This research report from technology research firm, Kaleido Insights introduces a framework for organizational preparedness—not only of data and infrastructure, but of people, ethical, strategic and practical considerations needed to deploy effective and sustainable machine and deep learning programs. This research is the first to market to articulate the need for readiness beyond data and data science talent. Based on extensive research and interviews of more than 25 businesses involved in AI deployments, the report identifies and examines five fundamental areas businesses must prepare for sustainable AI. Download the full report: https://www.kaleidoinsights.com/order-reports/artificial-intelligence-ai-readiness/
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
Explore the risks and concerns surrounding generative AI in this informative SlideShare presentation. Delve into the key areas of concern, including bias, misinformation, job loss, privacy, control, overreliance, unintended consequences, and environmental impact. Gain valuable insights and examples that highlight the potential challenges associated with generative AI. Discover the importance of responsible use and the need for ethical considerations to navigate the complex landscape of this transformative technology. Expand your understanding of generative AI risks and concerns with this engaging SlideShare presentation.
A journey into the business world of artificial intelligence. Explore at a high-level ongoing business experiments in creating new value.
* Review AI as a priority for value generation
* Explore ongoing experimentation
* Touch on how businesses are monetising AI
* Understand the intent of adoption by industries
* Discuss on the state of customer trust in AI
Part 1 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com
Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.
In this presentation you’ll learn about:
- Why on-device AI is key
- Full-stack AI optimizations to make on-device AI possible and efficient
- Advanced techniques like quantization, distillation, and speculative decoding
- How generative AI models can be run on device and examples of some running now
- Qualcomm Technologies’ role in scaling on-device generative AI
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
Leveraging Generative AI & Best practicesDianaGray10
In this event we will cover:
- What is Generative AI and how it is being for future of work.
- Best practices for developing and deploying generative AI based models in productions.
- Future of Generative AI, how generative AI is expected to evolve in the coming years.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
Explore the risks and concerns surrounding generative AI in this informative SlideShare presentation. Delve into the key areas of concern, including bias, misinformation, job loss, privacy, control, overreliance, unintended consequences, and environmental impact. Gain valuable insights and examples that highlight the potential challenges associated with generative AI. Discover the importance of responsible use and the need for ethical considerations to navigate the complex landscape of this transformative technology. Expand your understanding of generative AI risks and concerns with this engaging SlideShare presentation.
A journey into the business world of artificial intelligence. Explore at a high-level ongoing business experiments in creating new value.
* Review AI as a priority for value generation
* Explore ongoing experimentation
* Touch on how businesses are monetising AI
* Understand the intent of adoption by industries
* Discuss on the state of customer trust in AI
Part 1 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
The State of Global AI Adoption in 2023InData Labs
In our inaugural report, 2023 State of AI, we examine trends in AI adoption across industries, the current state of the market, and technologies that shape the field.
The goal of this report is to help company leaders and executives get a better handle on the AI landscape and the opportunities it brings for the business.
2023 State of AI report will help you to answer questions such as:
-How are organizations applying artificial intelligence in the real world in 2023?
-What industries are leading in terms of AI maturity?
-How has generative AI impacted businesses?
-How can organizations prepare for AI transformation?
Download your free copy now and adopt the key technologies to improve your business.
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
Conversational AI and Chatbot IntegrationsCristina Vidu
Conversational AI and Chatbots (or rather - and more extensively - Virtual Agents) offer great benefits, especially in combination with technologies like RPA or IDP. Corneliu Niculite (Presales Director - EMEA @DRUID AI) and Roman Tobler (CEO @Routinuum & UiPath MVP) are discussing Conversational AI and why Virtual Agents play a significant role in modern ways of working. Moreover, Corneliu will be displaying how to build a Workflow and showcase an Accounts Payable Use Case, integrating DRUID and UiPath Robots.
