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Posts from the ‘Machine learning’ Category

2021 State Of The Machine Learning Market: Enterprise Adoption Is Strong

data science, machine learning, enterprise software, AI, artificial Intelligence
  • 59% of all large enterprises are deploying data science (DS) and machine learning (ML) today.
  • Nearly 50% of all organizations have up to 25 or more ML models in use today.
  • 29% of enterprises are refreshing their data science and machine learning models every day.
  • The higher the data literacy an enterprise can achieve before launching Data Science & Machine Learning initiatives, the higher the probability of success.

These and many other insights defining the state of the data science and machine learning market in 2021 are from Dresner Advisory Services’ 2021 Data Science and Machine Learning Market Study. The 7th annual report is noteworthy for its depth of analysis and insight into how data science and machine learning adoption is growing stronger in enterprises. In addition, the study explains which factors drive adoption and determine the key success factors that matter the most when deploying data science and machine learning techniques. The methodology uses crowdsourcing techniques to recruit respondents from over 6,000 organizations and vendors’ customer communities. As a result, 52% of respondents are from North America and 34% from EMEA, with the balance from Asia-Pacific and Latin America. 

“The perceived importance of data science and machine learning correlates with organizational success with BI, with users that self-report as completely successful with BI almost twice as likely to rate data science as critical,” said Jim Ericson, vice president, and research director at Dresner Advisory. “The perceived level of data literacy also correlates directly and positively with the current or likely future use of data science and machine learning in 2021.” 

Key insights from the study include the following:

  • 59% of large enterprises are deploying data science and machine learning in production today.  Enterprises with 10K employees or more lead all others in adopting and using DS and ML techniques, most often in R&D and Business Intelligence Competency Center (BICC)-related work. Large-scale enterprises often rely on DS and ML to identify how internal processes and workflows can be streamlined and made more cost-efficient. For example, the CEO of a manufacturing company explained on a recent conference call that DS and ML pilots bring much-needed visibility and control across multiple plants and help troubleshoot inventory management and supply chain allocation problems.
machine learning
  • The importance of data science and ML to enterprises has doubled in eight years, jumping from 25% in 2014 to 70% in 2021. The Dresner study notes that a record level of enterprises sees data science and ML as critically important to their business in 2021. Furthermore, 90% of enterprises consider these technologies essential to their operations, rating them critically important or very important. Successful projects in Business Intelligence Competency Centers (BICC) and R&D helped data science and ML gain broad adoption across all organizations. Larger-scale enterprises with over 10K employees are successfully scaling data science and ML to improve visibility, control, and profitability in organizations today.
machine learning
  • Enterprises dominate the recruiting and retention of data science and machine learning talent. Large-scale enterprises with over 10K employees are the most likely to have BI experts and data scientists/statisticians on staff. In addition, large-scale enterprises lead hiring and retention in seven of the nine roles included in the survey. It’s understandable how the Business Intelligence (BI) expertise of professionals in these roles is helping remove the roadblocks to getting more business value from data science and machine learning. Enterprises are learning how to scale data science and ML models to take on problems that were too complex to solve with analytics or BI alone.    
machine learning
  • 80% of DS and ML respondents most want model lifecycle management, model performance monitoring, model version control, and model lineage and history at a minimum. Keeping track of the state of each model, including version control, is a challenge for nearly all organizations adopting ML today. Enterprises reach ML scale when they can manage ML models across their lifecycles using an automated system. The next four most popular features of model rollback, searchable model repository, collaborative, model co-creation tools, and model registration and certification are consistent with the feedback from Data Science teams on what they need most in an ML platform. 
machine learning
  • Financial Services prioritize model lifecycle management and model performance monitoring to achieve greater scale from the tens of thousands of models they’re using today. Consistent with other research that tracks ML adoption by industry, the Dresner study found that Financial Services leads all other industries in their need for the two most valuable features of ML platforms, model lifecycle management and model performance monitoring. Retail and Wholesale are reinventing their business models in real-time to become more virtual while also providing greater real-time visibility across supply chains. ML models in these two industries need automated model version control, model lineage and history, model rollback, collaborative, model co-creation tools, and model registration and certification. In addition, retailers and Wholesalers are doubling down on data science and machine learning to support new digital businesses, improve supply chain performance and increase productivity.
machine learning
  • Enterprises need support for their expanding range of regression models, text analytics functions, and ensemble learning. Over the last seven years, text analytics functions and sentiment analysis’ popularity has continually grown. Martech vendors and the marketing technologists driving the market are increasing sentiment analysis’ practicality and importance. Recommendation engines and geospatial analysis are also experiencing greater adoption due to martech changing the nature of customer- and market-driven analysis and predictive modeling. 
machine learning
  • R, TensorFlow, and PyTorch are considered the three most critical open-source statistical and machine learning frameworks in 2021. Nearly 70% of respondents consider R important to getting work done in data science and ML. The R language has established itself as an industry standard and is well-respected across DevOps, and IT teams in financial services, professional services, consulting, process, and discrete manufacturing. Tensorflow and Pytorch are considered important by the majority of organizations Dresner’s research team interviewed. They’re also among the most in-demand ML frameworks today, with new applicants having experience in all three being recruited actively today.   
machine learning
  • Data literacy predicts DS and ML program success rates. 64% of organizations say they have extremely high literacy rates, implying that DS and ML have reached mainstream adoption thanks partly to BI literacy rates in the past. Enterprises that prioritize data literacy by providing training, certification, and ongoing education increase success odds with ML. A bonus is that employees will have a chance to learn marketable skills they can use in their current and future positions. Investing in training to improve data literacy is a win/win.
machine learning
  • On-database analytics and in-memory analytics (both 91%), and multi-tenant cloud services (88%) are the three most popular technologies enterprises rely on for greater scalability. Dresner’s research team observes that the scalability of data science and machine learning often involves multiple, different requirements to address high data volumes, large numbers of users, data variety while supporting analytic throughput. Apache Spark support continues to grow in enterprises and is the fourth-most relied-on industry support for ML scalability.   
machine learning

2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth

2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth

Demand for TensorFlow expertise is one of the leading indicators of machine learning and AI adoption globally. Kaggle’s State of Data Science and Machine Learning 2020 Survey found that TensorFlow is the second most used machine learning framework today, with 50.5% of respondents currently using it.

TensorFlow expertise continues to be one of the most marketable machine learning and AI skills in 2021, making it a reliable leading indicator of technology adoption. In 2020, there were on average 4,134 LinkedIn open positions that required TensorFlow expertise soaring to 8,414 open LinkedIn positions this year in the U.S. alone. Globally, demand for TensorFlow expertise has doubled from 12,172 open positions in 2020 to 26,958 available jobs on LinkedIn today.  

Demand for machine learning expertise, as reflected in LinkedIn open positions, also shows strong growth. Increasing from 44,864 available jobs in 2020 to 78,372 in 2021 in the U.S. alone, organizations continue to staff up to support new initiatives quickly. Globally, LinkedIn’s open positions requiring machine-learning expertise grew from 98,371 in 2020 to 191,749 in 2021.

Market forecasts and projections also reflect strong growth for AI and machine learning spending globally for the long term. The following are key takeaways from the machine learning market forecasts from the last year include the following:

