AI and machine learning’s potential to drive greater visibility, control, and insight across shop floors while monitoring machines and processes in real-time continue to attract venture capital. $62 billion is now invested in 5,396 startups concentrating on the intersection of AI, machine learning, manufacturing, and Industry 4.0, according to Crunchbase.
PwC’s broader tech sector analysis shows a 30% year-over-year growth in funding rounds that reached $293.2 billion in 2021. Smart manufacturing startups are financed by seed rounds at 52%, followed by early-stage venture funding at 33%. The median last funding amount was $1.6 million, with the average being $9.93 million.
Abundant AI startup opportunities in smart manufacturing and industry 4.0
According to Gartner, “The underlying concept of Industry 4.0 is to connect embedded systems and smart production facilities to generate a digital convergence between industry, business, and internal functions and processes.” As a result, Industry 4.0 is predicted to grow from $84.59 billion in 2020 to $334.18 billion by 2028. AI and machine learning adoption in manufacturing are growing in five core fields: smart production, products and services improvements, business operations and management, supply chain, and business model decision-making. Deloitte’s survey on AI adoption in manufacturing found that 93% of companies believe AI will be a key technology to drive growth and innovation.
Machine intelligence (MI) is one of the primary catalysts driving increased venture capital investment in smart manufacturing. Startup CEOs and their customers want AI and machine learning models based on actual data, and machine intelligence is helping to make that happen. An article by McKinsey & Company provides valuable insights into market gaps for new ventures. McKinsey’s compelling data point is that those leading companies using MI achieve 3X to 4X the impact of their peers. However, 92% of leaders also have a process to track incomplete or inaccurate data – which is another market gap startups need to fill.
Based on the uplift MI creates for new smart manufacturing startup funding and the pervasive need manufacturers have to improve visibility & control across shop floors, startups have many potential opportunities. The following are five that AI and machine learning is helping to create:
AI-enabled Configure, Price, and Quote (CPQ) systems that can factor in supply chain volatility on product costs are needed. Several startups are already using AI and machine learning in CPQ workflows, and they compete with the largest enterprise software providers in the industry, including Salesforce, SAP, Microsoft, and others. However, no one has taken on the challenge of using AI to factor in how supply chain volatility changes standard and actual costs in real-time. For example, knowing the impact of pricing changes based on an allocation, how does that impact standard costs per unit on each order? Right now, an analyst needs to spend time doing that. AI and machine learning could take on that task so analysts could get to the larger, more complex, and costly supply chain problems impacting CPQ close rates and revenue.
Using AI-enabled real-time data capture techniques to identify anomalies in throughput as an indicator of machine health. The aggregated data manufacturing operations produced every day holds clues regarding each machine’s health on the shop floor. Automated data capture can identify scrap rates, yield rates and track actual costs. However, none of them can analyze the slight variations in process flow product outputs to warn of possible machine or supply chain issues. Each process manufacturing machine runs at its cadence or speed, and having an AI-based sensor system track and analyze why speeds are off could save thousands of dollars in maintenance costs and keep the line running. In addition, adding insight and intelligence to the machine’s real-time data feeds frees quality engineers to concentrate on more complex problems.
Industrial Internet of Things (IIoT) and edge computing data can be used for fine-tuning finite scheduling in real-time. Finite scheduling is part of the broader manufacturing systems organizations rely on to optimize shop floor schedules, machinery, and staff scheduling. It can be either manually intensive or automated to provide operators with valuable insights. A potential smart manufacturing opportunity is a finite scheduler that relies on AI and machine learning to keep schedules on track and make trade-offs to ensure resources are used efficiently. Finite schedulers also need greater accuracy in factoring in frequent changes to delivery dates. AI and machine learning could drive greater on-time delivery performance when integrated across all the shop floors a manufacturer relies on.
Automated visual inspections and quality analysis to improve yield rates and reduce scrap. Using visual sensors to capture data in real-time and then analyze them for anomalies is in its nascent stages of deployment and growth. However, this is an area where captured data sets can provide machine learning algorithms with enough accuracy to identify potential quality problems on products before they leave the factory. Convolutional neural networks are an effective machine learning technique for identifying patterns and anomalies in images. They’re perfect for the use case of streamlining visual inspection and in-line quality checks in discrete, batch, and process manufacturing.
Coordinated robotics (Cobots) to handle assemble-to-order product assembly. The latest cobots can be programmed to stay in sync with each other and perform pick, pack, ship, and place materials in warehouses. What’s needed are advanced cobots that can handle simple product assembly at a more competitive cost as manufacturers continue to face chronic labor shortages and often run a shift with less than half the teams they need.
Talent remains an area of need
Manufacturers’ CEOs and COOs say that recruiting and retaining enough talent to run all the production shifts they need is the most persistent issue. In addition, those manufacturers located in remote regions of the world are turning to robotics to fulfill orders, which opens up opportunities for integrating AI and machine learning to enable cobots to complete assemble-to-order tasks. The unknown impact of how fast supply chain conditions change needs work from startups, too, especially in tracking actual cost performance. These are just a few opportunities for startups looking to apply AI and machine learnings’ innate strengths to solve complex supply chain, manufacturing, quality management, and compliance challenges.
LinkedIn identified four key trends in their analysis, with flexible work is becoming table stakes for recruiting and retaining employees.