📙 Agenda:
The focus of our meetup is around the following areas - with a lot of room to discuss and share experiences:
- What is "Conversational AI" and why do we need Chatbots (Virtual Agents);
- Deep-Dive to a DRUID-UiPath Integration via an Accounts Payable Use Case;
- Discussion, Q&A
Speakers:
👨🏻💻 Corneliu Niculite, Presales Director - EMEA DRUID AI
👨🏼💻 Roman Tobler, UiPath MVP, Co-Founder & CEO Routinuum GmbH
This session streamed live on March 8, 2023, 16:00 PM CET.
Check out our upcoming events at: community.uipath.com
Contact us at: community@uipath.com
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders and senior management can understand and support, and help create the right conditions for delivering successful ML-based solutions to your business.
Solve for X with AI: a VC view of the Machine Learning & AI landscapeEd Fernandez
What you'll get from this deck
1. The M&A race for AI: by the numbers
2. Watch out! hype ahead: definitions & disclaimers
3. Machine Learning drivers: why is Machine Learning a ‘thing’ now (vs before)
4. Venture Capital: forming an industry, the AI/ML landscape
5. The One Hundred (+13) AI startups to watch in the Enterprise
6. The great Enterprise pivot: applying Machine Learning at scale
7. - where to go next -
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It was presented at the joint session of ONE and the Digital Council. It covers some of the key trends and developments in AI including operationalizing AI, responsible AI and National AI Strategies
Artificial intelligence is reshaping business, and the time is ripe for companies to capitalise AI. The organisation can use AI to move their focus from discrete business problems to significant business challenges.
An organisation should use ML and Data Science to drive digital transformation for more back-office operational efficiency, better user/engagement, smoother onboarding, and better ROI by lowering cost and bring more data-driven taking mechanism for transparency.
AI will be a valuable, transformational change agent not only to the way business is done but to the way people live their daily lives if it isn't perceived as a plug-and-play technology with immediate returns but more like a long term solution to rewire the organisation.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
* "Responsible AI Leadership: A Global Summit on Generative AI"
*April 2023 guide for experts and policymakers
* Developing and governing generative AI systems
* + 100 thought leaders and practitioners participated
* Recommendations for responsible development, open innovation & social progress
* 30 action-oriented recommendations aim
* Navigate AI complexities
🔹How will AI-based content-generating tools change your mission and products?
🔹This complimentary webinar [ON-DEMAND] explores multiple use cases that drive adoption in their early adopter customer base to provide product leaders with insights into the future of generative AI-powered businesses, and the potential generative AI holds for driving innovation and improving business processes.
The State of Global AI Adoption in 2023InData Labs
In our inaugural report, 2023 State of AI, we examine trends in AI adoption across industries, the current state of the market, and technologies that shape the field.
The goal of this report is to help company leaders and executives get a better handle on the AI landscape and the opportunities it brings for the business.
2023 State of AI report will help you to answer questions such as:
-How are organizations applying artificial intelligence in the real world in 2023?
-What industries are leading in terms of AI maturity?
-How has generative AI impacted businesses?
-How can organizations prepare for AI transformation?
Download your free copy now and adopt the key technologies to improve your business.
Today, I will be presenting on the topic of
"Generative AI, responsible innovation, and the law."
Artificial Intelligence has been making rapid strides in recent years,
and its applications are becoming increasingly diverse.
Generative AI, in particular, has emerged as a promising area of innovation, the potential to create highly realistic and compelling outputs.
Conversational AI and Chatbot IntegrationsCristina Vidu
Conversational AI and Chatbots (or rather - and more extensively - Virtual Agents) offer great benefits, especially in combination with technologies like RPA or IDP. Corneliu Niculite (Presales Director - EMEA @DRUID AI) and Roman Tobler (CEO @Routinuum & UiPath MVP) are discussing Conversational AI and why Virtual Agents play a significant role in modern ways of working. Moreover, Corneliu will be displaying how to build a Workflow and showcase an Accounts Payable Use Case, integrating DRUID and UiPath Robots.
