According to Burning Glass Technologies, the two tech job skills paying the highest salary premiums today and in 2021 are IT Automation ($24,969) and AI & Machine Learning ($14,175).
The average salary premiums for the most in-demand tech skills range from $4,204 to nearly $25,000.
Startups valued at $1 billion or more are 33% more likely to prioritize one or several top ten tech job skills in their new hire plans versus their legacy Fortune 100-based competitors or colleagues.
These and many other fascinating insights are from Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech, Burning Glass Technologies’ latest research study published earlier this month. Their latest study provides pragmatic, useful insights for tech professionals interested in furthering their careers and earning potential. Burning Glass Technologies is a leading job market analytics provider that delivers job market analytics that empowers employers, workers and educators to make data-driven decisions.
Using AI To Find The Most Valuable Job Skills
Using artificial intelligence-based technologies they’ve developed, Burning Glass Technologies analyzed over 17,000 unique skills demanded across their database of over one billion historical job listings. The study aggregates then define disruptive skill clusters as those skill groups projected to grow the fastest, are most undersupplied and provide the highest value. For additional details regarding their methodology, please see page 8 of the report.
The research study is noteworthy because it explains how essential acquiring skills is to translating new technologies’ benefits into business value. They’ve also taken their analysis a step further, providing technical professionals with additional insights they need to plan their personal development and careers.
Key takeaways from their analysis include the following:
IT Automation expertise can earn technical professionals a $24,969 salary premium, the most lucrative of all tech job skills to have in 2021. Burning Glass Technologies defines IT Automation as the skills related to automating and orchestrating digital processes and workflows. Six of the ten job skills are marketable enough to drive technical professionals’ salaries above $10,000 a year. At an average salary uplift of $8,851, proactive security (cybersecurity) job skills’ market value seems low. Future surveys in 2021 will most likely reflect the impact of the SolarWinds breach on demand for this skill set. The following graphic compares the average salary premium by tech job skill area.
Software Dev. Methodologies (DevOps) expertise is the most marketable going into 2021, with 634,600 open positions available in North America based on Burning Glass Technologies’ analysis. Employers initiated 1,714,483 job postings requesting at least one disruptive skill area between December 2019 and November 2020. With each skill predicted to grow at least 17%, technical professionals have several lucrative options for their personal and professional development plans. The following graphic compares job openings by skill areas for the time frame of the study:
Quantum Computing, Connected Technologies, Fintech and AI & Machine Learning expertise are predicted to be the fastest-growing tech job skills in 2021 and beyond. Demand for technical professionals skilled in building and optimizing quantum computers and their applications will be in high demand for the next five years based on the study’s findings. Connected Technologies refers to skills related to the Internet of Things and connected physical tools and the telecommunications infrastructure needed to enable them. Fintech skills are related to technologies, including blockchain and others, that make financial transactions more efficient and secure. The following graphic compares the top ten tech job skills predicted to grow the fastest in 2021.
AI & Machine Learning, Cloud Technologies, Parallel Computing and Proactive Security (Cybersecurity) are the most distributed across industries, translating into more diverse job opportunities for technical professionals with these skills. Professional Services leads all industries in demand for nine of the ten tech job skills, except Parallel Computing, the most in-demand skill in Manufacturing. Factors contributing to Professional Services leading all industries in demand for technical job skills include the following factors. First, their business models need to continue pivoting fast to stabilize during the pandemic. Second, better risk and compliance controls of remote operations are urgently needed. Third, better visibility into services costs across all systems to ensure financial reporting accuracy is a must-have, according to the CFOs I spoke with regarding the survey results. The following graphic compares demand for tech skills by industry sector.
Demand for AI and Machine Learning skills is growing at a 71% compound annual growth rate through 2025, with 197,810 open positions today. Technical professionals with job skills in this area see salary premiums of $14,175. Top positions include Data Scientist, Software Developer, Network Engineer, Network Architect, Data Engineer and Senior Data Scientist.
Positions requiring IT Automation job skills are predicted to grow 59% over the next five years and have 282,380 positions open today. Besides being the most lucrative job skillset to have, IT Automation job skills lead to positions including Software Developer, DevOps Engineer, Senior Software Developer, Systems Engineer and Java Developer or Engineer.