These and many other insights are from LinkedIn Top Companies 2022: The 50 best workplaces to grow your career in the U.S., published today. All 50 companies are currently hiring and have over 530,000 jobs open across the U.S, with over 70,000 being remote positions. The LinkedIn analysis of the best companies to grow your career spans 35 global markets, including the U.S., Canada, Mexico, Brazil, Argentina, Colombia, Chile, Ireland, France, Switzerland, Austria, Germany, Israel, Italy, Spain, the U.K., Sweden, Belgium, Denmark, the Netherlands, Portugal, India, Japan, Singapore, Philippines, Malaysia, Indonesia, Australia, New Zealand, UAE, Egypt, Saudi Arabia, South Africa, Nigeria, and Kenya.
LinkedIn’s Top Companies 2022 spotlights the organizations investing in employee success and career development. LinkedIn’s methodology and internal analysis ranked companies based on seven pillars that display career progression: ability to advance, skills growth, company stability, external opportunity, company affinity, gender diversity, and educational background.
The 19 Best Tech Companies To Grow Your Career In 2022
The following are profiles of the top 19 tech companies hiring in the U.S. today with links to available positions accessible via LinkedIn:
Global headcount: 1,600,000 (with 1,100,000 in the U.S.) | Top U.S. locations: Seattle, San Francisco Bay Area, New York City | Most notable skills: Warehouse Operations, Data Entry, AWS Lambda| Most common job titles: Software Engineer, Fulfillment Associate, Warehouse Associate | Largest job functions: Operations, Engineering, Program and Project Management | What you should know: Even as the country’s second-largest private employer, Amazon continues to compete in recruiting and retaining top talent amid a competitive labor market. The company recently announced that it’s doubling its maximum base salary for corporate and tech workers, and it raised average wages for warehouse workers late last year, increasing pay for more than half a million of its employees. But the e-commerce giant is going beyond compensation, too: investing $1.2 billion over the next three years to expand its education and skills training initiatives. Amazon now pays 100% of college tuition for frontline employees as part of its Career Choice program and covers high school diploma programs, GEDs, and English proficiency certifications.
Global headcount: 156,000 | Top U.S. locations: San Francisco Bay Area, New York City, Seattle | Most notable skills: Video Editing and Production, Google Cloud Platform (GCP), C++| Most common job titles: Software Engineer, Program Manager, Product Manager | Largest job function: Engineering, Information Technology, Program and Project Management |What you should know: It’s been a big year for Alphabet: The company onboarded nearly 6,500 employees last quarter and saw significant growth across Google’s Cloud service and YouTube (whose revenues are now growing at a faster rate than Netflix). For those interested in flexibility, the tech giant has a robust offering. In addition to adopting a hybrid work model, the company told LinkedIn that Alphabet offers four ‘work from anywhere’ weeks per year, sabbaticals for long-term employees, and ‘no meeting’ days. But Alphabet has also worked to maintain a collaborative culture and support career growth while working remotely. Employees can take advantage of resource groups like Women@Google and its Googler-to-Googler training, which lets its workers get first-hand knowledge across different fields from other employees.
Global headcount: 250,000 | Top U.S. locations: New York City; Raleigh-Durham, N.C.; San Francisco Bay Area | Most special skills: Kubernetes, Openshift, Hybrid Cloud| Most common job titles: Software Engineer, Project Manager, Data Scientist | Largest job functions: Engineering, Information Technology, Sales |What you should know: The perennial IT giant has re-upped its benefits offerings amid the Great Reshuffle, IBM told LinkedIn. The new initiatives are increased paid time off, more promotion and pay reviews, backup dependent care, virtual tutoring, and ‘compassionate leave’ for parents who experience stillbirth or miscarriage. In addition, as the company moves forward with a hybrid working model that allows employees to decide how often they want to be onsite, IBM has also transformed its onboarding process with “a focus on empathy and engagement” to help remote new hires feel more connected.
Global full-time headcount: 202,600 | Top U.S. locations: Atlanta, Dallas, New York City | Most notable skills:Design Thinking, Customer Experience, Futurism| Most common job titles: Retail Sales Consultant, Client Solutions Executive, Customer Service Representative | Largest job functions: Sales, Information Technology, Engineering |What you should know: Just three years after the acquisition of Time Warner, AT&T is changing course. The company agreed to a deal last year that will combine WarnerMedia’s assets with Discovery’s to create a new, separate global entertainment giant. Once the spinoff is completed (likely mid-2022), the telecom company will be focused on its core business — expanding access to broadband internet. For its employees, AT&T offers several advancement opportunities. For example, it invests $2 million annually in ‘AT&T University,’ an internal training program to help its workers upskill, and has partnered with groups like Udacity and Coursera to offer advanced online courses.
Global headcount: 154,000 | Top U.S. locations: San Francisco Bay Area; Austin, Texas; New York City | Most notable skills: Apple Software and Hardware, Technical Learning, iOS| Most common job titles: Software Engineer, Technical Specialist, Mac Genius | Largest job functions: Engineering, Information Technology, Sales |What you should know: Apple is increasing benefits and pay for retail workers to attract and retain employees at its 270 retail stores across the U.S. — including doubling sick days for both full-time and part-time employees and granting more vacation days. Its retail employees are also eligible for paid parental leave and can access discounted emergency childcare. In addition, after being one of the first companies to tell its corporate employees to work remotely in March 2020, Apple is now asking that they return to the office three days a week.
