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Intelligent process automation: The engine at the core of the next-generation operating model McKinsey 201703
Intelligent process automation: The engine at the core of the next-generation operating model By Federico Berruti, Graeme Nixon, Giambattista Taglioni, and Rob Whiteman Article Actions Share this article on LinkedIn Share this article on Twitter Share this article on Facebook Email this article Print this article Download this article Full intelligent process automation comprises five key technologies. Here’s how to use them to enhance productivity and efficiency, reduce operational risks, and improve customer experiences. Since the financial crisis of 2007–09, many companies have applied lean management to improve cost efficiencies, customer satisfaction, and employee engagement simultaneously, and many programs have achieved substantial impact on all dimensions. Progress on digital, however, has been more uneven. In the insurance sector, for example, an October 2016 FIS study found that 99.6 percent of insurers surveyed admitted they face obstacles in implementing digital innovation, while 80 percent recognize they need digital capabilities to meet business challenges. This difficulty has been compounded by the boom in “insurtech” investments in 2016—topping $3.5 billion in funding across 111 deals since 2015. As macroeconomic conditions continue to put pressure on profit margins across sectors, cost productivity and unlocking new value are back at the top of the senior-management agenda. The question is, what else can be done? That’s where intelligent process automation (IPA) comes in. We believe it will be a core part of companies’ next-generation operating models. Many companies across industries have been experimenting with IPA, with impressive results: Automation of 50 to 70 percent of tasks . . . . . . which has translated into 20 to 35 percent annual run-rate cost efficiencies . . . . . . and a reduction in straight-through process time of 50 to 60 percent . . . . . . with return on investments most often in triple-digit percentages. New technologies that promise double-digit or even triple-digit same-year returns should rightfully be viewed with skepticism. But our experience shows that the promise of IPA is real if executives carefully consider and understand the drivers of opportunity and incorporate them effectively with the other approaches and capabilities that drive the next-generation operating model. (For more on these approaches and capabilities, please read “The next-generation operating model for the digital world.”) What is intelligent p
#automation  #rpa  #ia  #innovation  #enterprise  #advice  @McKinsey 
4 days ago by phil_hendrix
Ready, Set, Fail - Avoiding setbacks in the intelligent automation race KPMG 2018
Ready, Set, Fail?: Avoiding setbacks in the intelligent automation race New study reveals most organizations’ low readiness to deploy artificial intelligence technologies
Executives have high expectations for the impact of intelligent automation, but they're not yet ready to implement it from the top down and at scale. They'll struggle to get adequate ROI until they recognize two critical issues: 1) intelligent automation investment decisions need to be C-level strategy imperatives, 2) intelligent automation is about business and operating model transformation not simply technology deployment.

It's not clear whether most companies understand that intelligent automation is about changing business processes, and then restructuring the organization around those new processes now driven by technologies that didn't exist before. This means shifting the business and operating model from one of people supported by technology to one of technology supported by people. It's a digital-first operating model.

KPMG recently undertook a study to understand the reasons for and implications of deploying IA and what it takes to scale. KPMG professionals interviewed executives from numerous industries and geographies worldwide about their experiences with deployment and their perspectives on the future. Most emphasized that IA is poised to digitally transform their companies and industries and profoundly impact their employees' roles.

