Posts from the ‘BMC’s Autonomous Digital Enterporise (ADE)’ Category

Jul 27

Why Security Needs To Be Integral To DevOps

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.
70% of DevOps team members have not been trained on how to secure software adequately according to a DevSecOps Global Skills survey.

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.

Conclusion

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.

Jun 21

10 Ways Enterprises Are Getting Results From AI Strategies

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.

10 Ways Enterprises Are Getting Results From AI Strategies

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.

Additional reading:

15 examples of artificial intelligence in marketing, eConsultancy, February 28, 2019

4 Positive Effects of AI Use in Email Marketing, Statista, March 1, 2019

4 Ways Artificial Intelligence Can Improve Your Marketing (Plus 10 Provider Suggestions), Forbes, Kate Harrison, January 20, 2019

Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

Artificial Intelligence: The Ultimate Technological Disruption Ascends, Woodside Capital Partners. (PDF,

DHL Trend Research, Logistics Trend Radar, Version 2018/2019 (PDF, 55 pp., no opt-in)

2018 (43 pp., PDF, free, no opt-in).

Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in)

How To Win Tomorrow’s Car Buyers – Artificial Intelligence in Marketing & Sales, McKinsey Center for Future Mobility, McKinsey & Company. February 2019. (44 pp., PDF, free, no opt-in)

How Top Marketers Use Artificial Intelligence On-Demand Webinar with Vala Afshar, Chief Digital Evangelist, Salesforce and Meghann York, Director, Product Marketing, Salesforce

In-depth: Artificial Intelligence 2019, Statista Digital Market Outlook, February 2019 (client access reqd).

bes Insights and Quantcast Study (17 pp., PDF, free, opt-in),

Marketing & Sales Big Data, Analytics, and the Future of Marketing & Sales, (PDF, 60 pp., no opt-in), McKinsey & Company.

McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus

McKinsey & Company, Notes from the AI frontier: Modeling the impact of AI on the world economy, September 2018 By Jacques Bughin, Jeongmin Seong, James Manyika, Michael Chui, and Raoul Joshi

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.

Powerful pricing: The next frontier in apparel and fashion advanced analytics, McKinsey & Company, December 2018

Winning tomorrow’s car buyers using artificial intelligence in marketing and sales, McKinsey & Company, February 2019

World Economic Forum, Impact of the Fourth Industrial Revolution on Supply Chains (PDF, 22 pgs., no opt-in)

World Economic Forum, Supply Chain 4.0 Global Practices, and Lessons Learned for Latin America and the Caribbean (PDF, 44 pp., no opt-in)

Worldwide Spending on Artificial Intelligence Systems Will Grow to Nearly $35.8 Billion in 2019, According to New IDC Spending Guide, IDC; March 11, 2019

May 16

Six Areas Where AI Is Improving Customer Experiences

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.

Additional Resources

4 Ways to Use Machine Learning in Marketing Automation, Medium, March 30, 2017

84 percent of B2C marketing organizations are implementing or expanding AI in 2018. Infographic. Amplero.

AI, Machine Learning, and their Application for Growth, Adelyn Zhou. SlideShare/LinkedIn. Feb. 8, 2018.

AI: The Next Generation of Marketing Driving Competitive Advantage throughout the Customer Life Cycle (PDF, 10 pp., no opt-in), Forrester, February 2017.

Artificial Intelligence for Marketers 2018: Finding Value beyond the Hype, eMarketer. (PDF, 20 pp., no opt-in). October 2017

Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

Artificial Intelligence: The Ultimate Technological Disruption Ascends, Woodside Capital Partners. (PDF, 111 pp., no opt-in). January 2017.

AWS Announces Amazon Machine Learning Solutions Lab, Marketing Technology Insights

B2B Predictive Marketing Analytics Platforms: A Marketer’s Guide, (PDF, 36 pp., no opt-in) Marketing Land Research Report.

Campbell, C., Sands, S., Ferraro, C., Tsao, H. Y. J., & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business Horizons, 63(2), 227-243.

David Simchi-Levi

Earley, S. (2017). The Problem of Personalization: AI-Driven Analytics at Scale. IT Professional, 19(6), 74-80.

Four Use Cases of Machine Learning in Marketing, June 28, 2018, Martech Advisor,

Gacanin, H., & Wagner, M. (2019). Artificial intelligence paradigm for customer experience management in next-generation networks: Challenges and perspectives. IEEE Network, 33(2), 188-194.

Hildebrand, C., & Bergner, A. (2019). AI-Driven Sales Automation: Using Chatbots to Boost Sales. NIM Marketing Intelligence Review, 11(2), 36-41.

How Machine Learning Helps Sales Success (PDF, 12 pp., no opt-in) Cognizant

Inside Salesforce Einstein Artificial Intelligence A Look at Salesforce Einstein Capabilities, Use Cases and Challenges, Doug Henschen, Constellation Research, February 15, 2017

Kaczmarek, J., & Ryżko, D. (2009). Quantifying and optimising user experience: Adapting AI methodologies for Customer Experience Management.

KPMG, Customer first. Customer obsessed. Global Customer Experience Excellence report, 2019 (92 pp., PDF)

Machine Learning for Marketers (PDF, 91 pp., no opt-in) iPullRank

Machine Learning Marketing – Expert Consensus of 51 Executives and Startups, TechEmergence.

Marketing & Sales Big Data, Analytics, and the Future of Marketing & Sales, (PDF, 60 pp., no opt-in), McKinsey & Company.

OpenText, AI in customer experience improves loyalty and retention (11 pp., PDF)

Sizing the prize – What’s the real value of AI for your business and how can you capitalize? (PDF, 32 pp., no opt-in) Pw

The New Frontier of Price Optimization, MIT Technology Review. September 07, 2017.

The Power Of Customer Context, Forrester (PDF, 20 pp., no opt-in) Carlton A. Doty, April 14, 2014. Provided courtesy of Pegasystems.