchange, working with the FMI – The Food Industry Association and supporting partner McKinsey. The Talent Exchange fills an urgent need in the market for a platform that matches people to the right jobs in companies that are hiring. Associated Wholesale Grocers, C&S Wholesale Grocers, CircleCI, Giovanni Foods, Ingles, Instacart, Lowe’s, Macy’s, Mondelez International Postmates, Stop & Shop, and United Airlines are all partnering with Eightfold and participating in the Talent Exchange. For additional details on the Talent Exchange, please see the post, How To Reduce The Unemployment Gap With AI. At the same time, the Talent Exchange was being developed and launched a new Virtual Event Recruiting solution. Eightfold has raised a total of $85M in funding and has opened additional offices in New York, London, Munich, and New Delhi. The company has customers in 4 continents, 25 countries, and 15+ languages because the AI models are language independent.
  • – Frame AI is an early warning and continuous monitoring system that operationalizes Voice-of-Customer insights across organizations in real-time to enable greater customer-centric decisions and direction. By consolidating and enriching data across help desks, call center, CRM, and other channels, Frame AI identifies emerging themes driving customer relationships and operational costs and makes them immediately actionable. CX teams use Frame AI to identify the “why” behind customer outcomes so that they can scale what works well, and limit the impact of what does not. was founded in 2016 and is backed by top venture capital firms, including FirstMark Capital, Greycroft Partners, and G20 Ventures. Notable customers include Intercom, HelpScout, Salesforce, Slack, and ZenDesk. Frame AI has raised a total of $10.3M in funding over three rounds. Their latest funding was raised on Apr 15, 2020, from a Series A round. is known for the clarity and intuitive design of their dashboards, and example of which is shown below with anonymized data:
  • Instreamatic – An AI-powered Voice Dialogue Marketing platform designed to power interactive, dialog-based advertisements on mobile platforms, Instreamatic is noteworthy for its use of natural language understanding (NLU) for training Voice AI algorithms. The company offers an all-in-one solution with streamlined integration options, providing audio publishers and advertisers with the tools to manage, measure, and monetize ad inventory with dialog ads. Instreamatic has raised a total of $2.2M in funding over five rounds. Their latest funding was raised on Apr 9, 2020, from a Seed round.
  • Jus Mundi – Jus Mundi is a public international law and investor-state arbitration search engine that combines an intuitive, user-friendly interface, advanced technologies including artificial intelligence and machine learning, with comprehensive content to increase the efficiency of international law research. International legal research can be particularly exhausting since information on cases may simply not be available to lawyers or because international law and investor-state jurisprudence are spread across various restrictive databases. Jus Mundi collects and indexes these documents so that its users don’t waste time trying to extract essential case information from legal materials. Its database contains over 4 000 international treaties and 12 000 decisions and awards. Its primary clients are international Anglo-American law firms. Jus Mundi has raised a total of €1M in funding over 1 round. This was a Seed round raised on Apr 1, 2020.
  • Kaizo – Kaizo builds a performance management platform for customer support teams. It uses gamification and AI to improve operational efficiency, elevate teams’ performance and retention with actionable OKRs. It’s available out of the box through the Zendesk Marketplace and is one of the top 10 rated customer support apps based on user feedback. Kaizo is a diverse team of entrepreneurs from all around the globe, working in Amsterdam and remotely from many countries, making a mark on team productivity and transforming performance management. Co-founded by Dominik Blattner and Christoph Auer-Welsbach, Kaizo aims to actively guide employees towards achieving their goals and making an impact in their companies. Kaizo has raised a total of $3M in funding over 1 round. This was a Seed round raised on Mar 26, 2020. The following is an example of how the Kaizo performance management platform gamifies customer support:
  • Luminovo – Luminovo is a deep learning company helping corporations develop tailored applications. The company was founded by a team of AI experts from Stanford University with experience applying AI in the wild, having worked at Google, Amazon, Intel, and McKinsey. The team relocated to Munich in 2017, making their AI expertise available to the German industry. By capitalizing on intelligent software and deep learning, Luminovo simplifies and accelerates electronics development and production processes. The startup’s mission is to bring innovations faster to everyone by reducing the time and resources needed to go from an idea to a market-ready electronic circuit board. Luminovo has raised a total of $2.5M in funding over 1 round. This was a Pre-Seed round raised on Apr 8, 2020.