📙 Agenda:
The focus of our meetup is around the following areas - with a lot of room to discuss and share experiences:
- What is "Conversational AI" and why do we need Chatbots (Virtual Agents);
- Deep-Dive to a DRUID-UiPath Integration via an Accounts Payable Use Case;
- Discussion, Q&A
Speakers:
👨🏻💻 Corneliu Niculite, Presales Director - EMEA DRUID AI
👨🏼💻 Roman Tobler, UiPath MVP, Co-Founder & CEO Routinuum GmbH
This session streamed live on March 8, 2023, 16:00 PM CET.
Check out our upcoming events at: community.uipath.com
Contact us at: community@uipath.com
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders and senior management can understand and support, and help create the right conditions for delivering successful ML-based solutions to your business.
Solve for X with AI: a VC view of the Machine Learning & AI landscapeEd Fernandez
What you'll get from this deck
1. The M&A race for AI: by the numbers
2. Watch out! hype ahead: definitions & disclaimers
3. Machine Learning drivers: why is Machine Learning a ‘thing’ now (vs before)
4. Venture Capital: forming an industry, the AI/ML landscape
5. The One Hundred (+13) AI startups to watch in the Enterprise
6. The great Enterprise pivot: applying Machine Learning at scale
7. - where to go next -
This presentation was given at the OECD Network of AI Specialists (ONE) held in Paris on February 26 and 27. It was presented at the joint session of ONE and the Digital Council. It covers some of the key trends and developments in AI including operationalizing AI, responsible AI and National AI Strategies
Artificial intelligence is reshaping business, and the time is ripe for companies to capitalise AI. The organisation can use AI to move their focus from discrete business problems to significant business challenges.
An organisation should use ML and Data Science to drive digital transformation for more back-office operational efficiency, better user/engagement, smoother onboarding, and better ROI by lowering cost and bring more data-driven taking mechanism for transparency.
AI will be a valuable, transformational change agent not only to the way business is done but to the way people live their daily lives if it isn't perceived as a plug-and-play technology with immediate returns but more like a long term solution to rewire the organisation.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(This updated version builds on our previous deck: slideshare.net/LoicMerckel/intro-to-llms.)
In this session, you'll get all the answers about how ChatGPT and other GPT-X models can be applied to your current or future project. First, we'll put in order all the terms – OpenAI, GPT-3, ChatGPT, Codex, Dall-E, etc., and explain why Microsoft and Azure are often mentioned in this context. Then, we'll go through the main capabilities of the Azure OpenAI and respective usecases that might inspire you to either optimize your product or build a completely new one.
AI & DEI: With Great Opportunities Comes Great HR ResponsibilityAggregage
https://www.humanresourcestoday.com/frs/26184029/ai---dei--with-great-opportunities-comes-great-hr-responsibility
The promise of AI for today’s organizations is real, yet in a frenzied state of experimentation, many stumble to get to a full-scale enterprise. As companies race to discover what generative AI can do, HR must lead conversations about how to balance cutting-edge innovations with integrity, trust, and diversity. Globally, organizations are at a critical intersection of Diversity, Equity, Inclusion, and AI acceleration. We will explore how AI is rapidly transforming workplace dynamics and decision-making processes. The safety and protection of the workforce have never been more important and need to be co-led by HR to prevent biases and ensure fair and equitable representation in systems, hiring, and the workforce evolution.
We'll cover:
• The opportunities that AI presents and the responsibility of HR
• How to enhance diverse perspectives in use cases
• Increasing collaboration between AI Developers, HR, Legal and IT
AI for enterprises Redefining industry standards.pdfChristopherTHyatt
"AI for Enterprises revolutionizes business landscapes, offering unparalleled efficiency, data-driven decision-making, and personalized customer experiences. From automation to advanced analytics, this transformative technology empowers organizations to streamline operations, enhance productivity, and stay ahead in the competitive digital era. Embrace the future of business with AI for Enterprises and unlock a realm of innovation, strategic insights, and sustainable growth."
Get Ready: AI Is Grown Up and Ready for BusinessCognizant
Despite great enthusiasm for AI, full-blown deployments remain the exception rather than the rule across businesses in the U.S. and Europe, according to our recent research. Businesses can turn the tide by honing their AI strategies, maintaining a human-centric approach, developing governance structures and ensuring AI applications are built on an ethical foundation.