  • Forrester says the AI market will be defined and grow within four software segments, with AI maker platforms growing the fastest, reaching $13 billion by 2025, helping drive the market to $37 billion by 2025. Forrester is defining the four AI software segments as follows: AI maker platforms for general-purpose AI algorithms and data sets; AI facilitator platforms for specific AI functions like computer vision; AI-centric applications and middleware tools built around AI for specialized tasks like medical diagnosis; and AI-infused applications and middleware tools that differentiate through advanced use of AI in an existing app or tool category.  New AI-centric apps built on AI functions such as medical diagnosis and risk detection solutions will be the second-largest market, valued at nearly $10 billion by 2025. Source: Sizing The AI Software Market: Not As Big As Investors Expect But Still $37 Billion By 2025, December 10, 2020.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • IDC predicts worldwide revenues for the artificial intelligence (AI) market, including software, hardware, and services, will grow from $327.5 billion in 2021 to $554.3 billion in 2024, attaining a five-year compound annual growth rate (CAGR) of 17.5%. IDC further predicts that the AI Software Platforms market will be the strongest, with a five-year CAGR of 32.7%. The slowest will be AI System Infrastructure Software, with a five-year CAGR of 13.7% while accounting for roughly 36% of AI software revenues. IDC found that among the three technology categories, software represented 88% of the total AI market revenues in 2020. It’s the slowest growing category with a five-year CAGR of 17.3%. AI Applications took the largest share of revenue within the AI software category at 50% in 2020. Source: IDC Forecasts Improved Growth for Global AI Market in 2021, February 23, 2021
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • AI projects continued to accelerate in 2020 in the healthcare, bioscience, manufacturing, financial services, and supply chain sectors despite economic & social uncertainty. Two dominant themes emerge from the combination of 30 diverse AI technologies in this year’s Hype Cycle. The first theme is the democratization or broader adoption of AI across organizations. The greater the democratization of AI, the greater the importance of developers and DevOps to create enterprise-grade applications. The second theme is the industrialization of AI platforms. Reusability, scalability, safety, and responsible use of AI and AI governance are the catalysts contributing to the second theme.  The Gartner Hype Cycle for Artificial Intelligence, 2020, is shown below: Source: Software Strategies Blog, What’s New In Gartner’s Hype Cycle For AI, 2020, October 20, 2020.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Capgemini finds that Life Sciences, Retail, Consumer Products, and Automotive industries lead in the percentage of successfully deployed AI use cases today. Life Sciences leads all interviewed industries to AI maturity, with 27% of companies saying they have deployed use cases in production and at scale. Retail is also above the industry average of 13% of companies that have deployed AI in production at scale, with 21% of companies in the industry has adopted AI successfully.  17% of companies in the Consumer Products and Automotive industries now have AI in production, running at scale. Source: Capgemini, Making AI Work For You, (The AI-powered enterprise: Unlocking the potential of AI at scale) 2021
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Between 2018 and 2020, there’s been a 76% increase in sales professionals using AI-based apps and tools. Salesforce’s latest State of Sales survey found that 57% of high-performance sales organizations use AI today. High-performing sales organizations are 2.8x more likely to use AI than their peers. High-performing sales organizations rely on AI to gain new insights into customer needs, improve forecast accuracy, gain more significant visibility of rep activity, improve competitive analysis, and more. Source: Salesforce Research, 4th Edition, State of Sales, June 2020
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • While 24% of companies are currently using AI for recruitment, that number is expected to grow, with 56% reporting they plan to adopt AI next year. In addition, Sage’s recent survey of 500 senior HR and people leaders finds adoption of AI as an enabling technology for talent management increasing. AI is proving effective for evaluating job candidates for potential, improving virtual recruiting events, and reducing biased language in job descriptions. It’s also proving effective in helping to improve career planning and mobility. Josh Bersin, a noted HR industry analyst, educator, and technologist, recently published an interesting report on this area titled The Rise of the Talent Intelligence Platform. Leaders in the field of Talent Intelligence Platforms include Eightfold.ai. Grounded in Equal Opportunity Algorithms, the Eightfold® Talent Intelligence Platform uses deep-learning AI to help each person understand their career potential, and each enterprise understands the potential of their workforce.Sources: VentureBeat, 8 ways AI is transforming talent management in 2021, March 25, 2021, and Eightfold.ai.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • 84% of marketers are using AI-based apps and platforms today, up from 28% in 2018. Salesforce Research’s latest State of Marketing survey finds that high-performing marketers use an average of seven different applications or use cases. The familiarity high-performing marketers have with AI is a primary factor in 52% of them predicting they will increase their use of AI-based apps in the future. Source: Salesforce Research, 6th Edition, State of Marketing, June 2020
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Marketing and Sales lead revenue increases due to AI adoption, yet lag behind other departments on cost savings.  40% of the organizations McKinsey interviewed see between a 6 and 10% increase in revenue from adopting AI in their marketing and sales departments. Adopting Ai to reduce costs delivers the best manufacturing and supply chain management results based on the McKinsey survey results. Revenue increases and cost reductions based on AI adoption are shown in the graphic below. Source: McKinsey & Company, The state of AI in 2020, November 17, 2020
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service as the most dominant. Businesses with between $500M to $1B lead all other revenue categories in the number and depth of AI adoption cases. Just over 52% of small businesses with sales of $25M or less use AI for predictive analytics for customer insights. It’s interesting to note that small companies are the leaders in AI spending, at 38.1%, to improve marketing ROI by optimizing marketing content and timing. Source: The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte, and American Marketing Association. (71 pp., PDF, free, no opt-in).
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Three out of four companies are fast-tracking automation initiatives, including AI.  Bain & Company found that executives would like to use AI to reduce costs and acquire new customers, but they’re uncertain about the ROI and cannot find the talent or solutions they need. Bain research conducted in 2019 found that 90% of tech executives view AI and machine learning as priorities that they should be incorporating into their product lines and businesses. But nearly as many (87%) also said they were not satisfied with their Company’s current approach to AI. Source: Bain & Company, Will the Pandemic Accelerate Adoption of Artificial Intelligence? May 26, 2020
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Gartner’s Magic Quadrant for Data Science and Machine Learning Platforms predicts a continued glut of exciting innovations and visionary roadmaps from competing vendors. Competitors in the Data Science and Machine Learning (DSML) market focus on innovation and rapid product innovation over pure execution. Gartner said key areas of differentiation include UI, augmented DSML (AutoML), MLOps, performance and scalability, hybrid and multicloud support, XAI, and cutting-edge use cases and techniques (such as deep learning, large-scale IoT, and reinforcement learning). Please see my recent article on VentureBeat, Gartner’s 2021 Magic Quadrant cites ‘glut of innovation’ in data science and ML, March 14, 2021.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • 76% of enterprises are prioritizing AI & machine Learning In 2021 IT Budgets. Algorithmia’s survey finds that six in ten (64%) organizations say AI and ML initiatives’ priorities have increased relative to other IT priorities in the last twelve months. Algorithmia’s survey from last summer found that enterprises began doubling down on AI & ML spending last year. The pandemic created a new sense of urgency regarding getting AI and ML projects completed, a key point made by CIOs across the financial services and tech sectors last year during interviews for comparable research studies. Source: Algorithmia’s Third Annual Survey, 2021 Enterprise Trends in Machine Learning.
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Technavio predicts the Artificial Intelligence platforms market will grow to $17.29 billion by 2025, attaining a compound annual growth rate (CAGR) of nearly 35%. The research firm cites the increased levels of AI R&D investments globally combined with accelerating adoption for pilot and proof of concept testing across industries. Technavio predicts Alibaba Group Holding Ltd., Alphabet Inc., and Amazon.com Inc. will emerge as top artificial intelligence platforms vendors by 2025. Source:  Artificial Intelligence Platforms Market to grow by $ 17.29 Billion at 35% CAGR during 2021-2025. June 21, 2021
2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth
  • Tractica predicts the AI software market will reach $126 billion in worldwide revenue by 2025.  The research firm predicts AI will grow fastest in consumer (Internet services), automotive, financial services, telecommunications, and retail industries. As a result, annual global AI software revenue is forecast to grow from $10.1 billion in 2018 to $126.0 billion by 2025. Source: T&D World, AI Software Market to Reach $126.0 Billion in Annual Worldwide Revenue by 2025.

Sources of Market Data on Machine Learning:

The Top 20 Machine Learning Startups To Watch In 2021

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  • There are a record number of 9,977 machine learning startups and companies in Crunchbase today, an 8.2% increase over the 9,216 startups listed in 2020 and a 14.6% increase over the 8,705 listed in 2019.
  • Artificial Intelligence (A.I.) and machine learning (ML)-related companies received a record $27.6 billion in funding in 2020, according to Crunchbase. 
  • Of those A.I. and machine learning startups receiving funding since January 1, 2020, 62% are seed rounds, 31% early-stage venture rounds and 6.7% late-stage venture capital-funded rounds.
  • A.I. and machine learning startups’ median funding round was $4.4 million and the average was $29.8 million in 2020, according to Crunchbase.

Throughout 2020, venture capital firms continued expanding into new global markets, with London, New York, Tel Aviv, Toronto, Boston, Seattle and Singapore startups receiving increased funding. Out of the 79 most popular A.I. & ML startup locations, 15 are in the San Francisco Bay Area, making that region home to 19% of startups who received funding in the last year. Israel’s Tel Aviv region has 37 startups who received venture funding over the last year, including those launched in Herzliya, a region of the city known for its robust startup and entrepreneurial culture.  

The following graphic compares the top 10 most popular locations for A.I. & ML startups globally based on Crunchbase data as of today:

Top 20 Machine Learning Startups To Watch In 2021

Augury – Augury combines real-time monitoring data from production machinery with AI and machine learning algorithms to determine machine health, asset performance management (APM) and predictive maintenance (PdM) to provide manufacturing companies with new insights into their operations. The digital machine health technology that the company offers can listen to the machine, analyze the data and catch any malfunctions before they arise. This enables customers to adjust their maintenance and manufacturing processes based on actual machine conditions. The platform is in use with HVAC, industrial factories and commercial facilities.

