Customers’ expectations, preferences, changing patterns in how and why they purchase need to be the core of any digital transformation effort. With it, digital transformation projects flourish and take on a life of their own. Without it, I’ve seen digital transformation projects become myopic, narrowly focused, substituting internal metric gains for measures that matter most to customers.
Digital Maturity Drives Revenue
Anyone who has worked on a digital transformation project quickly sees how the most digitally mature organizations can turn their investments in transformation into revenue by overwhelming customers with value. Initiatives that put customers first can serve to generate greater confidence among C-level executives and board members, leading to more funding. This is because business cases for customer-centric digital transformation projects are easier to create, more defensible and best of all, point to revenue gains and cost reductions.
Deloitte Insights’ recent survey uncovering the connection between digital maturity and financial performance accurately reflects the true state of customer-centric digital transformation. The article explains how the more digitally mature an organization is, the more achievable gains are in diversity and inclusion, Corporate Social Responsibility (CSR), customer satisfaction, product quality, gross margin and long-term financial performance. Deloitte’s latest study finds a strong correlation between the digital maturity of an enterprise and its net revenue and net profit margin. The following graphic makes clear how valuable pursuing digital maturity is, with customers being at the center of all transformation efforts. This contributes to greater net revenue and net profit margin growth:
A fascinating point regarding Deloitte Insights’ research is the correlation it uncovered between an organization’s digital transformation maturity and the benefits they gain in efficiency, revenue growth, product/service quality, customer satisfaction and employee engagement. They found a hierarchy of pivots successful enterprises make to keep pursuing more agile, adaptive organizational structures combined with business model adaptability, all driven by customer-driven innovation. The most digitally mature organizations can adopt new frameworks that prioritize market responsiveness, customer-centricity and have analytics and data-driven culture with actionable insights embedded in their DNA.
Mastering Data & Removing Roadblocks Are Key To Driving Customer Value
The two highest-payoff areas for accelerating digital maturity and achieving its many benefits are mastering data and creating more intelligent workflows. Deloitte Insights’ research team looked at the seven most effective digital pivots enterprises can make to become more digitally mature. The pivots that paid off the best as measured by revenue, margin, customer satisfaction, product/service quality and employee engagement combined data mastery and improving intelligent workflows. The following graphic shows how 51% of revenue growth can be explained by these two factors alone and 49% of improved customer satisfaction.
Data mastery and intelligent workflows are among the easiest areas to measure and include in a business case for digital transformation projects aimed at delivering a transcendent customer experience. Choosing to excel on the dimension of customer-centric data mastery gives enterprises the insights they need to create their unique omnichannel platforms. Adding in intelligent workflows that give customers the freedom to buy how, where and when they choose across any digital platform is the cornerstone of entirely new digital business models today. Capturing the voice of the customer and combining data mastery and intelligent workflows to gain an accurate, true 360-degree view of customers is invaluable for every aspect of go-to-market strategies.
Achieving Digital Maturity Requires A Framework
Enterprises that have customer centricity and a data-driven mindset are the most likely to succeed with a digital transformation initiative. As the Deloitte Insights study inferred, the most digitally mature organizations are continually adapting to customer and market dynamics. They’re prioritizing market responsiveness, striving to improve customer-centricity and have data-driven cultures with actionable insights as part of their DNA. Enterprises who see new digital business model opportunities and act on them capitalize on these three areas of organizational strength. They’re also able to combine their data mastery and intelligent workflows to identify areas of competitive opportunity to help them excel for their customers.
Consider how cybersecurity is now part of any customer experience, for good and bad. Multi-factor Authentication (MFA) and many other forms of identity verification secure customer transactions, yet they can also cause dissatisfaction. For any digitally mature enterprise, integrating cybersecurity into their existing framework is a challenge. The growth of new frameworks designed to empower greater customer-centricity, agility and actionable insights across every facet of a business is a fascinating area of watch.
One of the more interesting is BMC’s Autonomous Digital Enterprise (ADE) framework, which is shown below. Mapping Deloitte Insights’ top investment priorities for the next 12 months across all digital maturity levels to the ADE framework shows why frameworks like BMC’s are gaining adoption, particularly as organizations look to run and reinvent themselves with new digital business models built around AI/ML capabilities. The following graphic provides insights into how Deloitte’s top investment priorities are integral to BMC’s Autonomous Digital Enterprise Framework and its many contributions to the success of new digital business growth.