Artificial Intelligence: Competitive Edge for Business Solutions & Applications9 series
The growth of Artificial Intelligence in recent years brought forth a major challenge for brands in deploying such AI solutions. Many brands lack the clarity regarding where to start the AI integration process and profitably deploy these solutions in the most effective manner.
Incorporating artificial intelligence into your business systems and processes is a journey unlike any other digital technology implementation. Here is a five-step process for navigating it successfully.
Companies need to complement their AI initiatives with governance that drives ethics and trust or these efforts will fall short of expectations, our latest research findings suggest.
Investing in AI: Moving Along the Digital Maturity CurveCognizant
Digitally mature businesses are more likely to consider themselves at an advanced stage of AI adoption, according to our recent study, enabling them to turn data into insights at the scale and precision required today.
How Companies Can Move AI from Labs to the Business CoreCognizant
APAC and Middle East organisations have big expectations from AI, but they’re only just getting started. To realise the full potential of AI-led innovation, they must rapidly, but smartly, scale their deployments and embrace a strong ethical foundation, keeping a close eye on the human implications and cultural changes required to convert machine intelligence from lofty concept to business reality.
In a world where smart machines augment human work, board members and C-suite
executives need a working understanding of AI before they define how they will
implement it successfully within their organizations. Fingent provides technology
solutions that ensure that the technical implementation and strategic adoption of AI
builds toward their long-term goals.
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Goodbuzz Inc.
Driving Tangible Value for Business. Briefing Paper. Interest in AI/ML is soaring, but confusion and hype can mask the real benefits of these technologies. Organizations need to identify use cases that will produce value for them, especially in the areas of enhancing processes, detecting anomalies and enabling predictive analytics.
This AI business checklist is a tool that provides an easy-to-use structure for strategic discussions, goal setting and critical decisions in your leadership team. A structure that you can use as a business leader to guide your decisions towards getting full value out of AI technology in your organisation. It is meant to be a tool that you can return to to guide your progress.
AI for RoI - How to choose the right AI solution?Abhinav Singhal
Companies looking to adopt AI today are bombarded
with technology companies and start-ups selling advanced
machine learning based solutions built on exciting use
cases. However, before kickstarting newer pilots and
investing in these advanced solutions it is useful to step
back and reflect on the overall intent of using AI for
the organization and the traditional suite of analytical
techniques and resources available.Oneway, CIOs can assess
the suitability of an AI solution is it to break it down into
simpler elements and ask five basic questions.
AI solutions are the most important component of the digital transformation of many companies. AI Startups are racing ahead to address the needs of industries. In this paper, we present the broad strategies AI startups can employ to be successful.
Similar to AI Readiness: Five Areas Business Must Prepare for Success in Artificial Intelligence (20)
Skye Residences | Extended Stay Residences Near Toronto Airportmarketingjdass
Experience unparalleled EXTENDED STAY and comfort at Skye Residences located just minutes from Toronto Airport. Discover sophisticated accommodations tailored for discerning travelers.
Website Link :
https://skyeresidences.com/
https://skyeresidences.com/about-us/
https://skyeresidences.com/gallery/
https://skyeresidences.com/rooms/
https://skyeresidences.com/near-by-attractions/
https://skyeresidences.com/commute/
https://skyeresidences.com/contact/
https://skyeresidences.com/queen-suite-with-sofa-bed/
https://skyeresidences.com/queen-suite-with-sofa-bed-and-balcony/
https://skyeresidences.com/queen-suite-with-sofa-bed-accessible/
https://skyeresidences.com/2-bedroom-deluxe-queen-suite-with-sofa-bed/
https://skyeresidences.com/2-bedroom-deluxe-king-queen-suite-with-sofa-bed/
https://skyeresidences.com/2-bedroom-deluxe-queen-suite-with-sofa-bed-accessible/
#Skye Residences Etobicoke, #Skye Residences Near Toronto Airport, #Skye Residences Toronto, #Skye Hotel Toronto, #Skye Hotel Near Toronto Airport, #Hotel Near Toronto Airport, #Near Toronto Airport Accommodation, #Suites Near Toronto Airport, #Etobicoke Suites Near Airport, #Hotel Near Toronto Pearson International Airport, #Toronto Airport Suite Rentals, #Pearson Airport Hotel Suites
Unveiling the Secrets How Does Generative AI Work.pdfSam H
At its core, generative artificial intelligence relies on the concept of generative models, which serve as engines that churn out entirely new data resembling their training data. It is like a sculptor who has studied so many forms found in nature and then uses this knowledge to create sculptures from his imagination that have never been seen before anywhere else. If taken to cyberspace, gans work almost the same way.