Alation – Alation is credited with pioneering the data catalog market and is well-respected in the financial services community for its use of A.I. to interpret and present data for analysis. Alation has also set a quick pace to evolving its platform to include data search & discovery, data governance, data stewardship, analytics and digital transformation. With its Behavioral Analysis Engine, inbuilt collaboration capabilities and open interfaces, Alation combines machine learning with human insight to successfully tackle data and metadata management challenges. More than 200 enterprises are using Alation’s platform today, including AbbVie, American Family Insurance, Cisco, Exelon, Finnair, Munich Re, New Balance, Pfizer, Scandinavian Airlines and U.S. Foods. Headquartered in Silicon Valley, Alation is backed by leading venture capitalists including Costanoa, Data Collective, Icon, Sapphire and Salesforce Ventures.

Algorithmia – Algorithmia’s expertise is in machine learning operations (MLOps) and helping customers deliver ML models to production with enterprise-grade security and governance. Algorithmia automates ML deployment, provides tooling flexibility, enables collaboration between operations and development and leverages existing SDLC and CI/CD practices. Over 110,000 engineers and data scientists have used Algorithmia’s platform to date, including the United Nations, government intelligence agencies and Fortune 500 companies.

Avora – Avora is noteworthy for its augmented analytics platform, making in-depth data analysis intuitively as easy as performing web searches. The company’s unique technology hides complexity, empowering non-technical users to run and share their reports easily. By eliminating the limitations of existing analytics, reducing data preparation and discovery time by 50-80% and accelerating time to insight, Avora uses ML to streamline business decision-making. Headquartered in London with offices in New York and Romania, Avora helps accelerate decision making and productivity for customers across various industries and markets, including Retail, Financial Services, Advertising, Supply Chain and Media and Entertainment.

Boast.ai – Focused on helping companies in the U.S. and Canada recover their R&D costs from respective federal governments, Boast.ai enables engineers and accountants to gain tax credits using AI-based tools. Some of the tax programs Boast.ai works with include US R&D Tax Credits, Scientific Research and Experimental Development (SR&ED) and Interactive Digital Media Tax Credits (IDMTC). The startup has offices in San Francisco, Vancouver and Calgary.

ClosedLoop.ai – An Austin, Texas-based startup, ClosedLoop.ai has created one of the healthcare industry’s first data science platforms that streamline patient experiences while improving healthcare providers’ profitability.  Their machine learning automation platform and a catalog of pre-built predictive and prescriptive models can be customized and extended based on a healthcare provider’s unique population or client base needs. Examples of their technology applications include predicting admissions/readmissions, predicting total utilization & total risk, reducing out-of-network utilization, avoiding appointment no-shows, predicting chronic disease onset or progression and improving clinical documentation and reimbursement. The Harvard Business School, through its Kraft Precision Medicine Accelerator, recently named ClosedLoop.ai as one of the fastest accelerating companies in its Real World Data Analytics Landscapes report.

Databand – A Tel Aviv-based startup that provides a software platform for agile machine learning development, Databand was founded in 2018 by Evgeny Shulman, Joshua Benamram and Victor Shafran. Data engineering teams are responsible for managing a wide suite of powerful tools but lack the utilities they need to ensure their ops are running properly. Databand fills this gap with a solution that enables teams to gain a global view of their data flows, make sure pipelines complete successfully and monitor resource consumption and costs. Databand fits natively in the modern data stack, plugging seamlessly into tools like Apache Airflow, Spark, Kubernetes and various ML offerings from the major cloud providers.

DataVisor – DataVisor’s approach to using AI for increasing fraud detection accuracy on a platform level is noteworthy. Using proprietary unsupervised machine learning algorithms, DataVisor enables organizations to detect and act on fast-evolving fraud patterns and prevent future attacks before they happen. Combining advanced analytics and an intelligence network of more than 4.2B global user accounts, DataVisor protects against financial and reputational damage across various industries, including financial services, marketplaces, e-commerce and social platforms. They’re one of the more fascinating cybersecurity startups using AI today.

Exceed.ai – What makes Exceed.ai noteworthy is how their AI-powered sales assistant platform automatically communicates the lead’s context and enables sales and marketing teams to scale their lead engagement and qualification efforts accordingly. Exceed.ai follows up with every lead and qualifies them quickly through two-way, automated conversations with prospects using natural language over chat and email. Sales reps are freed from performing error-prone and repetitive tasks, allowing them to focus on revenue-generating activities such as phone calls and demos with potential customers.

Indico – Indico is a Boston-based startup specializing in solving the formidable challenge of how dependent businesses are on unstructured content yet lack the frameworks, systems and tools to manage it effectively. Indico provides an enterprise-ready A.I. platform that organizes unstructured content while streamlining and automating back-office tasks. Indico is noteworthy given its track record of helping organizations automate manual, labor-intensive, document-based workflows.  Its breakthrough in solving these challenges is an approach known as transfer learning, which allows users to train machine learning models with orders of magnitude fewer data than required by traditional rule-based techniques. Indico enables enterprises to deploy A.I. to unstructured content challenges more effectively while eliminating many common barriers to A.I. & ML adoption.

LeadGenius – LeadGenius is noteworthy for its use of AI to provide personalized and actionable B2B lead information that helps its clients attain their global revenue growth goals. LeadGenius’s worldwide team of researchers uses proprietary technologies, including AI and ML-based techniques, to deliver customized lead generation, lead enrichment and data hygiene services in the format, methods and frequency defined by the customer. Their mission is to enable B2B sales and marketing organizations to connect with their prospects via unique and personalized data sets.

Netra – Netra is a Boston-based startup that began as part of MIT CSAIL research and has multiple issued and pending patents on its technology today. Netra is noteworthy for how advanced its video imagery scanning and text metadata interpretation are, ensuring safety and contextual awareness. Netra’s patented A.I. technology analyzes videos in real-time for contextual references to unsafe content, including deepfakes and potential cybersecurity threats. 

Particle –  Particle is an end-to-end IoT platform that combines software including A.I., hardware and connectivity to provide a wide range of organizations, from startups to enterprises, with the framework they need to launch IoT systems and networks successfully.  Particle customers include Jacuzzi, Continental Tires, Watsco, Shifted Energy, Anderson EV, Opti and others. Particle is venture-backed and has offices in San Francisco, Shenzhen, Las Vegas, Minneapolis and Boston. Particle’s developer community includes over 200,000 developers and engineers in more than 170 countries today.

RideVision – RideVision was founded in 2018 by motorcycle enthusiasts Uri Lavi and Lior Cohen. The company is revolutionizing the motorcycle-safety industry by harnessing the strength of artificial intelligence and image-recognition technology, ultimately providing riders with a much broader awareness of their surroundings, preventing collisions and enabling bikers to ride with full confidence that they are safe. RideVision’s latest round was $7 million in November of last year, bringing their total funding to $10 million in addition to a partnership with Continental AG.

Savvie – Savvie is an Oslo-based startup specializing in translating large volumes of data into concrete actions that bakery and café owners can utilize to improve their bottom line every day.  In doing so, we help food businesses make the right decisions to optimize their operations and increase profitability while reducing waste at its source. What’s noteworthy about this startup is how adept they are at fine-tuning ML algorithms to provide their clients with customized recommendations and real-time insights about their food and catering businesses.  Their ML-driven insights are especially valuable given how bakery and café owners are pivoting their business models in response to the pandemic.

SECURITI.ai – One of the most innovative startups in cybersecurity, combining AI and ML to secure sensitive data in multi-cloud and mixed platform environments, SECURITI.ai is a machine learning company to watch in 2021, especially if you are interested in cybersecurity.  Their AI-powered platform and systems enable organizations to discover potential breach risk areas across multi-cloud, SaaS and on-premise environments, protect it and automate all private systems, networks and infrastructure functions.

SkyHive – SkyHive is an artificial intelligence-based SaaS platform that aims to reskill enterprise workforces and communities. It develops and commercializes a methodology, Quantum Labor Analysis, to deliver real-time, skill-level insights into internal workforces and external labor markets, identify future and emerging skills and facilitate individual-and company-level reskilling. SkyHive is industry-agnostic and supporting enterprise and government customers globally with a mission to reduce unemployment and underemployment. Sean Hinton founded the technology company in Vancouver, British Columbia, in 2017.