47% of artificial intelligence (AI) investments were unchanged since the start of the pandemic and 30% of organizations plan to increase their AI investments, according to a recent Gartner poll.
30% of CEOs own AI initiatives in their organizations and regularly redefine resources, reporting structures and systems to ensure success.
AI projects continue to accelerate this year in healthcare, bioscience, manufacturing, financial services and supply chain sectors despite greater economic & social uncertainty.
Five new technology categories are included in this year’s Hype Cycle for AI, including small data, generative AI, composite AI, responsible AI and things as customers.
These and many other new insights are from the Gartner Hype Cycle for Artificial Intelligence, 2020, published on July 27th of this year and provided in the recent article, 2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020. 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:
Details Of What’s New In Gartner’s Hype Cycle for Artificial Intelligence, 2020
Chatbots are projected to see over a 100% increase in their adoption rates in the next two to five years and are the leading AI use cases in enterprises today. Gartner revised the bots’ penetration rate from a range of 5% to 20% last year to 20% to 50% this year. Gartner points to chatbot’s successful adoption as the face of AI today and the technology’s contributions to streamlining automated, touchless customer interactions aimed at keeping customers and employees safe. Bot vendors to watch include Amazon Web Services (AWS), Cognigy, Google, IBM, Microsoft, NTT DOCOMO, Oracle, Rasa and Rulai.
GPU Accelerators are the nearest-term technology to mainstream adoption and are predicted to deliver a high level of benefit according to Gartner’s’ Priority Matrix for AI, 2020. Gartner predicts GPU Accelerators will see a 100% improvement in adoption in two to five years, increasing from 5% to 20% adoption last year to 20% to 50% this year. Gartner advises its clients that GPU-accelerated Computing can deliver extreme performance for highly parallel compute-intensive workloads in HPC, DNN training and inferencing. GPU computing is also available as a cloud service. According to the Hype Cycle, it may be economical for applications where utilization is low, but the urgency of completion is high.
AI-based minimum viable products and accelerated AI development cycles are replacing pilot projects due to the pandemic across Gartner’s client base. Before the pandemic, pilot projects’ success or failure was, for the most part, dependent on if a project had an executive sponsor and how much influence they had. Gartner clients are wisely moving to minimum viable product and accelerating AI development to get results quickly in the pandemic. Gartner recommends projects involving Natural Language Processing (NLP), machine learning, chatbots and computer vision to be prioritized above other AI initiatives. They’re also recommending organizations look at insight engines’ potential to deliver value across a business.
Artificial General Intelligence (AGI) lacks commercial viability today and organizations need to focus instead on more narrowly focused AI use cases to get results for their business. Gartner warns there’s a lot of hype surrounding AGI and organizations would be best to ignore vendors’ claims of having commercial-grade products or platforms ready today with this technology. A better AI deployment strategy is to consider the full scope of technologies on the Hype Cycle and choose those delivering proven financial value to the organizations adopting them.
Small Data is now a category in the Hype Cycle for AI for the first time. Gartner defines this technology as a series of techniques that enable organizations to manage production models that are more resilient and adapt to major world events like the pandemic or future disruptions. These techniques are ideal for AI problems where there are no big datasets available.
Generative AI is the second new technology category added to this year’s Hype Cycle for the first time. It’s defined as various machine learning (ML) methods that learn a representation of artifacts from the data and generate brand-new, completely original, realistic artifacts that preserve a likeness to the training data, not repeat it.
Gartner sees potential for Composite AI helping its enterprise clients and has included it as the third new category in this year’s Hype Cycle. Composite AI refers to the combined application of different AI techniques to improve learning efficiency, increase the level of “common sense,” and ultimately to much more efficiently solve a wider range of business problems.
Concentrating on the ethical and social aspects of AI, Gartner recently defined the category Responsible AI as an umbrella term that’s included as the fourth category in the Hype Cycle for AI. Responsible AI is defined as a strategic term that encompasses the many aspects of making the right business and ethical choices when adopting AI that organizations often address independently. These include business and societal value, risk, trust, transparency, fairness, bias mitigation, explainability, accountability, safety, privacy and regulatory compliance.