Global headcount: 189,000 (with 130,000 in the U.S.) | Top U.S. locations: Philadelphia, New York City, Los Angeles | Most notable skills: Media Production, Cable Modems, Broadcast Television | Most common job titles: Software Engineer, Communications Technician, Salesperson | Largest job functions: Engineering, Sales, Information Technology |What you should know: Comcast prioritizes career growth and development among its employees through various benefits — including mentorship programs, department rotations and tuition assistance for continuing education and skills development. As a part of its commitment to wellbeing, it also pays for 78% of its employees’ health care costs. Want an in? Comcast says the #1 skill it looks for in new hires is authenticity. “We believe that by being yourself, you are empowered to do your best work,” the company told LinkedIn.
Global headcount: 71,900 | Top U.S. locations: San Francisco Bay Area, Seattle, New York City | Most notable skills: PHP, Program Management, Social Media Marketing| Most common job titles: Software Engineer, Technical Recruiter, Data Scientist | Largest job functions: Engineering, Information Technology, Human Resources
Global headcount: 133,000 | Top U.S. locations: Austin, Texas; Boston; San Francisco Bay Area | Most notable skills: Software as a Service (SaaS), Kubernetes, Salesforce| Most common job titles: Account Executive, Software Engineer, Inside Sales Representative | Largest job functions: Sales, Information Technology, Engineering
Global headcount: 674,000 | Top U.S. locations: Washington D.C., New York City, Chicago | Most notable skills: Amazon Web Services (AWS), Management Consulting, Software Development Life Cycle (SDLC)| Most common job titles: Managing Director, Management Consultant, Business Integration Manager | Largest job functions: Information Technology, Business Development, Engineering
Global headcount: 119,400 (with 105,800 in the U.S.) | Top U.S. locations: New York City, Dallas, Washington D.C. | Most notable skills: Quotas, Wireless Technologies, Solution Selling| Most common job titles: Solutions Specialist, Customer Service Representative, Business Account Manager | Largest job functions: Sales, Engineering, Information Technology
Global headcount: 121,000 (with 55,700 in the U.S.) | Top U.S. locations: Portland, Ore.; Phoenix; San Francisco Bay Area | Most notable skills: JMP, System on a Chip (SoC), Statistical Process Control (SPC) | Most common job titles: Software Engineer, Process Engineer, System-on-Chip Design Engineer | Largest job functions: Engineering, Operations, Information Technology
Global headcount: 133,000 (46,600 in the U.S.) | Top U.S. locations: San Francisco Bay Area, Boston, Denver | Most notable skills: Oracle Cloud, NetSuite, OCI | Most common job titles: Software Engineer, Business Development Consultant, Application Sales Manager | Largest job functions: Engineering, Sales, Information Technology
Global headcount: 74,300 (41,000 in the U.S.) | Top U.S. locations: San Francisco Bay Area, Seattle, New York City | Most notable skills: Salesforce.com Administration, Salesforce Sales Cloud, Slack | Most common job titles: Account Executive, Software Engineer, Solutions Engineer | Largest job functions: Sales, Engineering, Information Technology
Global headcount: 81,800 (38,800 in the U.S.) | Top U.S. locations: San Francisco Bay Area; Raleigh-Durham, N.C.; Dallas | Most notable skills: Software as a Service (SaaS), Kubernetes, Network Engineering | Most common job titles: Software Engineer, Account Manager, Program Manager | Largest job functions: Engineering, Information Technology, Sales
Siemens is the parent company of Mendix and others.
Global headcount: 303,000 (with 40,000 in the U.S.) | Top U.S. locations: New York City, Philadelphia, Atlanta | Most notable skills: Building Automation, HVAC Controls, Electrical Troubleshooting | Most common job titles: Project Manager, Software Engineer, Senior Sales Executive | Largest job functions: Engineering, Sales, Operations
Global headcount: 10,400 (with 4,400 in the U.S.) | Top U.S. locations: San Francisco Bay Area, Boston, Washington D.C. | Most notable skills: Junos, Kubernetes, Border Gateway Protocol (BGP)| Most common job titles: Software Engineer, System Engineer, Technical Support Engineer | Largest job functions: Engineering, Sales, Information Technology
Viasat is the parent company of RigNet and others.
Global headcount: 5,800 | Top U.S. locations: San Diego, Denver, Atlanta | Most notable skills: RF Test, Amazon Web Services (AWS), Satellite Communications (SATCOM)| Most common job titles: Software Engineer, Program Manager, System Engineer | Largest job functions: Engineering, Information Technology, Operations
Global headcount: 5,000 (with 3,000 in the U.S.) | Top U.S. locations: Boston, Detroit, Los Angeles | Most notable skills: MATLAB, Simulink, Deep Learning| Most common job titles: Software Engineer, Application Support Engineer, Principal Software Engineer | Largest job function: Engineering, Information Technology, Sales
Flexible work is becoming table stakes for recruiting and retaining employees. With job seekers and employees in the driver’s seat and able to ask for the work-life balance they need, flexible work has become required to attract and retain top talent. Most companies on this year’s list offer some form of work-from-anywhere flexibility, with more than 70,000 remote jobs open now across the top 50 companies. Many companies also allow employees to set their schedules and work custom “on” hours through asynchronous work. Some, like Amazon (#1), Raytheon Technologies (#21), and General Motors (#44), are encouraging work-life balance with company-wide days off, while others offer unlimited paid vacation and sabbaticals. In addition, many companies are testing out new flexible offerings – employees at Cisco (#30) have adopted a four-day workweek through the company’s Interim Reduced Workweek program, IBM (#6) has set mandatory “off” hours, Cognizant (#33) offers the option to work a compressed week through its WorkFlex program, Realogy (#40) has a no meetings policy on “Focus Fridays,” Publicis Groupe (#41) allows employees the freedom to work from anywhere they like for up to six weeks per year and PwC (#32) allows employees to step away from work for up to six months while paid through its new Leave of Absence program.