Cracking the Workplace Discipline Code Main.pptxWorkforce Group
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
Putting the SPARK into Virtual Training.pptxCynthia Clay
This 60-minute webinar, sponsored by Adobe, was delivered for the Training Mag Network. It explored the five elements of SPARK: Storytelling, Purpose, Action, Relationships, and Kudos. Knowing how to tell a well-structured story is key to building long-term memory. Stating a clear purpose that doesn't take away from the discovery learning process is critical. Ensuring that people move from theory to practical application is imperative. Creating strong social learning is the key to commitment and engagement. Validating and affirming participants' comments is the way to create a positive learning environment.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
As a business owner in Delaware, staying on top of your tax obligations is paramount, especially with the annual deadline for Delaware Franchise Tax looming on March 1. One such obligation is the annual Delaware Franchise Tax, which serves as a crucial requirement for maintaining your company’s legal standing within the state. While the prospect of handling tax matters may seem daunting, rest assured that the process can be straightforward with the right guidance. In this comprehensive guide, we’ll walk you through the steps of filing your Delaware Franchise Tax and provide insights to help you navigate the process effectively.
Buy Verified PayPal Account | Buy Google 5 Star Reviewsusawebmarket
Buy Verified PayPal Account
Looking to buy verified PayPal accounts? Discover 7 expert tips for safely purchasing a verified PayPal account in 2024. Ensure security and reliability for your transactions.
PayPal Services Features-
🟢 Email Access
🟢 Bank Added
🟢 Card Verified
🟢 Full SSN Provided
🟢 Phone Number Access
🟢 Driving License Copy
🟢 Fasted Delivery
Client Satisfaction is Our First priority. Our services is very appropriate to buy. We assume that the first-rate way to purchase our offerings is to order on the website. If you have any worry in our cooperation usually You can order us on Skype or Telegram.
24/7 Hours Reply/Please Contact
usawebmarketEmail: support@usawebmarket.com
Skype: usawebmarket
Telegram: @usawebmarket
WhatsApp: +1(218) 203-5951
USA WEB MARKET is the Best Verified PayPal, Payoneer, Cash App, Skrill, Neteller, Stripe Account and SEO, SMM Service provider.100%Satisfection granted.100% replacement Granted.
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
𝐓𝐉 𝐂𝐨𝐦𝐬 provides unlimited package services including such as Event organizing, Event planning, Event production, Manpower, PR marketing, Design 2D/3D, VIP protocols, Interpreter agency, etc.