At the same time, executives highlighted several challenges. In addition to grappling with the extraordinary pace of change, they are faced with understanding and choosing among hundreds of technology options, the need for effective data and analytics, prioritizing automation focus, and defining their future workforce. KPMG research considered three main areas of intelligent automation -- basic or robotic process automation (RPA), enhanced automation and cognitive automation.
#ai  #ia  #status  #outlook  #strategy  #advice  #recommendations  @KPMG  #2018 
5 days ago by phil_hendrix
Artificial intelligence that improves job performance Fast Company 20181024
This artificial intelligence won’t take your job, it will help you do it better AI-powered tools are everywhere. The challenge lies in deploying them so they actually do some good. [Photo: wutwhanfoto/iStock] BY GWEN MORAN4 MINUTE READ Artificial intelligence (AI) and machine learning are increasingly powering workplace platforms and tools. The sophisticated automation tools have been widely promoted as relieving workers from tasks that are “dirty, dull, or dangerous,” unleashing them to do higher-level work and create. PwC research estimates that AI will contribute $15.7 trillion to the global economy by 2030, driven primarily by productivity gains and AI-fueled product innovation. In various categories, it’s beginning to deliver on its promise. Financial services companies are using such technologies in ways that range from chatbots that answer basic customer questions to AI-powered platforms that help prevent fraud and money laundering. Human resources (HR) applications help companies sort through resumes, find talent, and even conduct initial interviews. It can be used for maintenance alerts and prevent equipment and vehicle failure in automotive fleets. Purchasing algorithms can help sort through data to make better procurement decisions. In healthcare, promising applications range from robotic surgery to diagnoses of various conditions to AI-powered preauthorizations and other medical certifications.
#ai  #automation  #jobs  #work  #advice  #A+ 
11 days ago by phil_hendrix
What We Often Get Wrong About Automation HBR 20181011
What We Often Get Wrong About Automation Ravin Jesuthasan John Boudreau OCTOBER 11, 2018 When leaders describe how advances in automation will affect job prospects for humans, predictions typically fall into one of two camps. Optimists say that machines will free human workers to do higher-value, more creative work. Pessimists predict massive unemployment, or, if they have a flair for the dramatic, a doomsday scenario in which humans’ only job is to serve our robot overlords. What almost everyone gets wrong is focusing exclusively on the idea of automation “replacing” humans. Simply asking which humans will be replaced fails to account for how work and automation will evolve. Our new book, Reinventing Jobs: A 4-Step Approach for Applying Automation to Work, argues that while automation can sometimes substitute for human work, it also more importantly has the potential to create new, more valuable, and more fulfilling roles for humans.
#ai  #automation  #impact  #work  #jobs  #strategy  #advice  #framework  @RavinJesuthasan  @JohnBoudreau 
11 days ago by phil_hendrix
Reinventing Jobs: A 4-Step Approach for Applying Automation to Work, Ravin Jesuthasan, John Boudreau, eBook - Amazon.com
Reinventing Jobs: A 4-Step Approach for Applying Automation to Work Kindle Edition by Ravin Jesuthasan (Author), John Boudreau (Author)
#ai  #automation  #jobs  #work  #tasks  #impact  #advice  #framework  #book  #tl 
12 days ago by phil_hendrix
The cornerstones of large-scale technology transformation McKinsey 201810
The cornerstones of large-scale technology transformation By Michael Bender, Nicolaus Henke, and Eric Lamarre
A clear playbook is emerging for how to integrate and capitalize on advanced technologies—across an entire company, and in any industry. How does your company use advanced technologies to create value? This has become the defining business challenge of our time. If you ignore it or get it wrong, then anything from your job to your entire organization could become vulnerable to rivals who get it right. The new technologies come with many labels—digital, analytics, automation, the Internet of Things, industrial internet, Industry 4.0, machine learning, artificial intelligence (AI), and so on. For incumbent companies, they support the creation of all-new, digitally enabled business models, while holding out the vital promise of improving customer experiences and boosting the productivity of legacy operations. Advanced technologies are essential to modern enterprises, and it’s fair to say that every large company is working with them to some extent. 34:16 Audio The cornerstones of large-scale technology transformation In private discussions over the past year, we’ve asked more than 500 CEOs whether they think technology can improve business growth and productivity sufficiently to lift profits and shareholder value by 30 to 50 percent; a great many have said yes. So far, though, that prize has remained elusive for a lot of companies. Consider, for example, McKinsey research highlighting the large number of digital laggards, and the wide gap between them and leaders: digitally reinvented incumbents—those using digital to compete in new ways, and those making digital moves into new industries—are twice as likely as their traditional peers to experience exceptional financial growth.
#technology  #digital  #analytics  #strategy  #deployment  #advice  @McKinsey  #A+ 