Top companies offer stability in an unstable world. While many companies across the U.S. have faced challenges and disruptions over the last year, the Top Companies offer stability and upskilling opportunities that employees can count on – from tuition assistance and PTO for professional development to mentorship programs and job shadowing. Many organizations instituted new programs to retain employees. For example, Deloitte (#11) introduced a new Talent Experience Office focused on employee sentiments and preferences to help inform company choices, EY (#22) offers a Pathway to Purpose virtual program to help employees discover and live their personal purpose and vision, and Kimley-Horn (#31) offers job rotations, so employees learn from different roles and departments. Amazon (#1) is investing $1.2 billion to expand its education and skills training initiatives, Walmart (#5) gives field-based associates access to a no-cost college degree through its Live Better U program, and Verizon (#18) offers an apprenticeship program for those facing employment loss due to automation in technology to prepare them for the jobs of the future. PwC (#32) invested $3 billion in a “New World. New Skills” commitment to equip employees with digital training and awarded a “thank you” bonus of one-week extra pay. Bank of America (#8) provided an additional $1 billion in compensation stock awards to employees globally, and Northrop Grumman (#38) enhanced their annual bonus plan in addition to their ongoing stay interviews.
Mental health care is going mainstream across hiring and talent management. To keep employees healthy and happy at work, almost all of this year’s honorees now provide services that address mental health and well-being. Companies like Intel (#23), Salesforce (#28), and Juniper Networks (#46) provide dedicated mental health days, with many – including FedEx (#47) and Blackstone (#43) – offering company-paid mental health benefits. In addition, EY (#22) has expanded its no-cost counseling and mental health coaching sessions to 25 per year for employees and family. Deloitte (#11) provides a $1,000 well-being subsidy in addition to individualized psychological health resources. Unitedhealth Group (#13) provides complimentary access to wellness apps offering coaching, talk therapy, and more.
Authenticity, compassion, and curiosity are must-have skills. Most of the Top Companies do not require college degrees and instead look for soft skills that can translate across departments and roles. For example, the #1 skill Comcast (#10) seeks in new hires is authenticity, HCA Healthcare (#37) wants new hires to possess compassion, and Dell Technologies (#14) looks for people who thrive in an environment with a diversity of people and ideas. Accenture (#17), Oracle (#27), and Lockheed Martin (#29) value candidates with curiosity and eagerness to learn and grow. Alphabet (#2) looks for problem-solving skills and a growth mindset.
Bottom Line: Professional services (PS) organizations need to close the gaps in their CPQ selling strategies to win more deals, capture more revenue and protect margins from ongoing price pressure.
Why Services CPQ Is Too Slow Today
When PS organizations compete in sales cycles, the first competitor to have a complete quote with accurate pricing, schedules, and an engagement plan will often win. However, getting a complete quote out fast is a major challenge for most PS organizations today. Many PS organizations manually create their quotes by taking into account a broad base of factors that include the following: talent profiles of employees and the market value of their skills; utilization rates; direct and indirect engagement costs; typical gross margins by type of engagement; and, competitive pricing. The average PS organization takes six weeks to deliver a quote or proposal. John Ragsdale’s excellent recent article Automating Services Quote-to-Cash: Emergence of CPQ for Services provides useful insights into what needs to change for PS quoting and selling to increase its velocity.
Getting Services CPQ Right Is Hard
Gaps that drain revenue and margin grow wider when PS organizations attempt to use product-centric CPQ platforms to sell services. Too often, PS organizations attempt to wedge their quoting, pricing, and revenue management into a product-based CPQ system – and get mediocre results at best. Earlier in my career, I led a product management team that defined, created, and launched a quoting system for professional services inside a large IT organization. The most valuable lessons learned from that experience include the following:
PS bundles only work if they have simple, solid direct cost structures. Adding a synthetic SKU that represents a PS bundle only works for the most simple, automated PS engagements. Think of those PS engagements with long-standing direct cost structures that are simple, clear and easy to implement. Attempting to group PS bundles can easily lead to quoting mistakes that drain margin when a product-centric CPQ system is used for PS.
The greater the differences in PS revenue management, the more the need for a new CPQ platform. Many PS organizations are making a mistake by attempting to make product-centric CPQ platforms work for their unique costing, pricing, and selling needs. My team and I learned that the more a PS revenue model is unique and one-of-a-kind, the more it requires a unique CPQ platform.
Getting product-based CPQ rules and constraint logic right is hard in PS. Our teams’ biggest challenge in recycling IT’s CPQ app for PS was how difficult it was to get the rules-based engine to work for the wide variety of variables in a common service engagement. Rules created for transaction velocity needed to be reworked for greater variety. PS engagements didn’t follow a common logic structure like a product, making the constraint logic code only somewhat usable.