The exponential gains in accuracy, price/performance, low power consumption and Internet of Things sensors that collect AI model data have to lead to a new category called Things as Customers, as the fifth new category this year. Gartner defines things as Customers as a smart device or machine or that obtains goods or services in exchange for payment. Examples include virtual personal assistants, smart appliances, connected cars and IoT-enabled factory equipment.
Thirteen technologies have either been removed, re-classified, or moved to other Hype Cycles compared to last year. Gartner has chosen to remove VPA-enabled wireless speakers from all Hype Cycles this year. AI developer toolkits are now part of the AI developer and teaching kits category. AI PaaS is now part of AI cloud services. Gartner chose to move AI-related C&SI services, AutoML, Explainable AI (also now part of the Responsible AI category in 2020), graph analytics and Reinforcement Learning to the Hype Cycle for Data Science and Machine Learning, 2020. Conversational User Interfaces, Speech Recognition and Virtual Assistants are now part of the Hype Cycle for Natural Language Technologies, 2020. Gartner has also chosen to move Quantum computing to the Hype Cycle for Compute Infrastructure, 2020. Robotic process automation software is now removed from the Hype Cycle for AI, as Gartner mentions the technology in several other Hype Cycles.
Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to discover the innate capabilities of people and matching them to new opportunities in their own companies.
Bottom Line: Eightfold.ai’s innovative approach of combining their own AI and virtual hackathons to create and launch new additions to their Project Marketplace rapidly is a model enterprises need to consider emulating.
Eightfold.ai was founded with the mission that there is a right career for everyone in the world. Since its founding in 2016, Eightfold.ai’s Talent Intelligence Platform continues to see rapid global growth, attracting customers across four continents and 25 countries, supporting 15 languages with users in 110 countries. Their Talent Intelligence Platform is built to assist enterprises with Talent Acquisition and Management holistically.
What’s noteworthy about Eightfold.ai’s approach is how they have successfully created a platform that aggregates all available data on people across an enterprise – from applicants to alumni – to create a comprehensive Talent Network. Instead of relying on data-driven signals of past accomplishments, Eightfold.ai is using AI to discover the innate capabilities of people and matching them to new opportunities in their own companies. Eightfold’s AI and machine learning algorithms are continuously learning from enterprise and individual performance to better predict role, performance and career options for employees based on capabilities.
How Eightfold Sets A Quick Pace Innovating Their Marketplace
Recently Eightfold.ai announced Project Marketplace, an AI-based solution for enterprises that align employees seeking new opportunities and companies’ need to reskill and upskill their employees with capabilities that line up well with new business imperatives. Eightfold wanted to provide employees with opportunities to gain new skills through experiential learning, network with their colleagues, join project teams and also attain the satisfaction of helping flatten the unemployment curve outside. Project Marketplace helps employers find hidden talent, improve retention strategies and gain new knowledge of who has specific capabilities and skills. The following is a screen from the Marketplace that provides employees the flexibility of browsing all projects their unique capabilities qualify them for:
Employees select a project of interest and are immediately shown how strong of a match they are with the open position. Eightfold provides insights into relevant skills that an employee already has, why they are a strong match and the rest of the project team members – often a carrot in itself. Keeping focused on expanding employee’s capabilities, Eightfold also provides guidance of which skills an employee will learn. The following is an example of what an open project positions looks like:
Employee applicants can also view all the projects they currently have open from the My Projects view shown below:
Project Marketplace is the win/win every employee has yearned for as they start to feel less challenged in their current position and start looking for a new one, often outside their companies. I recently spoke with Ashutosh Garg, CEO and Co-Founder and Kamal Ahluwalia, Eightfold’s President, to see how they successfully ran a virtual hackathon across three continents to keep the Marketplace platform fresh with new features and responsive to the market.
How to Run A Virtual Hackathon
Starting with the hackathon, Eightfold relied on its own Talent Intelligence Platform to define the teams across all three continents, based on their employees’ combined mix of capabilities. Ashutosh, Kamal and the senior management team defined three goals of the hackathon:
Solve problems customers are asking about with solutions that are not on the roadmap yet.
Accelerate time to value for customers with new approaches no one has thought of before.
Find new features and unique strengths that further strengthen the company’s mission of finding the right career for everyone in the world.