Stravito – Stravito is an A.I. startup that’s combining machine learning, Natural Language Processing (NLP) and Search to help organizations find and get more value out of the many market research reports, competitive, industry, market share, financial analysis and market projection analyses they have by making them searchable. Thor Olof Philogène and Sarah Lee founded the company in 2017, who identified an opportunity to help companies be more productive, getting greater value from their market research investments. Thor Olof Philogène and Andreas Lee were co-founders of NORM, a research agency where both worked for 15 years serving multinational brands, eventually selling the company to IPSOS. While at NORM, Anders and Andreas were receiving repeated calls from global clients that had bought research from them but could not find it internally and ended up calling them asking for a copy. Today the startup has Carlsberg, Comcast, Colruyt Group, Danone, Electrolux, Pepsi Lipton and others. Stravito has offices in Stockholm (H.Q.), Malmö and Amsterdam.

Verta.ai – Verta is a startup dedicated to solving the complex problems of managing machine learning model versions and providing a platform to launch models into production. Founded by Dr. Manasi Vartak, Ph.D., a graduate of MIT, who led a team of graduate and undergraduate students at MIT CSAIL to build ModelDB, Verta is based on their work define the first open-source system for managing machine learning models. Her dissertation, Infrastructure for model management and model diagnosis, proposes ModelDB, a system to track ML-based workflows’ provenance and performance. In August of this year, Verta received a $10 million Series A round led by Intel Capital and General Catalyst, who also led its $1.7 million seed round. For additional details on Verta.ai, please see How Startup Verta Helps Enterprises Get Machine Learning Right. The Verta MLOps platform launch webinar provides a comprehensive overview of the platform and how it’s been designed to streamline machine learning models into production:

V7 – V7 allows vision-based A.I. systems to learn continuously from training data with minimal human supervision. The London-based startup emerged out of stealth in August 2018 to reveal V7 Darwin, an image labeling platform to create training data for computer vision projects with little or no human involvement necessary. V7 specializes in healthcare, life sciences, manufacturing, autonomous driving, agri-tech, sporting clients like Merck, GE Healthcare and Toyota. V7 Darwin launched at CVPR 2019 in Long Beach, CA. Within its first year, it has semi-automatically annotated over 1,000 image and video segmentation datasets. V7 Neurons is a series of pre-trained image recognition applications for industry use. The following video explains how V7 Darwin works:

How To Digitally Transform Talent Management For The Better

How To Digitally Transform Talent Management For The Better

Bottom Line:  CHROs and the HR teams they lead need to commit to keep learning and adopting digital technologies that help improve how they hire, engage and retain talent if they’re going to stay competitive.

Driven by the urgency to keep connected with employees, customers and suppliers, McKinsey’s recent Covid-19 survey finds global organizations are now seven years ahead of schedule on digital transformation initiatives. HR’s role is proving indispensable in enabling the fast pace of digital adoption today. By providing Business Continuity Planning (BCP), HR’s contributions to digital transformation separate the organizations that thrive despite crises versus those left behind, according to McLean & Company’s 2021 HR Trends Report. The graphic below from the report shows how effective HR has been in supporting the rapid changes needed to keep employees communicating and engaged.

The McLean and Company Trends Report also shows that talent management’s major gaps need attention now before they grow wider. These areas include analyzing the employee skills gap (24%), developing employees on new competencies (24%), and training new employees in specific new skills (21%). Improving talent acquisition, retention, diversity and inclusion, and employee experiences by digitally transforming them with greater personalization at scale and visibility is key.  CHROs and the HR teams they lead need to close these gaps now.

How To Digitally Transform Talent Management For The Better

 

How To Get Started Digitally Transforming Talent Management

Start with the gaps in talent management you see in your organization. The largest gaps are often in the following four areas: recruiting and talent acquisition; retention of top talent and diverse talent; lack of visibility into employee capabilities; and workforce strategies not aligned to business strategies. Key challenges that need to drive digital transformation in these four areas include the following:

  • Legacy recruiting and Applicant Tracking Systems prioritize HR’s needs to capture thousands of resumes instead of delivering an excellent candidate experience. Attracting and recruiting the most qualified candidates in a virtual-first world is a daunting task. Organizations who are leaders in digital transformation quickly realized this and relied on automating the applicant experience so much it began to resemble the Amazon 1-Click Ordering experience. McKinsey’s recent Covid survey found that 75% of organizations digitally transforming their operations, including HR, were able to fill tech talent gaps during the crisis:

How To Digitally Transform Talent Management For The Better

Source: McKinsey & Company, 2020, How Covid-19 has pushed companies over the technology tipping point—and transformed business forever

 

  • Top talent retention is more of a problem than many organizations realize, with top performers receiving between five and ten recruiter calls a month or more. The average tenure of employees at companies has been decreasing for nearly two decades. And a primary driver is not for lack of opportunity, but because employees can’t find a career path internally as easily as they can find a growth opportunity at another company. It’s possible to retain the top talent by guiding employees to what’s next in their careers. Of the many approaches to providing employees a self-service option for personalized coaching guidance at scale, Eightfold’s Talent Intelligence Platform is delivering results at such notable companies as Air Asia, Micron, NetApp, and others. Eightfold found that 47% of top talent leave within two years, but most would happily stay if given the right opportunity. The following video explains how Eightfold helps its customers retain talent:

 

 

  • Employees often lack visibility into new internal opportunities, and both HR and business leaders lack visibility into employees’ unique capabilities. There’s often a 360-degree lack of visibility into new internal career positions from the employee’s side and a lack of awareness on the employer’s side of their employee’s innate capabilities. The lack of visibility from the employer side limits their ability to benchmark talent, create programmatic, scalable, and flexible career development opportunities and ultimately redeploy talent in an agile way that serves business strategies that are evolving rapidly in response to the impacts of the global pandemic.
  • Workforce strategies that don’t align and support business strategies waste opportunities to improve morale, productivity, and employees’ professional growth. While organizations have invested heavily in valuable infrastructure, including Learning Management Systems (LMS) and other employee experience and development tools, they often lack a unified platform to help deliver the right growth opportunities to the right person at the right time.

Achieving Greater Automation, Visibility And Personalization At Scale

Talent management is core to any digital business and the competitive outcomes each can produce today and in the future. To make greater contributions, Talent Management needs to deliver the following by relying on a unified platform:

  • Talent Management platforms need to combine ongoing business insights based on operations data, technology management data, and business transformation apps and tools to create new digitally-driven employee experiences quickly.
  • A key design goal of any Talent Management platform has to be delivering personalized candidate or prospect experiences at scale through every communications channel an organization relies on, both digital and human.
  • The best Talent Management platforms provide the apps, data, and contextual intelligence to drive task and mission ownership deep into an organization and reinforce accountability. What’s noteworthy about Eightfold’s Talent Intelligence Platform is that it has designed-in empathy and the ability to deliver quick, effective decisions that further reinforce team inclusion. Eightfold’s many customer wins in Talent Management illustrate how combining empathy, inclusion, and accountability in a platform’s design pays off.

As McLean & Company’s 2021 HR Trends Report shows, taking a band-aid approach to solving Talent Management’s many challenges is effective in the short-term. Turning Talent Management into a solid contributor to business strategies for the long-term needs to start at the platform level, however. Eightfold’s approach to combining their Talent Management, Talent Insights and Talent Acquisition modules, all supported by their Talent Intelligence Platform, enables their customers to define their digital transformation goals and strategies and get results.

Source: McKinsey & Company, 2020, How Covid-19 has pushed companies over the technology tipping point—and transformed business forever

Conclusion

The Talent Management goal many organizations aspire to today is to digitally transform candidate or prospect experiences so well that people have an immediate affinity for the company they apply to, and the self-service options are so intuitive they rival Amazon’s 1-Click Ordering Experience. Across any industry, digital transformation succeeds when customers’ expectations are exceeded so far that a new category gets created. Uber’s contextual intelligence, rating system, and ability to optimize ride requests is an example. UberEats provides the same real-time visibility into every step of each order, creating greater trust. Domino’s Pizza Tracker app keeps customers informed of every phase of their orders. What’s common across all these examples is personalization at scale, real-time automation across service providers, and real-time visibility. Those same core values need to be at the center of any Talent Management digital transformation effort today.

10 Ways Cybersecurity Can Protect COVID-19 Vaccine Supply Chains

10 Ways Covid-19 Vaccine Supply Chains Need To Be Protected By Cybersecurity

  • The Pharma industry has lost $14 billion through Intellectual Property (IP) cyber theft worldwide, according to the United Kingdom Office of Cyber Security and Information Assurance.
  • 53% of pharmaceutical IP thefts and related breaches are carried out by someone with insider access, also according to the United Kingdom Office of Cyber Security and Information Assurance.
  • The pharma industry’s average total cost of a data breach is $5.06 million, with one of the highest costs of remediating the breach at $10.81 million across all industries, according to a recent ProofPoint study.
  • Over 93% of healthcare organizations experienced a data breach in the past three years, and 57% have had more than five data breaches, according to the Cybersecurity Ventures 2020 Healthcare Cybersecurity Report.
  • Gartner predicts the privileged access management (PAM) market will grow at a compound annual growth rate (CAGR) of 10.7% from 2020 through 2024, reaching $2.9 billion by 2024.