Quantifying the impact of having a customer-centric digital transformation strategy has proved elusive until recently. Deloitte Insights’ research shows how digital maturity enables greater gains from customer-centric digital transformation efforts. What’s fascinating about their research is how the progression of digital pivots leads to improved margin, revenue, customer satisfaction, diversity and inclusion and product quality gains. Equally interesting is the growing utility of frameworks like BMC’s, which are designed to enable long-standing enterprises to seamlessly embrace new digital business models, so they can flex and change with the world around them.
IDC predicts the global market for custom application development services is forecast to grow from $47B in 2018 to more than $61B in 2023, attaining a 5.3% Compound Annual Growth Rate (CAGR) in five years.
40% of DevOps teams will be using application and infrastructure monitoring apps that have integrated artificial intelligence for IT operations (AIOps) platforms by 2023, according to Gartner.
Looking to reduce the delays DevOps teams are challenged with, software development tool providers are accelerating the pace of integrating AI- and Machine Learning technologies into their apps and platforms. Accelerating every phase of the Software Development Lifecycle (SDLC) while increasing software quality is the goal. And the good news is use cases are showing those goals are being accomplished, taking DevOps to a new level of accuracy, quality, and reliability.
What’s particularly fascinating about the ten ways AI is accelerating DevOps is how effective it is proving to be in assisting developers with the difficult, time-consuming tasks that take away from coding. One of the most time-consuming tasks is managing the many iterations and versions of requirements documents. A leader in using AI to streamline every phase of the SDLC and assist with managing requirements is Jira Software from Atlassian, widely considered the industry standard in this area of DevOps.
The following are ten ways AI is accelerating DevOps today:
Improving DevOps productivity by relying on AL and ML to autosuggest code segments or snippets in real-time to accelerate development. DevOps teams interviewed for this article from several leading enterprise software companies competing in CRM, Supply Chain Management, and social media markets say this use case of AI is the most productive and has generated the greatest gains in accuracy. Initial efforts at using AI to autocomplete code were hit or miss, according to a DevOps lead at a leading CRM provider. She credits DevOps’ development tools providers’ use of supervised machine learning algorithms with improving how quickly models learn and respond to code requests. Reflecting what the DevOps teams interviewed for this article prioritized as the most valuable AI development in DevOps, Microsoft’s Visual Studio Intellicode has over 6 million installs as of today.
Streamlining Requirements Management using AI is proving effective at improving the accuracy and quality of requirements documents capturing what users need in the next generation of an app or platform. AI is delivering solid results streamlining every phase of creating, editing, validating, testing, and managing requirements documents. DevOps team members are using AI- and ML-based requirements management platforms to save time so they can get back to coding and creating software products often on tight deadlines. Getting requirements right the first time helps keep an entire project on the critical path of its project plan. Seeing an opportunity to build a business case of keeping projects on schedule, AI-powered software development tools providers are quickly developing and launching new apps in their area. It’s fascinating to watch how quickly Natural Language Processing techniques are being adopted into this area of DevOps tools. Enterprises using AI-based tools have been able to reduce requirements review times by over 50%.
AI is proving effective at bug detection and auto-suggestions for improving code. At Facebook, a bug detection tool predicts defects and suggests remedies that are proving correct 80% of the time with AI tools learning to fix bugs automatically. Semmle CodeQL is considered the leading AI-based DevOps tool in this area. DevOps teams using CodeQL can track down vulnerabilities in code and also find logical variants in their entire codebase. Microsoft uses Semmle for vulnerability hunting. Security researchers in Microsoft’s security response team use Semmle QL to find variants of critical problems, allowing them to identify and respond to serious code problems and prevent incidents.
AI is assisting in prioritizing security testing results and triaging vulnerabilities. Interested in learning more about how ML can find code vulnerability in real-time, I spoke with Maty Siman, CTO Checkmarx, says that “even organizations with the most mature SDLCs often run into issues with prioritizing and triaging vulnerabilities. ML algorithms that focus on developers’ or AppSec teams’ attention on true positives and vulnerable components that pose a threat are key to navigating this challenge.” Maty also says that ML algorithms can be taught to understand that one type of vulnerability vs. another has a higher percentage of being a true positive. With this automated “vetting” process in place, teams can optimize and accelerate their remediation efforts in a much more informed manner.
Improving software quality assurance by auto-generating and auto-running test cases based on the unique attributes of a given code base is another area where AI is saving DevOps teams valuable time. This is invaluable for stress-testing new apps and platforms across a wide variety of use cases. Creating and revising test cases is a unique skill set on any DevOps team, with the developers with this skill often being overwhelmed with test updates. AI-based software development tools are eliminating test coverage overlaps, optimizing existing testing efforts with more predictable testing, and accelerating progress from defect detection to defect prevention. AI-based software development platforms can identify the dependencies across complex and interconnected product modules, improving overall product quality in the process. Improving software quality enhances customer experiences, as well.