13 days ago by phil_hendrix
The cornerstones of large-scale technology transformation McKinsey 201810
The cornerstones of large-scale technology transformation By Michael Bender, Nicolaus Henke, and Eric Lamarre
A clear playbook is emerging for how to integrate and capitalize on advanced technologies—across an entire company, and in any industry. How does your company use advanced technologies to create value? This has become the defining business challenge of our time. If you ignore it or get it wrong, then anything from your job to your entire organization could become vulnerable to rivals who get it right. The new technologies come with many labels—digital, analytics, automation, the Internet of Things, industrial internet, Industry 4.0, machine learning, artificial intelligence (AI), and so on. For incumbent companies, they support the creation of all-new, digitally enabled business models, while holding out the vital promise of improving customer experiences and boosting the productivity of legacy operations. Advanced technologies are essential to modern enterprises, and it’s fair to say that every large company is working with them to some extent. 34:16 Audio The cornerstones of large-scale technology transformation In private discussions over the past year, we’ve asked more than 500 CEOs whether they think technology can improve business growth and productivity sufficiently to lift profits and shareholder value by 30 to 50 percent; a great many have said yes. So far, though, that prize has remained elusive for a lot of companies. Consider, for example, McKinsey research highlighting the large number of digital laggards, and the wide gap between them and leaders: digitally reinvented incumbents—those using digital to compete in new ways, and those making digital moves into new industries—are twice as likely as their traditional peers to experience exceptional financial growth.
#technology  #digital  #analytics  #strategy  #deployment  #advice  @McKinsey  #A+ 
13 days ago by phil_hendrix
How to Use Overlays to Increase Conversions CloudIQ
HOW TO USE OVERLAYS TO INCREASE CONVERSIONS You've already downloaded the guide, so why not broaden your knowledge and watch this webinar. In it you'll discover three of the most effective ways of using overlays to increase conversions - whether you're looking to increase subscribers, downloads, encourage repeat visits or drive more sales, these little wonders can assist you.
#ecommerce  #startup  #advice  +NautaCapital 
15 days ago by phil_hendrix
Vertical Beats Horizontal in Machine Learning Zetta Venture Partners 2016
Vertical Beats Horizontal in Machine Learning Signup for our newsletter here The best products in the world are made by vertically integrated businesses: Apple’s hardware to software; Amazon’s warehouses to websites; and Carnegie’s mines to mills [1]. Zetta is completely focused on investing in data and machine learning startups. We see lots of horizontal platforms and APIs that anyone can use to add some machine learning models to their application. However, machine learning has advanced to the point where customers expect better than commodity performance. We like to see startups vertically integrating their technical skills with the skills of domain experts and unique data acquisition to build applications with the level of accuracy required in commercial and industrial settings. This article will describe the state of machine learning, focusing on the importance of domain expertise in feature development and labeling data when building high accuracy models. We will then explore ways in which startups can get the requisite domain expertise and labeled data to build such models. Finally, we will consider some of the challenges in working with customers to develop software based on such models. We won’t teach anyone in the field anything (we recommend that ML practitioners skip the next section) but do hope that you benefit from our perspective having seen thousands of startups and talked to hundreds of customers, and understand a little more about how we think.
#ml  #advice  #data  #applications  #A+ 
28 days ago by phil_hendrix
3 Areas To Help Corporate Leaders Excel In AI Transformation 20181101
3 Areas To Help Corporate Leaders Excel In AI Transformation Dr. Andreas Liebl Brand Contributor NVIDIA BRANDVOICE UNTERNEHMERTUM/KUCEVIC Artificial intelligence has now left the “geek” corner and is no longer a purely technical topic. With more available data, greater computing power at a lower price, and by enabling technologies such as IoT, AI has emerged to become a disruptive force in recent years. A fundamental shift in how we build programs, use data, derive value, and shape business models is taking place. At the heart of this technology is the idea of automating complex areas of knowledge work, using machines where we traditionally couldn't due to the lack of “intelligent” capabilities such as vision, audio, language, and affection. The interest in this new field can be easily grasped by analyzing the importance of the correlated Google search terms. Artificial Intelligence, Machine Learning, and Data Science are together rising to become equally popular as Computer Science.
#ai  #advice  #applications  #enterprise  #innovation  #strategy 
4 weeks ago by phil_hendrix
The AI mindset: preparing people is as important as preparing data Jessica Groopman 20180420
The AI mindset: preparing people is as important as preparing data As companies work to understand the techniques and tools used for simulating cognitive functions in machines, they often overlook a critical aspect of preparedness