It’s fascinating to see how AI, cybersecurity and revenue management software companies continue to innovate at a fast pace delivering complex apps with everyone being remote. I asked Ashutosh how he and his management team approached the challenge of having a hackathon spanning three continents deliver results. Here’s what I learned from our discussion and these lessons are directly applicable to any virtual hackathon today:
Define the hackathon’s purpose clearly and link it to the company mission, explaining what’s at stake for customers, employees and the millions of people looking for work today – all served by the Talent Intelligence Platform broadening its base of features.
Realize that what you are building during the hackathon will help set some employees free from stagnating skills allowing them to be more employable with their new capabilities.
The hackathon is a chance to master new skills through experiential learning, further strengthening their capabilities as well. And often learning from some of the experts in the company by joining their teams.
Reward risk-taking and new innovative ideas that initially appear to be edge cases, but can potentially be game changers for customers.
I’ve been interviewing CEOs from startups to established enterprise software companies about how they kept innovation alive during the lockdown. CEOs have mentioned agile development, extensive use of Slack channels and daily virtual stand-ups. Ashutosh Garg is the only one to mention how putting intrinsic motivation into practice, along with these core techniques, binds hackathon teams together fast. Dan Pink’s classic TED Talk, The Puzzle of Motivation, explains intrinsic motivators briefly and it’s clear they have implications on a hackathon succeeding or not.
Measuring Results Of the Hackathon
Within a weekend, Project Marketplace revealed several new rock stars amongst the Eightfold hackathon teams. Instead of doing side projects for people who had time on their hands, this Hackathon was about making Eightfold’s everyday projects better and faster. Their best Engineers and Services team members took a step back, re-looked at the current approaches and competed with each other to find better and innovative ways. And they all voted for the most popular projects and solutions – ultimate reward in gaining the respect of your peers. As well as the most “prolific coder” for those who couldn’t resist working on multiple teams.
Remote work is creating daunting challenges for individuals at home as well as for companies. Business models need to change and innovation cannot take a back seat while most companies have employees working from home for the foreseeable future. Running a hackathon during a global lockdown and making it deliver valuable new insights and features that benefit customers now is achievable as Eightfold’s track record shows. Project marketplace may prove to be a useful ally for employees and companies looking to stay true to their mission and help each other grow – even in a pandemic. This will create better job security, a culture of continuous learning, loyalty and more jobs. AI will change how we look at our work – and this is a great example of inspiring innovation.
Bottom Line: Knowledge-sharing networks have been improving supply chain collaboration for decades; it’s time to enhance them with AI and extend them to resellers to revolutionize channel selling with more insights.
The greater the accuracy and speed of supply chain-based data integration and knowledge, the greater the accuracy of custom product orders. Add to that the complexity of selling CPQ and product configurations through channels, and the value of using AI to improve knowledge sharing networks becomes a compelling business case.
Why Channels Need AI-Based Knowledge Sharing Networks Now
Automotive, consumer electronics, high tech, and industrial products manufacturers are combining IoT sensors, microcontrollers, and modular designs to sell channel-configurable smart vehicles and products. AI-based knowledge-sharing networks are crucial to the success of their next-generation products. Likewise, to sell to any of these manufacturers, suppliers need to be pursuing the same strategy. AI-based services, including Amazon Alexa, Microsoft Cortana, and Google Voice and others, rely on knowledge-sharing networks to collaborate with automotive supply chains and strengthen OEM partnerships. The following graphic reflects how successful Amazon’s Alexa Automotive OEM sales team is at using knowledge-sharing networks to gain design wins across their industry.
The following are a few of the many reasons why creating and continually fine-tuning an AI-based knowledge-sharing network is an evolving strategy worth paying attention to:
Supply chains are the primary source of knowledge that must permeate an organization’s structure and channels for the company to stay synchronized to broader market demands. For CPQ channel selling strategies to thrive, they need real-time pricing, availability, available-to-promise, and capable-to-promise data to create accurate, competitive quotes that win deals. The better the supplier collaboration across supply chains and with channel partners, the higher the probability of selling more. A landmark study of the Toyota Production System by Professors Jeffrey H Dyer & Kentaro Nobeoka found that Toyota suppliers value shared data more than cash, making knowledge sharing systems invaluable to them (Dyer, Nobeoka, 2000).