Sports events - Golf competitions/billiards competitions/company sports events: dynamic and challenging
⭐ 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬:
➢ 2024 BAEKHYUN [Lonsdaleite] IN HO CHI MINH
➢ SUPER JUNIOR-L.S.S. THE SHOW : Th3ee Guys in HO CHI MINH
➢FreenBecky 1st Fan Meeting in Vietnam
➢CHILDREN ART EXHIBITION 2024: BEYOND BARRIERS
➢ WOW K-Music Festival 2023
➢ Winner [CROSS] Tour in HCM
➢ Super Show 9 in HCM with Super Junior
➢ HCMC - Gyeongsangbuk-do Culture and Tourism Festival
➢ Korean Vietnam Partnership - Fair with LG
➢ Korean President visits Samsung Electronics R&D Center
➢ Vietnam Food Expo with Lotte Wellfood
"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
The world of search engine optimization (SEO) is buzzing with discussions after Google confirmed that around 2,500 leaked internal documents related to its Search feature are indeed authentic. The revelation has sparked significant concerns within the SEO community. The leaked documents were initially reported by SEO experts Rand Fishkin and Mike King, igniting widespread analysis and discourse. For More Info:- https://news.arihantwebtech.com/search-disrupted-googles-leaked-documents-rock-the-seo-world/
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
India Orthopedic Devices Market: Unlocking Growth Secrets, Trends and Develop...Kumar Satyam
According to TechSci Research report, “India Orthopedic Devices Market -Industry Size, Share, Trends, Competition Forecast & Opportunities, 2030”, the India Orthopedic Devices Market stood at USD 1,280.54 Million in 2024 and is anticipated to grow with a CAGR of 7.84% in the forecast period, 2026-2030F. The India Orthopedic Devices Market is being driven by several factors. The most prominent ones include an increase in the elderly population, who are more prone to orthopedic conditions such as osteoporosis and arthritis. Moreover, the rise in sports injuries and road accidents are also contributing to the demand for orthopedic devices. Advances in technology and the introduction of innovative implants and prosthetics have further propelled the market growth. Additionally, government initiatives aimed at improving healthcare infrastructure and the increasing prevalence of lifestyle diseases have led to an upward trend in orthopedic surgeries, thereby fueling the market demand for these devices.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
AI Readiness: Five Areas Business Must Prepare for Success in Artificial Intelligence
1. By Jessica Groopman
Edited by Jaimy Szymanski
Includes input from 27 industry leaders
September 2018
Five Areas Businesses Must Prepare for Success in Artificial Intelligence
AI Readiness
2. 2
Executive Recommendations 3
Introduction 4
Definition of Artificial Intelligence
The Struggle for Readiness 5
The Five Areas of AI Readiness 6
Strategy 7
AI Transformation is an Extension of Digital Transformation
Strategic Approaches to AI
Lay the Foundation for AI Governance
Measuring AI’s Success
Key Questions to Ask
People 13
The AI Mindset
Identify Key Personae and Ready Each Group Accordingly
Address AI’s Limitations & Cultural Stigma
Best Practices
Key Questions to Ask
Data 22
Assess Enterprise Data Strategy
Ready the AI Data Feedback Loop
Leverage AI for Ongoing Enterprise Learning and Knowledge Management
Best Practices
Key Questions to Ask
Infrastructure 31
Assess Architecture Needs and Evaluation Criteria
Prepare Infrastructure
AI Software Solutions
AI Hardware
Best Practices
Key Questions to Ask
Ethics 39
Organization & Resources
Bias In, Bias Out
Transparency, Consent, and Data Privacy
Best Practices
Key Questions to Ask
Next Steps Towards Artificial Intelligence 48
About the Author 49
About Kaleido Insights 49
Research Methodology 50
Ecosystem Inputs 50
Acknowledgements 50
Endnotes 51
Table of Contents
3. 3
Executive Recommendations
Although this research weaves best practices and considerations throughout each respective area of
readiness, companies interviewed surfaced a number of overarching areas of advice, based on their
AI implementation experiences.
Investment in AI = Investment in People: Early successes in the space show that the sum of
human + AI is greater than either alone, thus business preparedness and investment in AI requires
proportionate preparedness and investment in people.
Start small, pilot problems, fail forward. Businesses can realize immediate incremental value with
narrow pilot efforts that are focused on solving concrete problems; increase value by learning from
mistakes.
Data and AI preparedness must align with broader data and business strategy. Starting small
doesn’t mean thinking small, instead, every AI initiative must begin by assessing data, people, and
processes to support and align with broader business objectives, impacts and governance structures.
Lockstep coordination between technical and business subject matter experts must support every
phase of AI lifecycle, from design, research, and deployment, to management and optimization.
Trust and transparency must infuse enterprise AI. From evangelizing the limitations of AI, to
demonstrating the benefits, clear communication and user support fosters adoption and curbs fears.