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Businesses of every size, stripe, and sector are struggling to prepare for artificial intelligence. As companies work to understand the techniques and tools used for simulating cognitive functions in machines, they often overlook a critical aspect of preparedness.

Preparing people is as important as preparing data
While data is the requisite asset, people are the ones building, measuring, consuming, and determining the success of AI in enterprise and consumer settings. People are also the ones whose jobs will change; whose tedium will become automated; who will consume or reject the outcomes of AI; and who will feel its myriad impacts. This means that AI readiness requires businesses invest in the cultural and mental constructs of AI across leadership, employees, even users.

Prepare for the AI mindset
AI isn’t just about data scientists and machine learning algorithms; as we deploy software and machines to handle more and more cognitive tasks, it is about rethinking how we do things and why. This, ironically, requires a level of introspection on the part of the individuals designing, building, and using the technology, as well as the broader organization’s ultimate goals for the technology. Regardless of whether employees are engineers, agents, executives, or field staff, the idea is planting the seed of a new mindset, an AI mindset.
#ai  #deployment  #barriers  #advice  #people  #endusers  @JessicaGroopman  #A+ 
4 weeks ago by phil_hendrix
Five Ways to Prepare for AI in Sales, Marketing and Beyond Jessica Groopman 20181024
Five Ways to Prepare for AI in Sales, Marketing and Beyond Nathan Decker, Director of eCommerce, evo Oct 24 2018 | 60 mins Enterprise preparation for AI has centered almost exclusively on data prep and data science talent. While without data there would be no AI, enterprises that fail to ready the broader organization, chiefly people, process, and principles, don’t just stunt their capacity for good AI, they risk sunk investment, jeopardize employee trust, brand backlash, or worse. Ensuring sustainable deployment starts with assessing enterprise data strategy, aligning myriad stakeholders, technological feasibility assessment, and a coordinated approach to ethics. Join VentureBeat and industry analyst and founding partner of Kaleido Insights, Jessica Groopman for discussion on the five fundamentals of AI readiness at our upcoming VB Live event! Attend this webinar and learn: * What you need to do to prepare for AI-- beyond the data science team * Real-world examples and research findings * Top 5 best practices for strategic AI implementation Speakers: * Nathan Decker, Director of eCommerce, evo * Ken Natori, President, Natori Company * Jessica Groopman, Industry analyst and founding partner of Kaleido Insights * Rachael Brownell, Moderator, VentureBeat
#marketing  #ai  #advice  #integration  #deployment  #impact  @JessicaGroopman  #webinar 
4 weeks ago by phil_hendrix
Content - The Atomic Particle of Marketing The Definitive Guide to Content Marketing Strategy Rebecca Lieb 20170628
Content - The Atomic Particle of Marketing: The Definitive Guide to Content Marketing Strategy Paperback – June 28, 2017 by Rebecca Lieb (Author), Jaimy Szymanski (Assistant)
#marketing  #content  #tl  #book  #advice  #strategy 
4 weeks ago by phil_hendrix
Untangling the Gordian Knot: The HFS Dummies’ Guide to Enterprise AI HFS Research 20181105
UNTANGLING THE GORDIAN KNOT: THE HFS DUMMIES’ GUIDE TO ENTERPRISE AI Nov 5, 2018 Reetika Fleming Maria Terekhova AI is one of the most hyped, yet truly most valuable, technologies available to enterprises as they look to optimize and future-proof their businesses. Any form of AI is powered by algorithms, formulaic code that instructs the software how to process and learn from input data. These algorithms can only learn by ingesting vast quantities of data and then deriving patterns from it. Algorithms use these patterns and implicit instructions to interpret future data, which can be internal company data or external market data.   AI’s value and the excitement it inspires lie in its ability to process data in far greater volumes, at far greater speed, and with the promise of infinitely better accuracy, than humans can. By design, AI doesn’t just mimic human analytical capabilities; it outstrips them. As such, organizations are actively looking at AI to drive cost savings by cutting down on FTEs, to better leverage their internal and external market data to generate deeper competitive insights, and to equip themselves to become predictive rather than reactive businesses. Multiple studies show that AI is already better than humans at certain tasks, for example predicting turnover rates and profiling customers. Further testimony to AI’s power are the funding pouring into the sector and the rate at which buyers are snapping up AI start-ups.   There’s a fundamental issue stopping many enterprises from unlocking AI’s full potential. People tend to discuss AI as if it is a monolith or single category, when in fact there are many specialized AI subcategories for specific tasks. For example, machine learning (ML) doesn’t have the semantic understanding of a sophisticated natural language processing (NLP) system, and even the best NLP system wouldn’t be well equipped to identify images with the precision of a computer vision engine. If these distinctions are elided—as they so often are—then enterprises won’t find the AI technology best suited to address their unique needs.   Download this insight to learn the definitions and capabilities of different types of AI
#ai  #enterprise  #deployment  #advice  #explainer  @HfS  #A+  #report 
4 weeks ago by phil_hendrix
Stanford Distinguished Careers Institute
The Stanford Distinguished Careers Institute offers people in midlife with major career accomplishments the opportunity to renew their purpose, develop new communities and recalibrate wellness, and to transform themselves for new roles with social impact.
#professional  #development  #career  #advice  #training  #courses  @Stanford  +GlennLangdon 
4 weeks ago by phil_hendrix
Why Applied Machine Learning Is Hard Jason Brownlee 20171222
Why Applied Machine Learning Is Hard by Jason Brownlee on December 22, 2017 in Machine Learning Process How to Handle the Intractability of Applied Machine Learning. Applied machine learning is challenging. You must make many decisions where there is no known “right answer” for your specific problem, such as: What framing of the problem to use? What input and output data to use? What learning algorithm to use? What algorithm configuration to use? This is challenging for beginners that expect that you can calculate or be told what data to use or how to best configure an algorithm. In this post, you will discover the intractable nature of designing learning systems and how to deal with it. After reading this post, you will know: How to develop a clear definition of your learning problem for yourself and others. The 4 decision points you must consider when designing a learning system for your problem. The 3 strategies that you can use to specifically address the intractable problem of designing learning systems in practice. Let’s get started.
#ml  #tutorial  #advice  #bestpractices  #explainer  @JasonBrownlee  #A+ 
7 weeks ago by phil_hendrix

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