Smart manufacturing metrics also need to be contributing real-time data to knowledge sharing systems channel partners use, relying on AI to create quotes for products that can be built the fastest and are the most attractive to each customer. Combining manufacturing’s real-time monitoring data stream of ongoing order progress and production availability with supply chain pricing, availability, and quality data all integrated to a cloud-based CPQ platform gives channel partners what they need to close deals now. AI-based knowledge-sharing networks will link supply chains, manufacturing plants, and channel partners to create smart factories that drive more sales. According to a recent Capgemini study, manufacturers are planning to launch 40% more smart factories in the next five years, increasing their annual investments by 1.7 times compared to the previous three years, according to their recent Smart factories @ scale Capgemini survey. The following graphic illustrates the percentage growth of smart factories across key geographic regions, a key prerequisite for enabling AI-based knowledge-sharing networks with real-time production data:
By closing the data gaps between suppliers, manufacturing, and channels, AI-based knowledge-sharing networks give resellers the information they need to sell with greater insight. Amazon’s Alexa OEM marketing teams succeeded in getting the majority of design-in wins with automotive manufacturers designing their next-generation of vehicles with advanced electronics and AI features. The following graphic from Dr. Dyer’s and Nobeoka’s study defines the foundations of a knowledge-sharing network. Applying AI to a mature knowledge-sharing network creates a strong network effect where every new member of the network adds greater value.
Setting the foundation for an effective knowledge sharing network needs to start with platforms that have AI and machine learning designed in with structure that can flex for unique channel needs. There are several platforms capable of supporting AI-based knowledge-sharing networks available, each with its strengths and approach to adapting to supply chain, manufacturing, and channel needs. One of the more interesting frameworks not only uses AI and machine learning across its technology pillars but also takes into consideration that a company’s operating model needs to adjust to leverage a connected economy to adapt to changing customer needs. BMC’s Autonomous Digital Enterprise (ADE) is differentiated from many others in how it is designed to capitalize on AI and Machine Learning’s core strengths to create innovation ecosystems in a knowledge-sharing network. Knowledge-sharing networks thrive on continuous learning. It’s good to see major providers using adaptive and machine learning to strengthen their platforms, with BMC’s Automated Mainframe Intelligence (AMI) emerging as a leader. Their approach to using adaptive learning to maintain data quality during system state changes and link exceptions with machine learning to deliver root cause analysis is prescient of where continuous learning needs to go. The following graphic explains the ADE’s structure.
Knowledge-sharing networks have proven very effective in improving supply chain collaboration, supplier quality, and removing barriers to better inventory management. The next step that’s needed is to extend knowledge-sharing networks to resellers and enable knowledge sharing applications that use AI to tailor product and service recommendations for every customer being quoted and sold to. Imagine resellers being able to create quotes based on the most buildable products that could be delivered in days to buying customers. That’s possible using a knowledge-sharing network. Amazon’s success with Alexa design wins shows how their use of knowledge-sharing systems helped to provide insights needed across automotive OEMs wanted to add voice-activated AI technology to their next-generation vehicles.
Jeffrey H Dyer, & Kentaro Nobeoka. (2000). Creating and managing a high-performance knowledge-sharing network: The Toyota case. Strategic Management Journal: Special Issue: Strategic Networks, 21(3), 345-367.
Myers, M. B., & Cheung, M. S. (2008). Sharing global supply chain knowledge. MIT Sloan Management Review, 49(4), 67.
Wang, C., & Hu, Q. (2020). Knowledge sharing in supply chain networks: Effects of collaborative innovation activities and capability on innovation performance. Technovation, 94, 102010.
One in 10 enterprises now use 10 or more AI applications; chatbots, process optimization, and fraud analysis lead a recent survey’s top use cases according to MMC Ventures.
83% of IT leaders say AI & ML is transforming customer engagement, and 69% say it is transforming their business according to Salesforce Research.
IDC predicts spending on AI systems will reach $97.9B in 2023.
AI pilots are progressing into production based on their combined contributions to improving customer experience, stabilizing and increasing revenues, and reducing costs. The most successful AI use cases contribute to all three areas and deliver measurable results. Of the many use cases where AI is delivering proven value in enterprises today, the ten areas discussed below are notable for the measurable results they are providing.