  • MixMode – MixMode is an AI-focused cybersecurity startup company using patented AI originally developed for projects Defense Advanced Research Projects Agency (DARPA) and the U.S. Department of Defense (DoD). MixMode’s Network Security Monitoring Platform provides deep network visibility and predictive threat detection capabilities, enabling an organizations’ security team to perform real-time efficiently and retrospective threat detection and visualization. MixMode is used by breach response teams worldwide, security analysts and SOC teams can integrate MixMode into their playbooks, SIEMs, or utilize MixMode on a standalone basis to dramatically reduce investigation time, cost and expertise required to respond to persistent threats, malware, insider attacks, and nation state espionage efforts.  MixMode has raised a total of $17.3M in funding over five rounds. Their latest funding was raised on Apr 7, 2020, from a Series A round. The following is an example of a MixMode dashboard:
  • ModelOp – ModelOp enables large enterprises to address the scale and governance challenges necessary to gain the most value from enterprise AI and Machine Learning investments. The ModelOp Center platform automates the complete life cycle for models, regardless of where they are created or deployed. Fortune1000 companies in financial services, manufacturing, healthcare, and other industries rely on ModelOp to integrate their models into operations. ModelOp has offices in Chicago, IL, Salt Lake City, UT and San Jose, CA. ModelOp has raised a total of $6M in funding over three rounds. Their latest funding was raised on Mar 31, 2020, from a Series A round.
  • OctoML – OctoML’s mission is to change how developers optimize and deploy machine learning models for their AI needs. OctoML’s mission is to enable more developers to more easily and safely deploy ML models to more hardware. OctoML has raised a total of $18.9M in funding over 2 rounds. Their latest funding was raised on Apr 3, 2020 from a Series A round. OctoML builds on Apache TVM to offer automated ML operations with a unified software foundation, for any model on any hardware. What makes OctoML noteworthy is their unique approach of using ML to optimize ML, reducing the optimization and tuning time for ML operations. The company’s core offering is the Octomizer. This SaaS platform enables anyone to turn their ML models into highly optimized packages for deployment in the edge and in the cloud. A diagram of the Octomizer is shown below:
  • Olive – Olive develops artificial intelligence and RPA solutions that enable healthcare organizations to improve efficiency and patient care while reducing costly administrative errors. Its AI solution acts as the intelligent router between systems and data by automating repetitive, high volume tasks and workflows providing true interoperability for organizations. Olive are pioneers working to alleviate the most routine, mundane tasks healthcare professionals do on their jobs, so they are freed up to work on more complex, challenging, and rewarding treatment programs for their patients Olive has raised a total of $123.8M in funding over six rounds. Their latest funding was raised on Mar 31, 2020, from a Series E round.
  • Paige – Paige builds software to advance the diagnosis, treatment, and biomarker discovery for cancer. The Memorial Sloan Kettering spin-out aims to help pathologists and clinicians make faster, more informed diagnostic and treatment decisions and to bring new digital biomarkers to their practice. Paige’s proprietary Machine Learning solutions provide insights from decades of data from the world’s experts in cancer care and were recently published in Nature Medicine. The company’s first product for prostate cancer detection received Breakthrough Designation from the FDA as a novel therapy that stands to improve diagnostic accuracy while reducing costs to health systems. Paige has raised a total of $75M in funding over three rounds. Their latest funding was raised on Apr 23, 2020, from a Series B round.
  • PostEra – PostEra uses machine learning to close the design-make-test cycle of drug discovery and reduce our clients’ cycle times. They are currently leading an international team of scientists to find a COVID antiviral via on open science initiative. PostEra has raised a total of $2.5M in funding over two rounds. Their latest funding was raised on Mar 30, 2020, from a Seed round.
  • – helps e-commerce businesses increase conversion and increase order value with deep learning-based technology to analyze the individual consumer’s preferences and behaviors, predict future sales, and provide personalized recommendations for online and offline during the consumer’s shopping journey. Different vertical e-commerce markets have different shopping behaviors and preferences. focuses on the vertical e-commerce industry, the fashion industry, and dives more into the fashion, e.g., apparel, cosmetics, and accessory, etc., to build the dedicated deep learning models and algorithms for it and automatically arrange suitable models and algorithms for e-commerce. has raised two rounds. Their latest funding was raised on Mar 10, 2020, from a Convertible Note round.