Bottom Line: Having developed COVID-19 vaccines in a fraction of the time it takes to create new treatments, pharmaceutical companies need to protect the priceless IP, supporting data, and supply chains from cyberattacks.

Showing how powerful global collaboration between pharmaceutical industry leaders can be, the world’s leading vaccine producers delivered new vaccines in record time. The IP behind COVID-19 vaccines and their supporting supply chains need state-of-the-art protection comprised of cybersecurity technologies and systems, as the vaccines’ IP is an asset that cyber attackers have already tried to obtain.

Pharmaceutical’s Growing Number of Threat Surfaces Make Cybersecurity a Priority 

In the race to create a COVID-19vaccine by collaborating across the industry, pharmaceutical companies have exposed more threat surfaces than existed before the pandemic. In R&D, Clinical Trials, Manufacturing, and Distribution, there’s a proliferation of new threat surfaces cyber attackers are targeting today, as evidenced by threat analysis reports from the U.S. Homeland Security Department’s Cybersecurity & Infrastructure Security Agency (CISA).

The report provides specifics about how cyber attackers could impersonate an executive from a Chinese biomedical company known for having end-to-end cold chain expertise, which is essential for delivering vaccines reliably. The cyber attackers conducted spear-phishing attacks against global companies who support the global cold chain needed for distributing vaccines. There were credential harvesting attempts against global organizations in at least six countries known today to access vaccine transport and distribution sensitive information.

Launching a phishing campaign with the goal of harvesting details on key executives and access credentials across the cold chain is just the beginning. According to Lookout’s Pharmaceutical Industry Threat Report, some of the most significant threat surfaces are the most problematic today, including the following:

Research & Development & Clinical Trials

  • Collaborative research teams across pharmaceutical manufacturers globally
  • Scientists creating initial compounds and completing primary research to define a vaccine.
  • Integration of study sites at the test device and reporting system level

Manufacturing and Distribution

  • Plant workers’ systems, including tablets with build instructions on them
  • Physician & Pharmacist Networks
  • Distribution Channels and their supporting IT systems

Cyber attackers are taking a more synchronized, multifaceted approach to attacking Covid-19 supply chains, reiterated in CISA’s report. There’s evidence that state-sponsored cyber attackers attempt to move laterally through networks and remain there in stealth, allowing them to conduct cyber espionage and collect additional confidential information from victim environments for future operations. Cyber attackers are initially focused on phishing, followed by malware distribution, registration of new Covid-specific domain names, and always looking for unprotected threat surfaces.

10 Ways Cybersecurity Can Protect COVID-19 Vaccine Supply Chains

By combining multiple cybersecurity best practices and strategies, pharmaceutical companies stand a better chance of protecting their valuable IP and vaccines. Presented below are ten ways the pharmaceutical industry needs to protect the COVID-19 vaccine supply chain today:

  1. Prioritize Privileged Access Management (PAM) across the vaccine supply chain, ensuring least privilege access to sensitive data starting with IP. CISA’s note finds that there have been multiple attempts at capturing privileged credentials, which often have broad access privileges and are frequently left standing open. PAM is needed immediately to institute greater controls around these privileged accounts across the supply chain and only grant just enough just-in-time access to sensitive IP, shipping and logistics data, vaccination schedules, and more. Leaders include Centrify, which is noteworthy for cloud-based PAM implementations at the enterprise and supply chain levels. Additional vendors in this area include BeyondTrust, CyberArk, Ivanti, Thycotic, Ping Identity, and Senhasegura.
  2. Assess every supplier’s security readiness in vaccine supply chains, defining minimum levels of compliance to security standards that include a single, unified security model across all companies. In creating a secured vaccine supply chain, it’s imperative to have every supplier network member on the same security model. Taking this step ensures accountability, greater clarity of roles and responsibilities, and a common definition of privileged roles and access privileges.  Leaders in this area include BeyondTrust, Centrify, CyberArk, Ivanti, and Thycotic.
  3. Taking a Zero Trust-based approach to secure every endpoint across the vaccine manufacturer’s R&D, Clinical Trials, Manufacturing, and Distribution networks is necessary to shut down cyber attackers taking advantage of legacy security weaknesses approaches. The pharmaceutical companies and myriad logistics providers see a much faster than the expected proliferation of endpoints today. Trusted and untrusted domains from legacy server operating systems are a time sink when it comes to securing endpoints – and proving unreliable despite the best efforts that Security Operations teams are putting into them. Worst of all, they leave vaccine supply chains vulnerable because they often take an outdated “trust but verify” cybersecurity approach. Leaders include Illumio, Ivanti (MobileIron), Cisco, Appgate, Palo Alto Networks, and Akamai Technologies.
  4. Extend the Zero Trust framework across the entire supply chain by implementing microsegmentation and endpoint security requirements across all phases of the vaccine’s development cycles. This will ensure cyber attackers don’t have the opportunity to embed code to activate later. The goal is to push Zero Trust principles to all related processes integrating with the vaccines’ pipeline, including all dependencies across the entire development lifecycle.
  5. Incorporating Multi-Factor Authentication (MFA) across every system in the vaccine supply chain is a given. Usernames and passwords alone are not enough, and MFA is low-hanging fruit to authenticate authorized users. MFA is based on two or more factors that can authenticate who you are based on something you know (passwords, PINs, code works), something you have (a smartphone, tokens devices that produce pins or pre-defined pins), or something you are (biometrics, facial recognition, fingerprints, iris, and face scans). For example, Google provides MFA as part of their account management to every account holder and has a thorough security check-up, which is useful for seeing how many times a given password has been reused.
  6. Alleviate the conflicts of who will pay for increasing cybersecurity measures by making supplier-level security a separate line item in any CISOs and CIO’s budget. Today certain pharma supply chain CISOs are expected to ramp up cybersecurity programs with the same budget before Covid-19. While there are slight increases in cybersecurity budget levels, it’s often not enough to cover the higher costs of securing a broader scope of supply chain operations. CISOs need to have greater control over cybersecurity budgets to protect vaccine IP and distribution. Relying on traditional IT budgets controlled by CIOs isn’t working. There needs to be a new level of financial commitment to securing vaccine supply chains.
  7. Consider using an AIOps platform adept at unifying diverse IT environments into a single, cohesive AI-based intelligence system that can identify anomalous network behavior in real-time and take action to avert breaches. Based on conversations with CIOs across the financial services industry, it is clear they’re leaning in the direction of AIOps platforms that provide real-time integration to cloud platforms combined with greater control over IT infrastructure. LogicMonitor’s prioritizing IT integration as a core strength of their platform shows, as they have over 2,000 integrations available out of the box. Relying on Collectors’ agentless system, LogicMonitor retrieves metrics such as cloud provider health and billing information. This collector then pulls metrics from different devices using various methods, including SNMP, WMI, perf Mon JMX, APIs, and scripts.
  8. Unified Endpoint Security (UES) needs to become a standard across all vaccine supply chains now. Vendors who can rapidly process large amounts of data to detect previously unknown threats are needed today to stop cyberattacks from capturing IP, shipment data, and valuable logistics information. Absolute Software’s approach to leveraging its unique persistence, resilience, and intelligence capabilities is worth watching. Their approach delivers unified endpoint security by relying on their Endpoint Resilience platform, which includes a permanent digital tether to every enterprise’s endpoint. Absolute is enabling self-healing, greater visibility, and control by having an undeletable digital thread to every device. Based on conversations with their customers in Education and Healthcare, Absolute’s unique approach gives IT complete visibility into where every device is at all times and what each device configuration looks like in real-time.
  9. Pharma supply chains need to have a strategy for achieving more consistent Unified Endpoint Management (UEM) across every device and threat surface of the vaccine supply chain. UEM’s many benefits, including streamlining continuous OS updates across multiple mobile platforms, enabling device management regardless of the connection, and having an architecture capable of supporting a wide range of devices and operating systems. Another major benefit enterprises mention is automating Internet-based patching, policy, configuration management. Ivanti is the global market leader in UEM, and their recent acquisition of Cherwell expands the reach of their Neurons platform, providing service and asset management from IT to lines of business and from every endpoint to the IoT edge. Neurons are Ivanti’s AI-based hyper-automation platform that connects Unified Endpoint Management, Security, and Enterprise Service Management. Ivanti is prioritizing its customers’ needs to autonomously self-heal and self-secure devices and self-service end-users.
  10. Track-and-traceability is essential in any vaccine supply chain, making the idea of cyber-physical passports that include serialization for vaccine batches more realistic given how complex supply chains are today. Passports are an advanced labeling technology that provides the benefits of virtual tracking, verification of specific compounds, and yield rates of key materials. Serialization is a must-have for ensuring greater traceability across vaccine supply chains proving effective in stopping counterfeiting. Having digital passports traceable electronically can further help thwart cyber attackers.