What each of these ten use cases has in common is the accuracy and efficiency they can analyze and recommend actions based on real-time monitoring of customer interactions, production, and service processes. Enterprises who get AI right the first time build the underlying data structures and frameworks to support the advanced analytics, machine learning, and AI techniques that show the best potential to deliver value. There are various frameworks available, with BMC’s Autonomous Digital Enterprise (ADE) encapsulating what enterprises need to scale out their AI pilots into production. What’s unique about BMC’s approach is its focus on delivering transcendent customer experiences by creating an ecosystem that uses technology to cater to every touchpoint on a customer’s journey, across any channel a customer chooses to interact with an enterprise on.
10 Areas Where AI Is Delivering Proven Value Today
Having progressed from pilot to production across many of the world’s leading enterprises, they’re great examples of where AI is delivering value today. The following are 10 areas where AI is delivering proven value in enterprises today
Customer feedback systems lead all implementations of AI-based self-service platforms. That’s consistent with the discussions I’ve had with manufacturing CEOs who are committed to Voice of the Customer (VoC) programs that also fuel their new product development plans. The best-run manufacturers are using AI to gain customer feedback better also to improve their configure-to-order product customization strategies as well. Mining contact center data while improving customer response times are working on AI platforms today. Source: Forrester study, AI-Infused Contact Centers Optimize Customer Experience Develop A Road Map Now For A Cognitive Contact Center.
McKinsey finds that AI is improving demand forecasting by reducing forecasting errors by 50% and reduce lost sales by 65% with better product availability. Supply chains are the lifeblood of any manufacturing business. McKinsey’s initial use case analysis is finding that AI can reduce costs related to transport and warehousing and supply chain administration by 5% to 10% and 25% to 40%, respectively. With AI, overall inventory reductions of 20% to 50% are possible. Source: Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? McKinsey & Company.
The majority of CEOs and Chief Human Resource Officers (CHROs) globally plan to use more AI within three years, with the U.S. leading all other nations at 73%. Over 63% of all CEOs and CHROs interviewed say that new technologies have a positive impact overall on their operations. CEOs and CHROs introducing AI into their enterprises are doing an effective job at change management, as the majority of employees, 54%, are less concerned about AI now that they see its benefits. C-level executives who are upskilling their employees by enabling them to have stronger digital dexterity skills stand a better chance of winning the war for talent. Source: Harris Interactive, in collaboration with Eightfold Talent Intelligence And Management Report 2019-2020 Report.
AI is the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems. AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. The most significant gains are being made where AI can contribute to solving complex constraints, cost, and delivery problems manufacturers are facing today. For example, AI is providing insights into where automation can deliver the most significant scale advantages. Source: McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus.
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 use cases. Just over 52% of small businesses with sales of $25M or less are using 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).
A semiconductor manufacturer is combining smart, connected machines with AI to improve yield rates by 30% or more, while also optimizing fab operations and streamlining the entire production process. They’ve also been able to reduce supply chain forecasting errors by 50% and lost sales by 65% by having more accurate product availability, both attributable to insights gained from AI. They’re also automating quality testing using machine learning, increasing defect detection rates up to 90%. These are the kind of measurable results manufacturers look for when deciding if a new technology is going to deliver results or not. These and many other findings from the semiconductor’s interviews with McKinsey are in the study, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? . The following graphic from the study illustrates the many ways AI and machine learning are improving semiconductor manufacturing.
AI is making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition, propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers’ preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO.
AI is reducing logistics costs by finding patterns in track-and-trace data captured using IoT-enabled sensors, contributing to $6M in annual savings. BCG recently looked at how a decentralized supply chain using track-and-trace applications could improve performance and reduce costs. They found that in a 30-node configuration, when blockchain is used to share data in real-time across a supplier network, combined with better analytics insight, cost savings of $6M a year is achievable. Source: Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra.
Detecting and acting on inconsistent supplier quality levels and deliveries using AI-based applications is reducing the cost of bad quality across electronic, high-tech, and discrete manufacturing. Based on conversations with North American-based mid-tier manufacturers, the second most significant growth barrier they’re facing today is suppliers’ lack of consistent quality and delivery performance. Using AI, manufacturers can discover quickly who their best and worst suppliers are, and which production centers are most accurate in catching errors. Manufacturers are using dashboards much like the one below for applying machine learning to supplier quality, delivery, and consistency challenges. Source: Microsoft, Supplier Quality Analysis sample for Power BI: Take a tour.