AI is proving adept at troubleshooting defects in complex software apps and platforms after they’ve been released and shipped to customers. Enterprise software companies go to great lengths in their software QA processes to eliminate bugs, logic errors, and unreliable segments of code. Retrofitting releases or, worst case, recalling them is costly and impacts customers’ productivity. AI-based QA tools are proving effective at predicting which areas of an enterprise application will fail before being delivered into complex customer environments. AI is proving effective at root cause analysis, and also has proved effective in accelerating a leading CRM providers’ application delivery and a 72% reduction in time-to-restore in customers’ enterprise environments. Another DevOps team says they are using AI to auto-configure their applications’ settings to optimize performance in customer deployments.
ML-based code vulnerability detection can spot anomalies reliably and alert DevOps teams in real-time. Maty Siman, CTO Checkmarx told me that, “assuming that your developers are writing quality, secure code, machine learning can set a baseline of “normal activity” and identify and flag anomalies from that baseline.” He continued, saying that “ultimately, we live in an IT and security landscape that’s evolving every minute of every day, requiring systems and tools that learn and adapt at the same, if not a greater, speed. Organizations and developers can’t do it alone and require solutions that improve the accuracy of threat detection to help them prioritize what matters most.” Spotting anomalies quickly and taking action on them is integral to building a business case for AI software-based QA and DevOps tools.
Advanced DevOps teams are using AI to analyze and find new insights across all development tools, Application Performance Monitoring (APM), Software QA, and release cycle systems. DevOps teams at a leading Supply Chain Management (SCM) enterprise software provider are using AI to analyze why certain projects go so well and deliver excellent code while others get caught in perpetual review and code rewrite cycles. Using supervised machine learning algorithms, they’re able to see patterns and gain insights into their data. Becoming data-driven is quickly becoming part of their DNA, a DevOps lead told me this week on a call.
Improving traceability within each release cycle to find where gaps in DevOps collaboration and data integration workflows can be improved. AI is enabling DevOps teams to stay more coordinated with each other, especially across remote geographic locations. AI-driven insights are helping to see how shared requirements and specifications can reflect localization, unique customer requirements, and specific performance benchmarks.
Creating a more integrated DevOps strategy where AI can deliver the most value depends on frameworks that can keep DevOps customer-centric while improving agility and nurturing an analytics-driven DNA to gain insights into operations. DevOps leaders interviewed for this article say integrating security into development cycles reduces bottlenecks that get in the way of staying on schedule. Several went on to say that frameworks capable of integrating Quality Assurance into the DevOps workflows are key. AI’s use cases taken together reflect the potential to revolutionize DevOps. Executing on this promise, however, requires a framework that empowers enterprise DevOps teams to deliver a transcendent customer experience, automate customer transactions, and provide support for automation everywhere. One of the leaders in this area is BMC’s Autonomous Digital Enterprise framework, which helps businesses harness AI/ML capabilities to run and reinvent in a rapidly transforming world. It’s helping enterprises innovate faster than their competitors by enabling the agility, customer centricity, and actionable insights integral to driving data-driven business outcomes.
Accelerating development cycles while ensuring the highest quality code gets produced is a challenge all DevOps teams face. AI is helping to accelerate every phase of DevOps development cycles by anticipating what developers need before they ask for it. Auto suggesting code segments, improving software quality assurance techniques with automated testing, and streamlining requirements management are core areas where AI is delivering value to DevOps today.
Bottom Line: DevOps and security teams need to leave one-time gating inspections in the past and pursue a more collaborative real-time framework to achieve their shared compliance, security and time-to-market goals.
Shorter product lifecycles the need to out-innovate competitors and exceed customer expectations with each new release are a few of the many reasons why DevOps is so popular today. Traditional approaches to DevOps teams collaborating with security aren’t working today and product releases are falling behind or being rushed to-market leading to security gaps as a result.
Based on conversations with DevOps team leaders and my own experience being on a DevOps team the following are factors driving the urgency to integrate security into DevOps workflows:
Engineering, DevOps and security teams each have their lexicon and way of communicating reinforced by siloed systems.
Time-to-market and launch delays are common when engineering, DevOps and security don’t have a unified system to use that includes automation tools to help scale tasks and updates.
Developers are doing Application Security Testing (AST) with tools that aren’t integrated into their daily development environments, making the process time-consuming and challenging to get done.
Limiting security to the testing and deployment phases of the Software Development Lifecycle (SDLC) is a bottleneck that jeopardizes the critical path, launch date and compliance of any new project.