  • Socure – Socure is a New York-based software company that provides consumers and businesses with an AI-based cybersecurity SaaS solution to fight against the risk of identity theft and related fraudulent activities. It enables the next-generation of multi-factor authentication by applying machine learning techniques with biometrics and data intelligence from email, phone, IP, social media, and the broader internet. The company capitalizes on alternative data that has proven to provide better fraud prediction capability. Founded in 2012, Socure is recommended for enterprises and financial institutions. Socure has raised a total of $61.9M in funding over seven rounds. Their latest funding was raised on Feb 28, 2019, from a Series C round.
  • – is a data marketplace allowing anyone to search, buy, sell, and download datasets aimed for machine learning and big data purposes. provides a way to connect people that have data with developers and industries that need the data to build their AI applications. By providing a way to find and buy already compiled data quickly, developers are saving valuable time and money using to find data they need and collaborate with other data scientists who have the same interests.
  • – is a startup based on Indium Software’s SaaS software application, which is designed to convert complex text data into accurate insights using AI and machine learning. is a hands-on, intuitive text analytics tool built on Python libraries. The SaaS-based text analytics suite provides insights to enhance customer experience by processing raw text data using NLP, AI, and DL algorithms. The startup has been successful in selling into banking, retail, e-commerce, manufacturing, education, hospitals (healthcare), and lifesciences.
  • ThinkDeep AI – ThinkDeep AI is a startup based in Bordeaux at the ENSC (National School for Cognitive Science) on the INP Engineering Center of Excellence. The company was founded by experts in Artificial Intelligence (AI), Image Processing and Computer Vision. They are the minds behind Deepflow, a low-code platform that abstracts away the complexity of designing and running data-science workflows. Their visual editor let domain experts create highly-specialized AI workflows by iterating from production-ready examples. A vast library of pre-existing workflows is available for free for anyone to copy and iterate. ThinkDeep AI has raised a total of €250K in funding over 1 round. This was a Pre-Seed round raised on Apr 5, 2020.
  • Tonkean –  Tonkean uses AI to autonomously coordinate, execute, and manage organizations’ business workflows across data and people closing information and process gaps. Tonkean’s approach to applying AI to business workflows is noteworthy for its intuitive design that provides for team members to contribute and provide input. The Tonkean Bot is configurable and acts as the orchestrator for an organizations’ entire workflow. The Bot performs the machine-focused tasks and coordinates with team members when their input and expertise is needed. Current customers include Salesforce, Microsoft, TripActions, Lyft, Hopper, and more. Tonkean has raised a total of $31.2M in funding over five rounds. Their latest funding was raised on Apr 8, 2020, from a Series A round.
  • vendi – vendi is an AI-assisted marketplace to buy and sell quality products, starting with phones. vendi was built to remove online scammers, improve peer to peer safety, and automate the online selling experience. Using their verification network, set of principles, and automatic listing powered by machine learning, they’ve increased product portfolio quality and created a safe space for people to buy and sell. vendi has raised a total of £600K in funding over 1 round. This was a Pre-Seed round raised on Apr 9, 2020.
  • Voci Technologies – Voci helps companies analyze their audio for in-depth voice of the customer insights and track call center performance metrics using the most accurate speech recognition, natural language, and machine learning technologies. Companies in highly regulated industries rely on Voci to safeguard their audio and monitor call center agents for compliance purposes. Voci also produces highly readable automatic speech-to-text voicemail output for telecommunication companies and their customers. Voci also is developing and selling cloud-based and on-premises real-time and archiving solutions for enterprises. Voci Technologies has raised a total of $18M in funding over nine rounds. Their latest funding was raised on Mar 12, 2018, from a Series B round.
  • – Zest AI makes the power of machine learning safe to use in credit underwriting. Lenders using Zest Automated Machine Learning make better decisions and better loans — increasing revenue, reducing risk, and automating compliance. Zest AI was founded in 2009 with the mission of making fair and transparent credit available to everyone and is now one of the fastest-growing fintech software companies. Zest AI has raised a total of $217M in funding over six rounds. Their latest funding was raised on Jul 18,’s Insights is the best blog I’ve seen covering the intersection of machine learning, credit underwriting, and reporting, and it’s great to see their AI Team conducting fascinating research. Examples of recent Insight posts include Young Americans Are Most Anxious About COVID’s Impact On Their Credit Score and Why Transparent AI Is More Important Now Than Ever.

Source: 27 04 20

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