Optimizing Shop Floor Operations with Real-Time Monitoring and AI is in production at Hitachi today. Combining real-time monitoring and AI to optimize shop floor operations, providing insights into machine-level loads and production schedule performance, is now in production at Hitachi. Knowing in real-time how each machine’s load level impacts overall production schedule performance leads to better decisions managing each production run. Optimizing the best possible set of machines for a given production run is now possible using AI. Source: Factories of the Future: How Symbiotic Production Systems, Real-Time Production Monitoring, Edge Analytics, and AI Are Making Factories Intelligent and Agile, Youichi Nonaka, Senior Chief Researcher, Hitachi R&D Group and Sudhanshu Gaur Director, Global Center for Social Innovation Hitachi America R&D.
Papadopoulos, T., Gunasekaran, A., Dubey, R., & Fosso Wamba, S. (2017). Big data and analytics in operations and supply chain management: managerial aspects and practical challenges. Production Planning & Control, 28(11/12), 873-876.
It’s time for AI startups to step up and use their formidable technology expertise in AI to help get more Americans back to work now.
Bottom Line: A.I.’s ability to predict and recommend job matches will help get more Americans back to work, helping to reduce the 22 million unemployed today.
One in ten Americans is out of work today based latest U.S. Department of Labor data. They’re primarily from the travel and hospitality, food services, and retail trade and manufacturing industries, with many other affected sectors. McKinsey & Company’s recent article, A new AI-powered network, is helping workers displaced by the coronavirus crisis provides context around the scope of challenges involved in closing the unemployment gap. McKinsey, Eightfold A.I., and the FMI – The Food Industry Association combined efforts to create the Talent Exchange, powered by Eightfold.ai in a matter of weeks. McKinsey insights across a broad base of industries to help Eightfold and FMI create the Talent Exchange in record time. “In talking with clients across the U.S., it became very clear that there is a huge labor mismatch, and individuals are being affected very differently—from retailers furloughing tens of thousands of workers to other organizations needing to hire more than 100,000 workers quickly. We’re excited to help bring a scalable offering to the market,” said McKinsey partner Andrew Davis. McKinsey and FMI collaborating with Eightfold speak volumes to how Americans are coming together to combat the COVID-19 fallout as a team.
Eightfold’s Talent Exchange Is A Model For How To Use A.I. For Good
Eightfold’s Talent Exchange uses A.I. algorithms to match candidates with available roles, based on each individual’s skills and previous experience.
Current employers who have to furlough or lay off employees can invite employees to participate in the program. Eightfold also designed in a useful feature that enables employers to add lists of impacted employees and send them a link to register for the Exchange. Employers can view their entire impacted workforce in a single dashboard and can filter by role, department, or location to see details about the talent needs from hiring companies and how their impacted employees are getting placed in new roles. The following is the Talent Exchange dashboard for current employers showing progress in placing employees with furlough and outplacement partners, including the number of offers accepted by each:
Employees impacted by a furlough or lay-off can create and update profiles free on the Talent Exchange, defining their job preferences, skills, and experience. That’s invaluable data for hiring companies relying on the platform to make offers and fill positions quickly. How current employers handle furloughs and lay-offs today will be their identity for years to come, a point John Bersin made during the webinar saying “employers who thrive in the future are going to build long-term relationships with employees today.” Employees receive the following when their current employer adds their name to the Eightfold Talent Exchange. The fictional Company Travel Air is used for this example:
Hiring companies see candidate matches generated by the Exchange, so they can contact these prospects or immediately offer them new jobs. Eightfold’s A.I. engineering teams have automated and personalized this contact as well, expediting the process even further. Hiring companies can add onboarding instructions to allow new hires to start as soon as they are ready and have real-time views of their hiring dashboard shown below:
Combining A.I.’s innate strengths with H.R. and talent management professionals’ expertise and insights is closing the unemployment gap today. Employers furloughing or laying off employees need to look out for them and get their profile data on the Talent Exchange, helping them find new jobs with hiring companies. As was so well-said by Josh Bersin during the webinar this week, “smart employers should think of their hourly workers as talent, not fungible, replaceable bodies.” For hiring companies in a war for proven employees with talent today, that mindset is more important than ever.