Adding to the urgency is the volume of builds DevOps teams produce in software companies and enterprises daily and the need for having security integrated into DevOps becomes clear. Consider the fact that Facebook on Android alone does 50,000 to 60,000 builds a day according to research cited from Checkmarx who is taking on the challenge of integrating DevOps and security into a unified workflow. Their Software Security Platform unifies DevOps with security and provides static and interactive application security testing, newly launched software composition analysis and developer AppSec awareness and training programs to reduce and remediate risk from software vulnerabilities.
Synchronizing Security Into DevOps Delivers Much Needed Speed & Scale
DevOps teams thrive in organizations built for speed, continuous integration, delivery and improvement. Contrast the high-speed always-on nature of DevOps teams with the one-time gating inspections security teams use to verify regulatory, industry and internal security and compliance standards and it’s clear security’s role in DevOps needs to change. Integrating security into DevOps is proving to be very effective at breaking through the roadblocks that stand in the way of getting projects done on time and launched into the market. Getting the security and DevOps team onto the same development platform is needed to close the gaps between the two teams and accelerate development. Of the many approaches available for accomplishing this Checkmarx’s approach to integrating Application Security Testing into DevOps shown below is among the most comprehensive:
Making DevOps A Core Strength Of An Organization
By 2025 nearly two-thirds of enterprises will be prolific software producers with code deployed daily to meet constant demand and over 90% of new apps will be cloud-native, enabling agility and responsiveness according to IDC FutureScape: Worldwide IT Industry 2020 Predictions. IDC also predicts there will be 1.6 times more developers than now, all working in collaborative systems to enable innovation. The bottom line is that every company will be a technology company in the next five years according to IDC’s predictions.
To capitalize on the pace of change happening today driven by DevOps, organizations need frameworks that deliver the following:
Greater agility and market responsiveness – Organizations need to create operating models that integrate business, operations and technology into stand-alone businesses-within-the-business domains.
Customer Centricity at the core of business models – The best organizations leverage a connected economy to ensure that they can meet and exceed customer expectations. By creating an ecosystem that caters to every touchpoint of the customer journey using technology, these organizations seem to anticipate their customer needs and deliver the goods and services needed at the right time via the customer’s preferred channel. As a result, successful organizations see growth from their existing customer base while they acquire new ones.
Have a DNA the delivers a wealth of actionable Insights – Organizations well-positioned to turn data into insights that drive actions to serve and anticipate customer needs are ahead of competitors today regarding time-to-market. These organizations know how to pull all the relevant information, capabilities and people together so they can act quickly and efficiently in making the right decisions. They are the companies that will know the outcome of their actions before they take them and they will be able to anticipate their success.
BMC’s Autonomous Digital Enterprise framework, shown below highlights how companies that have an innovation mindset and the three common traits of agility, customer centricity and actionable insights at their foundation have greater consistency and technology maturity in their business model characteristics compared to competitors. They also can flex and support fundamental operating model characteristics and key technology-enabled tenets. These tenets include delivering a transcendent customer experience, automating customer transactions and providing automation everywhere seeing enterprise DevOps as a natural evolution of DevOps, enabling a business to be more data-driven and achieving more adaptive cybersecurity in a Zero-Trust framework.
Meeting the challenge of integrating security in DevOps provides every organization with an opportunity to gain greater agility and market responsiveness, become more customer-centric and develop the DNA to be more data-driven. These three goals are achievable when organizations look to how they can build on their existing strengths and reinvent themselves for the future. As DevOps success goes so goes the success of any organization. Checkmarx’s approach to putting security at the center of DevOps is helping to break down the silos that exist between engineering, DevOps and security. To attain greater customer-centricity, become more data-driven and out-innovate competitors, organizations are adopting frameworks including BMC’s Autonomous Digital Enterprise to reinvent themselves and be ready to compete in the future now.
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-
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.
Bottom Line: This year’s hard reset is amplifying how vital customer relationships are and how much potential AI has to find new ways to improve them.
30% of customers will leave a brand and never come back because of a bad experience.
27% of companies say improving their customer intelligence and data efforts are their highest priority when it comes to customer experience (CX).
By 2023, 30% of customer service organizations will deliver proactive customer services by using AI-enabled process orchestration and continuous intelligence, according to Gartner.
$13.9B was invested in CX-focused AI and $42.7B in CX-focused Big Data and analytics in 2019, with both expected to grow to $90B in 2022, according to IDC.
The hard reset every company is going through today is making senior management teams re-evaluate every line item and expense, especially in marketing. Spending on Customer Experience is getting re-evaluated as are supporting AI, analytics, business intelligence (BI), and machine learning projects and spending. Marketers able to quantify their contributions to revenue gains are succeeding the most at defending their budgets.