Only launch after CRM and Revenue Management integration is complete. Our team was handed a project that had languished in IT for nearly a year because PS selling teams wouldn’t use it. The problem was that the quoting module ran batch updates to a series of databases to get customer records and fetch the latest price tables off of a mainframe. In addition, CPQ wasn’t connected in real-time to CRM or Revenue Management.
Closing Long-Standing Services CPQ Gaps
The more a Services CPQ app can close the gaps between CRM, PSA, Revenue Management, and CPQ apps and their workflows, the more effective it will be stopping margin and revenue leakage. Having APIs that share data in real-time between CRM, PSA, and Revenue Management within each quote creation session has the potential to save thousands of hours a year. FinancialForce’s recently announced Services CPQ shows how a platform-based integration strategy works. The following graphic shows how revenue potential increases as a Services CPQ’s systems become more integrated.
FinancialForce’s approach to taking on the challenge of providing an enterprise-grade Services CPQ is noteworthy for several reasons, including the following:
Real-time visibility and control of Services CPQ Effectiveness. Having Services CPQ, PSA and Revenue Management on the same Salesforce platform provides the visibility and control PS sales managers need to track quoting effectiveness by program, geography, customer segment, and rep. The more real-time the data integration across these systems the greater the potential for revenue growth in existing accounts and winning new ones.
Changing professional services quotes in real-time without impacting sales cycles is possible. Due to the integrated design of Services CPQ, one change made anywhere on a quote will replicate through the entire system and change all related factors immediately.
Getting in control of professional services engagement dates and utilization rates by associates helps reduce time-to-market and assures better time-to-customer performance. Keeping track of the myriad of factors that influence a services quote using a manually-based process is too slow for how quickly engagements are decided. Instead, having a single, unified data model that can track effectiveness and provide updates on how they impact engagement project plans is needed to excel at selling with Services CPQ. Adopting an agile CPQ strategy that relies on an integration thread to unify all systems is the secret to scaling and selling more with an agile approach to services CPQ.
Pricing needs to be one of the core strengths in an integrated Services CPQ platform. Realizing how a customers’ requested changes to a professional services engagement will impact costs and margins gives PS teams with an integrated system a formidable pricing advantage. FinancialForce’s approach to solving the Services CPQ challenge shows the potential to take on this challenge and provide its PS customers with the insights they need to upsell engagements – and not lose margin doing it.
A must-have for any Services CPQ platform is support for channel partner collaboration and team quoting. For any Services CPQ to scale up and deliver its full potential value, there needs to be support for customizing partner selling experiences while providing for team selling and quoting. FinancialForce solves this by relying on the Salesforce platform. By closing the gaps between the systems Services CPQ relies on, the channel selling teams and partners gain greater flexibility in defining customized products.
Services CPQ needs to scale out on a platform to achieve its full potential by providing the analytical insights to track engagement lifecycles and customer lifetime value by engagement. FinancialForce has proven they can do this in their Spring 2021 release. Taking on the most challenging aspects of a Services CPQ architecture starts by providing insights and guidance on how best to optimize the mix of associates and their utilization and billing rates, locations of each engagement, margin threshold levels, and the expected duration of each engagement. Additionally, the world’s leading professional services organizations could use an automated Services CPQ solution as many of them don’t rely on enough data, letting revenue leakage happen without knowing it.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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)
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.
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.
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.
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. 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.
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.
AI can help retailers understand the consumer, Phys.org. January 14, 2019
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Bottom Line: Capitalizing on AI and machine learning’s inherent strengths to create contextual intelligence in real-time, LogicMonitor’s early warning and failure prevention systems reflect where AIOps is delivering results today.
LogicMonitor’s track record of making solid contributions to their customers’ ability to bring greater accuracy, insight, and precision into monitoring all IT assets is emerging as a de facto industry standard. Recently I was speaking with a startup offering Hosted Managed Services of a variety of manufacturing applications, and the must-have in their services strategy is LogicMonitor LM Intelligence. LogicMonitor’s AIOps platform is powered by LM Intelligence, enabling customers’ businesses to gain early warning into potential trouble spots in IT operations stability and reliability. LogicMonitor does the hard work for you with automated alert thresholds, AI-powered early warning capabilities, customizable escalation chains, workflows, and more.
Engineers who are working at the Hosted Managed Services provider I recently spoke with say LM Intelligence is the best use case of AI and machine learning to provide real-time alerts, contextual insights, discover new patterns in data, and make automation achievable. The following is an example of the LM Intelligence dashboard:
How LogicMonitor’s Architecture Supports AIOps
One of the core strengths LogicMonitor continues to build on is integration, which they see as essential to their ability to excel at providing AIOps support for their customers. Their architecture is shown below. By providing real-time integration to public cloud platforms, combined with control over the entire IT infrastructure structure along with over 2,000 integrations from network to cloud, LogicMonitor excels at unifying diverse IT environments into a single, cohesive AIOps-based intelligence system. The LogicMonitor platform collects cloud data through our cloud collectors. These collectors retrieve metrics such as the cloud provider health and billing information by making API calls to the cloud services. The collector is a Windows Service or background process that is installed in a virtual machine. This collector then pulls metrics from the different devices using a variety of different methods, including SNMP, WMI, perf Mon JMX, APIs, and scripts.