Conclusion

By closing the cybersecurity gaps in vaccine supply chains, the world’s nations can find new, leaner, more efficient processes to distribute vaccines and protect their citizens. It’s evident from the results achieved so far in the U.S. alone that relying on traditional supply chains and means of distribution isn’t getting the job done fast enough, and cyber attackers are already looking to take advantage. By combining multiple cybersecurity tactics, techniques, and procedures, the vaccine supply chain stands to improve and be more secure from threats.

How FinancialForce Is Using AI To Fight Revenue Leakage

How FinancialForce Is Using AI To Fight Revenue Leakage

Bottom Line: Using AI to measure and predict revenue, costs, and margin across all Professional Services (PS) channels leads to greater accuracy in predicting payment risks, project overruns, and service forecasts, reducing revenue leakage in the process.

Professional Services’ Revenue Challenges Are Complex

Turning time into revenue and profits is one of the greatest challenges of running a Professional Services (PS) business. What makes it such a challenge is incomplete time tracking data and how quickly revenue leaks spring up, drain margins, and continue unnoticed for months. Examples of revenue leaks across a customers’ life cycles include the following:

  • Billing errors are caused by the booking and contract process not being in sync with each other leading to valuable time being wasted.
  • When products are bundled with services, there’s often confusion over recognizing each revenue source, when, and by which PS metric.
  • Inconsistent, inaccurate project cost estimates and actual activity lead to inaccurate forecasting, delaying the project close and the potential for bad debt write-offs and high Days Sales Outstanding (DSO).
  • Revenue leakage gains momentum and drains margins when the following happens:
    • Un-forecasted delays and timescale creep
    • Reduced utilization rates across each key resource required for the project to be completed
    • Invoice and billing errors that result in invoice disputes that turn into high DSOs & write-offs
    • Incorrect pricing versus the costs of sales & service often leads to customer churn.
    • Revenue leakage gains momentum as each of these factors further drains margin

Adding up all these examples and many more can easily add up to 20-30% of actual lost solution and services margin. In many ways, it’s like death by a thousand small cuts. The following graphic provides examples across the customer lifecycle:

How FinancialForce Is Using AI To Fight Revenue Leakage

Why Professional Services Are Especially Vulnerable To Revenue Leakage 

Selling projects and the promise of their outcomes in the future create a unique series of challenges for PS organizations when it comes to controlling revenue leakage. It often starts with inaccurately scoping a project too aggressively to win the deal, only to determine the complexity of tasks originally budgeted for will take 10 – 30% longer or more. Disconnects on project scope are unfortunately too common, turning small revenue leaks into major ones and the potential of long Days Sales Outstanding (DSO) on invoices. When revenue leaks get ingrained in a project’s structure, they continue to cascade into each subsequent phase, growing and costing more than expected.

The SPI 2021 Professional Services Maturity™ Benchmark Service published by Services Performance Insight, LLC in February of this year provides insights into the hidden costs and prevalence of revenue leakage. The following table illustrates how organizations with high levels of revenue leakage also perform badly against other key metrics, including client referencability. The more revenue leakage an organization experiences, the more billable utilization drops, on-time project deliveries become worse, and executive real-time visibility becomes poorer.

How FinancialForce Is Using AI To Fight Revenue Leakage

How FinancialForce Is Using AI To Fight Revenue Leakage

It’s noteworthy that FinancialForce is now on its 12th consecutive product release that includes Salesforce Einstein, and many customers, including Five9, are using AI to manage revenue leakage across their PS business. Throughout the pandemic, the FinancialForce DevOps, product management, and software quality teams have been a machine, creating rich new releases on schedule and with improved AI functionality based on Einstein. The 12th release includes prebuilt data models, lenses, dashboards, and reports.

Andy Campbell, Solution Evangelist at FinancialForce, says that “FinancialForce customers have access to best practices to minimize revenue leakage by scoping and selling the right product and services mix to allocating the optimal range and amount of services personnel and finally billing, collecting and recognizing the right amount of revenue for services provided.” Andy continued, saying that recent dashboards have been built for resource managers to automate demand and capacity planning and service revenue forecasting and assist financial analysts in managing deferred revenue and revenue leakage.

By successfully integrating Einstein into their ERP system for PS organizations, FinancialForce helps clients find new ways to reduce revenue leakage and preserve margin. Relying on AI-based insights for each phase of a PS engagement delivered a 20% increase in Customer Lifetime Value according to a FinancialForce customer. And by combining FinancialForce and Salesforce, customers see an increased bid:win ratio of 10% or more. The following graphic illustrates how combining the capabilities of Einstein’s AI platform with FinancialForce delivers results.

How FinancialForce Is Using AI To Fight Revenue Leakage

Conclusion

FinancialForce’s model building in Einstein is based on ten years of structured and unstructured data, aggregated and anonymized, then used for in-tuning AI models. FinancialForce says these models are used as starting points or templates for AI-based products and workflows, including predict to pay.  Salesforce has also done the same for its Sales Cloud Analytics and Service Cloud Analytics. In both cases, Salesforce and FinancialForce customers benefit from best practices and recommendations based on decades of data, which should be particularly interesting considering the “black swan” nature of 2020 data for most of their customers.

How A Startup Uses AI To Help You Find The Market Research You Need

How A Startup Uses AI To Help You Find The Market Research You Need

  • 95% of the content essential for decision making in an organization is unstructured, residing in PDFs and various file formats that defy easy indexing and quick access, according to MIT Media Labs.
  • 80% of typical organizations’ data is unstructured, slowing down work, often leading to less-than-optimal decision-making, according to an Accenture study published earlier this year.
  • Organizations use 35% of their structured data for insights and decision-making, but only 25% of their unstructured enterprise data, according to an Accenture study on how data is used for decision-making.
  • 60% to 80% of employees can’t find the information they are looking for even when there’s content management or knowledge management system in place, according to IBM’s knowledge management study.

Bottom Line: Stravito is an AI startup that’s combining machine learning, Natural Language Processing (NLP) and Search to help organizations find and get more value out of the many market research reports, competitive, industry, market share, financial analysis and market projection analyses they have by making them searchable.

When It Comes To Finding Market Research Data, Intranets Aren’t Getting It Done

Facing tight deadlines to get a marketing plan together for a new product, channel, or selling strategy, market research and product marketing teams will give up looking for a report they know they’ve bought and re-purchase it. The tighter the deadline and the more important the plan, the more this happens.

When a quick call to the Market Research Analyst who has access privileges to all the market research subscriptions doesn’t have the reports a team needs, they either move on without the data or repurchase the report. Having spent the first years of my career as a Market Research Analyst, I can attest to the accuracy of IBM’s finding that 30% of a typical knowledge workers’ day is spent searching for information and understanding its context and original methodology. All reports our organization had distribution rights to internally went on the Intranet site. There were hundreds of reports available online on an Intranet platform with mediocre search capabilities.

The company was founded by Thor Olof Philogène and Sarah Lee in 2017, who together identified an opportunity to help companies be more productive getting greater value from their market research investments. Thor Olof Philogène and Andreas Lee were co-founders of NORM, a research agency where both worked for 15 years serving multinational brands, eventually selling the company to IPSOS. While at NORM, Anders and Andreas were receiving repeated calls from global clients that had bought research from them but could not find it internally and ended up calling them asking for a copy. Today the startup has Carlsberg, Comcast, Colruyt Group, Danone, Electrolux, Pepsi Lipton and others. Stravito has offices in Stockholm (HQ), Malmö and Amsterdam.

Instead of settling for less-than-optimal market and industry data that partially deliver the insights needed for an exceptional product launch or sales campaign, marketing & senior management teams need to set their sights higher. It’s time to replace legacy Intranet sites and their limited search functions with AI-based search engines that auto-tag content and build taxonomies based on content attributes in real-time. Stravito combines AI, machine learning, NLP and Search on a single platform that can index every major file type an organization uses, creating a taxonomy that streamlines search queries.