Fundamentals of CX Economics
Knowing if and by how much CX initiatives and strategies are paying off has been elusive. Fortunately, there are a variety of benchmarks and supporting methodologies being developed that contextualize the contribution of CX. KPMG’s recent study, How Much Is Customer Experience Worth? provides guidance in the areas of CX and its supporting economics. The following table provides an overview of key financial measures’ interrelationships with CX. The table below summarizes their findings:
The KPMG study also found that failing to meet customer expectations is two times more destructive than exceeding them. That’s a powerful argument for having AI and machine learning ingrained into CX company-wide. The following graphic quantifies the economic value of improving CX:
Where AI Is Improving CX
For AI projects to make it through the budgeting crucible that the COVID-19 pandemic has created, they’re going to have to show a contribution to revenue, cost reduction, and improved customer experiences in a contactless world. Add in the need for any CX strategy to be on a resilient, proven platform and the future of marketing comes into focus. Examples of platforms and customer-centric digital transformation networks that can help re-center an organization on data- and AI-driven customer insights include BMC’s Autonomous Digital Enterprise (ADE) and others. The framework is differentiated from many others in how it is designed to capitalize on AI and Machine Learning’s core strengths to improve every aspect of the customer (CX) and employee experience (EX). BMC believes that providing employees with the digital resources they need to excel at their jobs also delivers excellent customer experiences.
Having worked my way through college in customer service roles, I can attest to how valuable having the right digital resources are for serving customers What I like about their framework is how they’re trying to go beyond just satisfying customers, they’re wanting to delight them. BMC calls this delivering a transcendent customer experience. From my collegiate career doing customer service, I recall the e-mails delighted customers sent to my bosses that would be posted along a wall in our offices. In customer service and customer experience, you get what you give. Having customer service reps like my younger self on the front line able to get resources and support they need to deliver more authentic and responsive support is key. I see BMC’s ADE doing the same by ensuring a scalable CX strategy that retains its authenticity even as response times shrink and customer volume increases.
The following are six ways AI can improve customer experiences:
Improving contactless personalized customer care is considered one of the most valuable areas where AI is improving customer experiences. These “need to do” marketing areas have the highest complexity and highest benefit. Marketers haven’t been putting as much emphasis on the “must do” areas of high benefit and low complexity, according to Capgemini’s analysis. These application areas include Chatbots and virtual assistants, reducing revenue churn, facial recognition and product and services recommendations. Source: Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. (PDF, 28 pp).
Anticipating and predicting how each customers’ preferences of where, when, and what they will buy will change and removing roadblocks well ahead of time for them. Reducing the friction customers face when they’re attempting to buy within a channel they’ve never purchased through before can’t be left to chance. Using augmented, predictive analytics to generate insights in real-time to customize the marketing mix for every individual Customer improves sales funnels, preserves margins, and can increase sales velocity.
Knowing which customer touchpoints are the most and least effective in improving CX and driving repurchase rates. Successfully using AI to improve CX needs to be based on data from all trackable channels that prospects and customers interact with. Digital touchpoints, including mobile app usage, social media, and website visits, all need to be aggregated into data sets ML algorithms to use to learn more about every Customer continually and anticipate which touchpoint is the most valuable to them and why. Knowing how touchpoints stack up from a customer’s point of view immediately says which channels are doing well and which need improvement.
Recruiting new customer segments by using CX improvements to gain them as prospects and then convert them to customers. AI and ML have been used for customer segmentation for years. Online retailers are using AI to identify which CX enhancements on their mobile apps, websites, and customer care systems are the most likely to attract new customers.
Retailers are combining personalization, AI-based pattern matching, and product-based recommendation engines in their mobile apps enabling shoppers to try on garments they’re interested in buying virtually. Machine learning excels at pattern recognition, and AI is well-suited for fine-tuning recommendation engines, which are together leading to a new generation of shopping apps where customers can virtually try on any garment. The app learns what shoppers most prefer and also evaluates image quality in real-time, and then recommends either purchase online or in a store. Source: Capgemini, Building The Retail Superstar: How unleashing AI across functions offers a multi-billion dollar opportunity.
Relying on AI to best understand customers and redefine IT and Operations Management infrastructure to support them is a true test of how customer-centric a business is. Digital transformation networks need to support every touchpoint of the customer experience. They must have AI and ML designed to anticipate customer needs and deliver the goods and services required at the right time, via the Customer’s preferred channel. BMC’s Autonomous Digital Enterprise Framework is a case in point. Source: Cognizant, The 2020 Customer Experience.