Using AIOps To Monitor, Analyze, Automate
LogicMonitor has created an architecture that’s well-suited to support the three dominant dimensions of AIOps, including Monitoring, Analytics (AIOps), and Automating. Their product and services strategies in the past have reflected a strong focus on Monitoring. The logic of prioritizing Monitoring as a product strategy area was to provide the AI and machine learning models with enough data to train on so they could identify anomalies in data patterns faster. Their 2018/2019 major releases in the Monitor area reflect how the unique strength they have of capturing and making use of any IT asset that can deliver a signal is paying off. Key Monitor developers recently include the following:
Public Cloud Monitoring
LogicMonitor’s core strengths in AIOps are in the Anomaly Detection and Early Warning System areas of their product strategy. Their rapid advances in the Early Warning System development show where AIOps is delivering solid results today. Supporting the Early Warning System, there are Dynamic Thresholds and Root Cause Analysis based on Dependencies as well.
The Automate area of their product strategy shows strong potential for future growth, with the ServiceNow integration having upside potential. Today Alert Chaining and Workflow support integrations to Ansible, Terraform, Slack, Microsoft, Teama, Putter, Terraform, OpsGenie, and others.
LogicMonitor’s platform handles 300B metrics on any given day and up to 10B a month, with over 28K collectors deployed integrated with approximately 1.4M devices being monitored. Putting AI and machine learning to work, interpreting the massive amount of data the platform captures every day to fine-tune their Early Warning and Failure Prevention Systems, is one of the most innovative approaches to AIOps today. Their AIOps Early Warning System is using machine learning Algorithms to fine-tune Root Cause Analysis and Dynamic Thresholds continually. AIOps Log Intelligence is also accessing the data to complete Automatic Log Anomaly Detection, Infrastructure change detection, and Log Volume Reduction to Signal analysis.
Startups’ ambitious AI-based new product development is driving AI-related investment with $16.5B raised in 2019, driven by 695 deals according to PwC/CB Insights MoneyTree Report, Q1 2020.
AI expertise is a skill product development teams are ramping up their recruitment efforts to find, with over 7,800 open positions on Monster, over 3,400 on LinkedIn and over 4,200 on Indeed as of today.
One in ten enterprises now uses ten or more AI applications, expanding the Total Available Market for new apps and related products, including chatbots, process optimization and fraud analysis, according to MMC Ventures.
Rapid advances in AI-based apps, products and services will also force the consolidation of the IoT platform market. The IoT platform providers concentrating on business challenges in vertical markets stand the best chance of surviving the coming IoT platform shakeout. As AI and ML get more ingrained in new product development, the IoT platforms and ecosystems supporting smarter, more connected products need to make plans now how they’re going to keep up. Relying on technology alone, like many IoT platforms are today, isn’t going to be enough to keep up with the pace of change coming. The following are 10 ways AI is improving new product development today:
14% of enterprises who are the most advanced using AI and ML for new product development earn more than 30% of their revenues from fully digital products or services and lead their peers is successfully using nine key technologies and tools. PwC found that Digital Champions are significantly ahead in generating revenue from new products and services and more than a fifth of champions (29%) earn more than 30% of revenues from new products within two years of information. Digital Champions have high expectations for gaining greater benefits from personalization as well. The following graphic from Digital Product Development 2025: Agile, Collaborative, AI-Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.) compares Digital Champions’ success with AI and ML-based new product development tools versus their peers:
61% of enterprises who are the most advanced using AI and ML (Digital Champions) use fully integrated Product Lifecycle Management (PLM) systems compared to just 12% of organizations not using AI/ML today (Digital Novices). Product Development teams the most advanced in their use of AL & ML achieve greater economies of scale, efficiency and speed gains across the three core areas of development shown below. Digital Champions concentrate on gaining time-to-market and speed advantages in the areas of Digital Prototyping, PLM, co-creation of new products with customers, Product Portfolio Management and Data Analytics and AI adoption:
AI is actively being used in the planning, implementation and fine-tuning of interlocking railway equipment product lines and systems. Engineer-to-order product strategies introduce an exponential number of product, service and network options. Optimizing product configurations require an AI-based logic solver that can factor in all constraints and create a Knowledge Graph to guide deployment. Siemens’ approach to using AI to find the optimal configuration out of 1090 possible combinations provides insights into how AI can help with new product development on a large scale. Source: Siemens, Next Level AI – Powered by Knowledge Graphs and Data Thinking, Siemens China Innovation Day, Michael May, Chengdu, May 15, 2019.
Eliminating the roadblocks to getting new products launched starts with using AI to improve demand forecast accuracy. Honeywell is using AI to reduce energy costs and negative price variance by tracking and analyzing price elasticity and price sensitivity as well. Honeywell is integrating AI and machine-learning algorithms into procurement, strategic sourcing and cost management getting solid returns across the new product development process. Source: Honeywell Connected Plant: Analytics and Beyond. (23 pp., PDF, no opt-in) 2017 Honeywell User’s Group.
Relying on AI-based techniques to create and fine-tune propensity models that define product line extensions and add-on products that deliver the most profitable cross-sell and up-sell opportunities by product line, customer segment and persona. It’s common to find data-driven new product development and product management teams using propensity models to define the products and services with the highest probability of being purchased. Too often, propensity models are based on imported data, built-in Microsoft Excel, making their ongoing use time-consuming. AI is streamlining creation, fine-tuning and revenue contributions of up-sell and cross-sell strategies by automating the entire progress. The screen below is an example of a propensity model created in Microsoft Power BI.