Having AI as the foundation of the Stravito platform delivers the following benefits:

  • AI-powered fast search gives individuals the ability to find and share insights and information quicker than any legacy Intranet technology could. With everyone working from home and self-service being a goal every marketing, business planning and IT department is trying to achieve today, Stravito’s architecture is designed for simple queries and requests anyone can quickly learn to create.
  • Relying on AI and machine learning to alleviate the need to manually upload and tag hundreds of market research reports and analysis. Stravito’s approach to data categorization using AI also identifies and removes duplicate report copies and can be configured to filter out any reports past a specific date. Search perimeters, auto-tagging and in-PDF search options are all configurable. Stravito will rank PDFs by the percentage of relevant content they have for a specific search term, providing a bar graph designating which pages have the most relevant content.
  • Stravito’s design team has successfully combined AI, machine learning and advanced user interface design to produce an application comparable to Spotify, Google and Netflix. Developing and launching an enterprise-level search engine designed for usability first is noteworthy. Many enterprise applications still aren’t achieving this design goal despite being mentioned as a first priority by enterprise software vendors. As can be seen from their search results screen, Stravito’s approach is to combine information discovery and collaboration:

 

  • Stravito deserves credit for finding new ways to use AI and machine learning to accomplish drag-and-drop integration of any commonly used file format in an organization – and then have it assigned to a taxonomy in seconds. Stravito’s innovative use of AI, machine learning and auto-tagging provides its customers with a simple drag-and-drop interface that supports bulk uploads. The platform has API integration designed with any market research or advisory service with an API library compatible with their platform. Their customer base actively relies on Euromonitor and Mintel today, for example.

Conclusion

Stravito fills the gap legacy Intranet technologies and current generation collaboration platforms are not addressing. That’s the need to provide a more powerful search engine, one capable of continually adapting to new information and documents. Supervised machine learning has proven effective for taking on challenges related to creating and keeping taxonomies current. Stavito’s product strategy of providing personalized recommendations for the content of interest is a natural progression of their platform. For organizations overwhelmed with research data yet can’t seem to get the reports to decision-makers fast enough, the Stravito platform is worth checking out.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

  • AI and Machine Learning are on track to generate between $1.4 Trillion to $2.6 Trillion in value by solving Marketing and Sales problems over the next three years, according to the McKinsey Global Institute. 
  • Marketers’ use of AI soared between 2018 and 2020, jumping from 29% in 2018 to 84% in 2020, according to Salesforce Research’s most recent State of Marketing Study. 
  • AI, Machine Learning, marketing & advertising technologies, voice/chat/digital assistants, and mobile tech & apps are the five technologies that will have the greatest impact on the future of marketing, according to Drift’s 2020 Marketing Leadership Benchmark Report.

Chief Marketing Officers (CMOs) and the marketing teams they lead are expected to excel at creating customer trust, a brand that exudes empathy and data-driven strategies that deliver results. Personalizing channel experiences at scale works when CMOs strike the perfect balance between their jobs’ emotional and logical, data-driven parts. That’s what makes being a CMO today so challenging. They’ve got to have the compassion of a Captain Kirk and the cold, hard logic of a Dr. Spock and know when to use each skill set. CMOs and their teams struggle to keep the emotional and logical parts of their jobs in balance.

Asked how her team keeps them in balance, the CMO of an enterprise software company told me she always leads with empathy, safety and security for customers and results follow. “Throughout the pandemic, our message to our customers is that their health and safety come first and we’ll provide additional services at no charge if they need it.” True to her word, the company offered their latest cybersecurity release update to all customers free in 2020.  AI and machine learning tools help her and her team test, learn and excel iteratively to create an empathic brand that delivers results.

The following are ten ways AI and machine learning are improving marketing in 2021:

1.    70% of high-performance marketing teams claim they have a fully defined AI strategy versus 35% of their under-performing peer marketing team counterparts. CMOs who lead high-performance marketing teams place a high value on continually learning and embracing a growth mindset, as evidenced by 56% of them planning to use AI and machine learning over the next year. Choosing to put in the work needed to develop new AI and machine learning skills pays off with improved social marketing performance and greater precision with marketing analytics. Source: State of Marketing, Sixth Edition. Salesforce Research, 2020.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

2.    36% of marketers predict AI will have a significant impact on marketing performance this year. 32% of marketers and agency professionals were using AI to create ads, including digital banners, social media posts and digital out-of-home ads, according to a recent study by Advertiser Perceptions. Source: Which Emerging Tech Do Marketers Think Will Most Impact Strategy This Year?, Marketing Charts, January 5, 2021.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

3.    High-performing marketing teams are averaging seven different uses of AI and machine learning today and just over half (52%) plan on increasing their adoption this year. High-performing marketing teams and the CMOs lead them to invest in AI and machine learning to improve customer segmentation. They’re also focused on personalizing individual channel experiences. The following graphic underscores how quickly high-performing marketing teams learn then adopt advanced AI and machine learning techniques to their competitive advantage. Source: State of Marketing, Sixth Edition. Salesforce Research, 2020.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

4.    Marketers use AI-based demand sensing to better predict unique buying patterns across geographic regions and alleviate stock-outs and back-orders. Combining all available data sources, including customer sentiment analysis using supervised machine learning algorithms, it’s possible to improve demand sensing and demand forecast accuracy. ML algorithms can correlate location-specific sentiment for a given product or brand and a given product’s regional availability. Having this insight alone can save the retail industry up to $50B a year in obsoleted inventory.  Source: AI can help retailers understand the consumer, Phys.org. January 14, 2019.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

5.    Disney is applying AI modeling techniques, including machine learning algorithms, to fine-tune and optimize its media mix model. Disney’s approach to gaining new insights into its media mix model is to aggregate data from across the organization including partners, prepare the model data and then transform it for use in a model. Next, a variety of models are used to achieve budget and media mix optimization. Then compare scenarios. The result is a series of insights that are presented to senior management. The following dashboard shows the structure of how they analyze AI-based data internally. The data shown is, for example only; this does not reflect Disney’s actual operations.   Source: How Disney uses Tableau to visualize its media mix model (https://www.tableau.com/best-marketing-dashboards)

10 Ways AI And Machine Learning Are Improving Marketing In 2021

6.    41% of marketers say that AI and machine learning make their greatest contributions to accelerating revenue growth and improving performance. Marketers say that getting more actionable insights from marketing data (40%) and creating personalized consumer experiences at scale (38%) round out the top three uses today. The study also found that most marketers, 77%, have less than a quarter of all marketing tasks intelligently automated and 18% say they haven’t intelligently automated any tasks at all. Marketers need to look to AI and machine learning to automated remote, routine tasks to free up more time to create new campaigns. Source: Drift and Marketing Artificial Intelligence Institute, 2021 State of Marketing AI Report.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

7.    Starbucks set the ambitious goal of being the world’s most personalized brand by relying on predictive analytics and machine learning to create a real-time personalization experience. The global coffee chain faced several challenges starting with how difficult it was to target individual customers with their existing IT infrastructure. They were also heavily reliant on manual operations across their thousands of stores, which made personalization at scale a formidable challenge to overcome. Starbucks created a real-time personalization engine that integrated with customers’ account information, the mobile app, customer preferences, 3rd party data and contextual data. They achieved a 150% increase in user interaction using predictive analytics and AI, a 3X improvement in per-customer net incremental revenues. The following is a diagram of how DigitalBCG (Boston Consulting Group) was able to assist them. Source: Becoming The World’s Most Personalized Brand, DigitalBCG.  

10 Ways AI And Machine Learning Are Improving Marketing In 2021

8.    Getting personalization-at-scale right starts with a unified Customer Data Platform (CDP) that can use machine learning algorithms to discover new customer data patterns and “learn” over time.  For high-achieving marketing organizations, achieving personalization-at-scale is their highest and most urgent priority based on Salesforce Research’s most recent State of Marketing survey. And McKinsey predicts personalization-at-scale can create $1.7 trillion to $3 trillion in new value. For marketers to capture a part of this value, changes to the mar-tech stack (shown below) must be supported by clear accountability and ownership of channel and customer results. Combining a modified mar-tech stack with clear accountability delivers results.   Source: McKinsey & Company, A technology blueprint for personalization at scale. May 20, 2019. By Sean Flavin and Jason Heller.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

9.    Campaign management, mobile app technology and testing/optimization are the leading three plans for a B2C company’s personalization technologies. Just 19% of enterprises have adopted AI and machine learning for B2C personalization today. The Forrester Study commissioned by IBM also found that 55% of enterprises believe the technology limitations inhibit their ability to execute personalization strategies. Source: A Forrester Consulting Thought Leadership Paper, Commissioned by IBM, Personalization Demystified: Enchant Your Customers By Going From Good To Great, February 2020.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

10. Successful AI-driven personalization strategies deliver results beyond marketing, delivering strong results enterprise-wide, including lifting sales revenue, Net Promoter Scores and customer retention rates. When personalization-at-scale is done right, enterprises achieve a net 5.63% increase in sales revenue, 10.26% increase in order frequency, uplifts in average order value and an impressive 13.25% improvement in cross-sell/up-sell opportunities. The benefits transcend marketing alone and drive higher customer satisfaction metrics as well.   Source: A Forrester Consulting Thought Leadership Paper, Commissioned by IBM, Personalization Demystified: Enchant Your Customers By Going From Good To Great, February 2020.