AI is enabling the next generation of frameworks that reduce time-to-market while improving product quality and flexibility in meeting unique customization requirements on every customer order. AI is making it possible to synchronize better suppliers, engineering, DevOps, product management, marketing, pricing, sales and service to ensure a higher probability of a new product succeeding in the market. Leaders in this area include BMC’s Autonomous Digital Enterprise (ADE). BMC’s ADE framework shows the potential to deliver next-generation business models for growth-minded organizations looking to run and reinvent their businesses with AI/ML capabilities and deliver value with competitive differentiation enabled by agility, customer centricity and actionable insights. The ADE framework is capable of flexing and responding more quickly to customer requirements than competitive frameworks due to the following five factors: proven ability to deliver a transcendent customer experience; automated customer interactions and operations across distributed organizations; seeing enterprise DevOps as natural evolution of software DevOps; creating the foundation for a data-driven business that operates with a data mindset and analytical capabilities to enable new revenue streams; and a platform well-suited for adaptive cybersecurity. Taken together, BMC’s ADE framework is what the future of digitally-driven business frameworks look like that can scale to support AI-driven new product development. The following graphic compares the BMC ADE framework (left) and the eight factors driving digital product development as defined by PwC (right) through their extensive research. For more information on BMC’s ADE framework, please see BMC’s Autonomous Digital Enterprise site. For additional information on PwC’s research, please see the document Digital Product Development 2025: Agile, Collaborative, AI-Driven and Customer Centric, PwC, 2020 (PDF, 45 pp.).
Using AI to analyze and provide recommendations on how product usability can be improved continuously. It’s common for DevOps, engineering and product management to run A/B tests and multivariate tests to identify the usability features, workflows and app & service responses customers prefer. Based on personal experience, one of the most challenging aspects of new product development is designing an effective, engaging and intuitive user experience that turns usability into a strength for the product. When AI techniques are part of the core new product development cycle, including usability, delivering enjoyable customer experiences, becomes possible. Instead of a new app, service, or device is a chore to use, AI can provide insights to make the experience intuitive and even fun.
Forecasting demand for new products, including the causal factors that most drive new sales is an area AI is being applied to today with strong results. From the pragmatic approaches of asking channel partners, indirect and direct sales teams, how many of a new product they will sell to using advanced statistical models, there is a wide variation in how companies forecast demand for a next-generation product. AI and ML are proving to be valuable at taking into account causal factors that influence demand yet had not been known of before.
Designing the next generation of Nissan vehicles using AI is streamlining new product development, trimming weeks off new vehicle development schedules. Nissan’s pilot program for using AI to fast-track new vehicle designs is called DriveSpark. It was launched in 2016 as an experimental program and has since proven valuable for accelerating new vehicle development while ensuring compliance and regulatory requirements are met. They’ve also used AI to extend the lifecycles of existing models as well. For more information, see the article, DriveSpark, “Nissan’s Idea: Let An Artificial Intelligence Design Our Cars,” September 2016.
Using generative design algorithms that rely on machine learning techniques to factor in design constraints and provide an optimized product design. Having constraint-optimizing logic within a CAD design environment helps GM attain the goal of rapid prototyping. Designers provide definitions of the functional requirements, materials, manufacturing methods and other constraints. In May 2018, General Motors adopted Autodesk generative design software to optimize for weight and other key product criteria essential for the parts being designed to succeed with additive manufacturing. The solution was recently tested with the prototyping of a seatbelt bracket part, which resulted in a single-piece design that is 40% lighter and 20% stronger than the original eight component design. Please see the Harvard Business School case analysis, Project Dreamcatcher: Can Generative Design Accelerate Additive Manufacturing? for additional information.
Greenfield, D. (2019). Advice on scaling IIoT projects. ProFood World
Hayhoe, T., Podhorska, I., Siekelova, A., & Stehel, V. (2019). Sustainable manufacturing in industry 4.0: Cross-sector networks of multiple supply chains, cyber-physical production systems and AI-driven decision-making. Journal of Self-
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.
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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.
Product Managers need to consider how adding Location Intelligence can improve the contextual accuracy of marketing, sales, and customer service apps and platforms.
Marketers need to look at how they can capitalize on smartphones’ prolific amounts of location data for improving advertising, buying, and service experiences for customers.
R&D, Operations, and Executive Management lead all other departments in their adoption and use of Location Intelligence this year.
Enterprises favor cloud-based Location Intelligence deployments in 2020, with on-premise deployments also seeing new sales this year.
These and many other fascinating insights are from Dresner Advisory Services’2020 Location Intelligence Market Study, their 7th annual report that examines enterprise end-users’ requirements and features including geocoding support, location intelligence visualization, analytics capabilities, and third-party GIS integration. The study is noteworthy for its depth of insights into industry adoption of Location Intelligence and how user requirements drive industry capabilities. Dresner Advisory Services defines location intelligence as a form of Business Intelligence (BI), where the dominant dimension used for analysis is location or geography. Most typically, though not exclusively, analyses are conducted by viewing data points overlaid onto an interactive map interface.
“When we began covering Location Intelligence in 2014, we saw the potential for the topic to gain mainstream interest,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. “With the growth in visualization and the emergence of the Internet of Things (IoT), incorporating maps and location into business analyses have become increasingly important to many organizations.” Please see page 11 for a description of the methodology and page 13 for an overview of study demographics. Wisdom of Crowds® research is based on data collected on usage and deployment trends, products, and vendors.
Key insights from the study that provides an excellent background on the current state of location intelligence in 2020 include the following:
R&D, Operations, and Executive Management lead all enterprise areas in adoption with Location Intelligence being considered critical to their ongoing operations. The majority of Marketing & Sales leaders see Location Intelligence as very important to their ongoing operations. The following graphic compares how important Location Intelligence is to each of the seven departments included in the survey:
90% of Government organizations consider Location Intelligence to be critical or very important to their ongoing operations. Healthcare providers have the second-highest number of organizations who rate Location Intelligence as critical. The study found that mean importance levels are similar across Business Services, Financial Services, Manufacturing, and Consumer Services organizations and decline further among Technology, Retail/Wholesale, and Higher Education segments.
Data visualization/mapping dominates all other Location Intelligence use cases in 2020, with over 70% of organizations considering it critical or very important to accomplishing their goals. The study found that the majority of other use cases haven’t achieved the broad adoption data visualization & mapping has. Despite the lower levels of criticality assigned to the nine other use cases, they each show the potential to streamline essential marketing, sales, and operational areas of an enterprise. Site planning/site selection, geomarketing, territory management/optimization, and logistics optimization make up a tier of secondary interest that taken together streamlines supply chains while making an organization easier to buy from. The Dresner research team also defines the third tier of use cases led by fleet routing and citizen services, followed by IoT & smart cities, indoor mapping, and real estate investment/pricing analysis. Despite IoT being over-promoted by vendors, just over 50% of enterprises say the technology is not important to them at this time. The following graphic compares Location Intelligence use cases by the level of criticality as defined by responding organizations:
R&D leads all departments in data visualization/mapping adoption, reflecting the high level of importance this use case has across entire enterprises as well. Additional departments and functional areas relying on data visualization/mapping include Operations, Business Intelligence Competency Center (BICC), and Executive Management. Geomarketing is seeing the most significant adoption in Marketing & Sales. Operations lead all other functional areas in the adoption of logistics optimization and fleet routing use cases. Dresner’s research team found that R&D’s interest in Location Intelligence, which varies across use cases, may reflect the use of packaged applications as well as select custom development.
Map-based visualization, dashboard inclusion of maps, and drill-down navigation through map interfaces are the three highest priority features enterprises look for today. These three features are considered very important to between 64% to 67% of leaders interviewed. Layered visualizations, multi-layer support, and custom region definition are the next most important features. The following graphic provides an overview of prioritized Location intelligence visualization features.
Executive Management, BICC, and Operations have the highest level of interest in map-based visualizations that further accelerate the adoption of Location Intelligence across enterprises. Executive Management also leads all others in their interest in dashboard inclusion of maps and custom map support. Executive Management’s increasing adoption of multiple Location intelligence use cases is a catalyst driving greater enterprise-wide adoption. R&D’s prioritizing the layering of visualizations on top of maps, offline mapping and animation of data on maps are leading indicators of these use cases attaining greater enterprise adoption in future years.
Four of the top ten Location Intelligence features are considered very important/critical to enterprises, reflecting a maturing market. The most popular (counting, quantifying, or grouping) is critical or very important to 46% of organizations and at least important to nearly 70%. Another indicator of how quickly Location Intelligence is maturing in enterprises is the advanced nature of analytics features being relied on today. Predicting trends and volatility, detecting clusters and outliers, and measuring distances reflect how multiple departments in enterprises are collaborating using Location Intelligence to achieve their shared goals.
Government dominates the use of data visualization/mapping with a strong interest in site planning/site selection, citizen services, fleet routing, and territory management. Business Services are most interested in using Location Intelligence for Indoor Mapping and IoT & Smart Cities. Geomarketing is the most adopted feature in Higher Education, Financial Services, Healthcare, and Retail/Wholesale. Manufacturing and Retail/Wholesale lead all other industries in their adoption of Logistics Optimization. The following graphic provides insights into Location Intelligence use case by industry:
Executive Management and Business Intelligence Competency Centers (BICC) most prioritize Location Intelligence applications that have built-in or native geocoding. Enterprises are looking at how built-in or native geocoding can scale across their Location Intelligence use cases and broader BI strategy with Executive Management taking the lead on achieving this goal. Automated geocoding support and street-level geocoding support are also a high priority to Executive Management. Marketing/Sales lead all other departments in their interest in geofencing/reverse geofencing, indicating enterprises are beginning to use these geocoding features to achieve greater accuracy in their marketing and selling strategies. It’s interesting to note that geofencing/reverse geofencing has progressed from R&D in previous studies to Marketing/Sales putting the highest priority on it today. Dresner’s research team interprets the shift to customer-facing strategies being an indicator of broader enterprise adoption for geofencing/reverse geofencing.
61% of organizations say Google integration is essential to their Location Intelligence strategies. Google continues to dominate organizations’ roadmaps as the integration of choice for adding more GIS data to Location Intelligence strategies. ESRI is the second choice with 45% of organizations naming it as an integration requirement. Database extensions (30%) are the next most cited, followed by OpenStreetMap (20%). All other choices are requirements at less than 20% of organizations.