10 Ways AI And Machine Learning Are Improving Marketing In 2021

CMOs and their teams rely on AI and machine learning to iteratively test and improve every aspect of their marketing campaigns and strategies. Striking the perfect balance between empathy and data-driven results takes a new level of data quality which isn’t possible to achieve using Microsoft Excel or personal productivity tools today. The most popular use of AI and machine learning in organizations is delivering personalization at scale across all digital channels. There’s also increasing adoption of predictive analytics based on machine learning to fine-tune propensity models to improve up-sell and cross-sell results. 

Bibliography

AI can help retailers understand the consumer, Phys.org. January 14, 2019

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76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgets

  • 43% of enterprises say their AI and Machine Learning (ML) initiatives matter “more than we thought,” with one in four saying AI and ML should have been their top priority sooner.
  • 50% of enterprises plan to spend more on AI and ML this year, with 20% saying they will be significantly increasing their budgets.
  • 56% of all enterprises rank governance, security and auditability issues as their highest-priority concerns today.
  • In just over a third of enterprises surveyed (38%), data scientists spend more than 50% of their time on model deployment.   

Enterprises accelerated their adoption of AI and machine learning in 2020, concentrating on those initiatives that deliver revenue growth and cost reduction. Consistent with many other surveys of enterprises’ AI and machine learning accelerating projects last year, Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning finds enterprises expanding into a wider range of applications starting with process automation and customer experience. Based on interviews with 403 business leaders and practitioners who have insights into their company’s machine learning efforts, the study represents a random sampling of industries across a spectrum of machine learning maturity levels. Algorithmia chose to limit the survey to only those from enterprises with $100M or more in revenue. Please see page 34 of the study for additional details regarding the methodology.   

Key insights from the research include the following:

  • 76% of enterprises prioritize AI and machine learning (ML) over other IT initiatives in 2021. Six in ten (64%) say AI and ML initiatives’ priorities have increased relative to other IT priorities in the last twelve months. Algorithmia’s survey from last summer found that enterprises began doubling down on AI & ML spending last year. The pandemic created a new sense of urgency regarding getting AI and ML projects completed, a key point made by CIOs across the financial services and tech sectors last year during interviews for comparable research studies.
76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgets
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning
  • 83% of enterprises have increased their budgets for AI and machine learning year-over-year from 2019 to 2020. 20% of enterprises increased their budget by over 50% between 2019 and 2020. According to MMC Ventures’ The State of AI Divergence Study, one in ten enterprises now uses ten or more AI applications with chatbots, process optimization and fraud analysis leading all categories. A recent Salesforce Research report, Enterprise Technology Trends, found that 83% of IT leaders say AI & ML is transforming customer engagement and 69% say it is transforming their business. The following compares year-over-year AI and ML budget changes between FY 2018 – 2019 and FY 2019 – 20.
76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgets
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning
76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgets
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning
  • Improving customer experiences to drive greater revenue growth and automating processes to reduce costs are the two most popular use cases or application areas for AI and ML in enterprises today. It’s noteworthy that seven of the top 20 use cases are customer-centric, nearly half of all use cases tracked in Algorithmia’s survey.  46% of enterprises are using AI & ML to combat fraud, which will most likely grow given the growth and severity of breaches, including the SolarWinds cyberattack. Capgemini’s recent study of AI adoption in cybersecurity found network, data and endpoint security are the three leading use cases of AI in cybersecurity today, with each predicted to get more funding in 2021, according to CISOs interviewed for the report.
76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgets
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning
  • AI and ML business cases that provide greater customer revenue growth, reduced costs and greater financial visibility have the highest priority of being funded inside any enterprise today. The combination of improving customer experiences, automating processes (to reduce costs) and generating financial insights (for greater financial visibility) is the ideal combination for getting a proof of concept started for an AI or ML project. The proliferation of AI and ML use cases shown in the graphic below is attributable to how each contributes to enterprises achieving a tangible, positive ROI by combining them to solve specific business problems.
76% Of Enterprises Prioritize AI & Machine Learning In 2021 IT Budgets
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning

83% Of Enterprises Transformed Their Cybersecurity In 2020

83% Of Enterprises Transformed Their Cybersecurity In 2020

  • 73% of enterprises (over 500 employees) accelerated their cloud migration plans to support the shift to remote working across their organizations due to the pandemic.
  • 81% of enterprises accelerated their IT modernization processes due to the pandemic.
  • 48% of all companies surveyed have accelerated their cloud migration plans, 49% have sped up their IT modernization plans because of Covid-19.
  • 32% of large-scale enterprises, over 500 employees, are implementing more automation using artificial intelligence-based tools this year.

These and many other insights are from a recent survey of IT leaders completed by CensusWide and sponsored by Centrify. The survey’s objectives on understanding how the dynamics of IT investments, operations and spending have shifted over the last six months. The study finds that the larger the enterprise, the more important it is to secure remote access to critical infrastructure to IT admin teams. Remote access and updating privacy policies and notices are two of the highest priorities for mid-size organizations to enterprises today. The methodology is based on interviews with 215 IT leaders located in the U.S.     

Key insights from the survey include the following:

  • The overwhelming majority of enterprises have transformed their cybersecurity approach over the last six months, with 83% of large-scale enterprises leading all organizations. It’s encouraging to see small and medium-sized businesses adjusting and improving their approach to cybersecurity. Reflecting how digitally-driven many small and medium businesses are, cybersecurity adjustments begin in organizations with 10 to 49 employees. 60% adjusted their cloud security postures as a result of distributed workforces. 

83% Of Enterprises Transformed Their Cybersecurity In 2020

  • 48% of all organizations had to accelerate cloud migration due to the pandemic, with larger enterprises leading the way. Enterprises with over 500 employees are the most likely to accelerate cloud migration plans due to the pandemic. 73.5% of enterprises with more than 500 employees accelerated cloud migration plans to support their employees’ remote working arrangements, leading all organization categories. This finding reflects how cloud-first the largest enterprises have become this year. It’s also consistent with many other surveys completed in 2020, reflecting how much the cloud has solidly won the enterprise. 
83% Of Enterprises Transformed Their Cybersecurity In 2020
  • 49% of all organizations and 81% of large-scale enterprises had to accelerate their IT modernization process due to the pandemic. For the largest enterprises, IT modernization equates to digitizing more processes using cloud-native services (59%), maintaining flexibility and security for a partially remote workforce (57%) and revisiting and adjusting their cybersecurity stacks (40%).
83% Of Enterprises Transformed Their Cybersecurity In 2020
  •  51% of enterprises with 500 employees or more are making remote, secure access their highest internal priority. In contrast, 27% of all organizations’ IT leaders say that providing secure, granular access to IT admin teams, outsourced IT and third-party vendors is a leading priority. The larger the enterprise, the more important remote access becomes. The survey also found organizations with 250 – 500 employees are most likely to purchase specific cybersecurity tools and applications to meet compliance requirements. 
83% Of Enterprises Transformed Their Cybersecurity In 2020

 

Conclusion & Wrap-Up  

IT leaders are quickly using the lessons learned from the pandemic as a crucible to strengthen cloud transformation and IT modernization strategies. One of every three IT leaders interviewed, 34%, say their budgets have increased during the pandemic. In large-scale enterprises with over 500 employees, 59% of IT leaders have seen their budgets increase.

All organizations are also keeping their IT staff in place. 63% saw little to no impact on their teams, indicating that the majority of organizations will have both the budget and resources to maintain or grow their cybersecurity programs. 25% of IT leaders indicated that their company plans to keep their entire workforce 100% remote.

It’s encouraging to see IT leaders getting the support they need to achieve their cloud transformation and IT modernization initiatives going into next year. With every size of organization spending on cybersecurity tools, protecting cloud infrastructures needs to be a priority. Controlling administrative access risk in the cloud and DevOps is an excellent place to start with a comprehensive, modern Privileged Access Management solution. Leaders in this field, including Centrify, whose cloud-native architecture and flexible deployment and management options, deliver deep expertise in securing cloud environments.

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