Systems and process documentation and explainability are critical to mitigate risks of AI’s complexity
and autonomy.
Maturity won’t be defined by any single application. Advanced AI deployments will be marked by
the ability to infuse both user-facing services and interactions with back-end or enterprise process and
supply chain optimization.
Machine learning must translate to enterprise learning. Business leaders must create a culture of
learning and adaptability, by supporting people with skills development, workflows, and infrastructure
to learn from interactions and productize domain knowledge.
4. 4
Introduction
The fundamental business question of “what do we do with all of this data?” is colliding with our endless
fascination for technological biomimicry. Artificial Intelligence (AI) is an unavoidable concept in the digital
age, if not for its pervasive application, then by virtue of the fact that it provides a technological solution to
information overload and the quest for context. Recent research finds some 80 percent of businesses plan to
invest in AI in the next three years.1
Even if the techniques and concepts underlying AI are more than 50 years old, the recent renaissance in
commercial environments is a result of a rather sudden convergence of three key trends: colossal data
generation; better algorithms; and faster processing hardware. AI is already yielding business impacts
across functions, including product development, process and workflow optimization, recommendation and
personalization at scale, risk mitigation, and cybersecurity, among scores of others. Enterprise interest in AI has
been compounded by its promises for efficiency in the form of speed, accuracy, agility, and access to insights
embedded in “dark data”—a reference to the estimated 80% of unused enterprise data.2
Meanwhile, the technology giants of the world are leading the charge, not only in the amount of data they
possess to train models, the frameworks and libraries they’re open-sourcing, or the firmware and hardware for
computing such colossal data, but in how AI-driven services are impacting consumer expectations. Every day
we hear new stories of AI used for innovation, for ill, for intrigue; new techniques emerge; and data volumes
and complexities grow.
Innovators and change agents see the hype of AI everywhere but struggle to know where to begin.
This Kaleido Insights report examines the need for AI readiness and introduces a framework for organizational
preparedness—not only of data and infrastructure, but of people and processes needed to deploy effective and
sustainable machine and deep learning programs. What follows is the culmination of extensive primary and
secondary research to surface the barriers, enablers, and best practices of leading organizations as they go
about deploying AI in business processes and services.
Definition of Artificial Intelligence
As often the case in emerging technologies, different market constituencies define terms differently. Artificial
intelligence (AI) evokes a special challenge (and emotionalism) as it is almost always compared to human
intelligence— a domain which also struggles to be defined.
Kaleido Insights defines AI as an umbrella term for the variety of tools and methods used to mimic cognitive
functions across three areas: perception/vision, speech/language; and learning/analysis. A machine’s ability to
“cognate” is supported by multiple approaches—machine learning, deep learning, natural language processing
(NLP), computer vision, and other existing and emerging techniques—multiples of which can be used at the
same time for a given use case. Kaleido Insights acknowledges discrete differences among techniques, but
for simplicity, this report will use “AI” interchangeably for applications involving machine learning and other
techniques mentioned above.
5. 5
Although the race towards AI is on, the vast majority of organizations struggle to know where to begin.
An estimated 85% of organizations want to deploy AI, but have not.3
But, the gap between aspiration and
execution is wide, not only because data is a mess; the same study found less than 30% of enterprises
surveyed had any kind of AI strategy in place. Barriers to readiness, never mind deployment, span the
following areas:
FIGURE 1 BUSINESS, MARKET,AND SOCIETAL BARRIERS TO AI ADOPTION
Beyond the significant emotional, societal, and competitive challenges lie an array of barriers to practical AI
deployment, some of which are mentioned in Figure 1. Now— in what are no doubt the most nascent days of
commercial AI deployment— is the time for organizations to grapple with the business, economic, cultural, and
technical questions AI engenders.
Our research finds the most common stumbling blocks to deployment lie in data, technology, and talent—each
of which vary based on the nature of the business. More often than not, infrastructure is lacking to execute on
data; technology barriers around processing and integration stunt deployments; and people at all levels are
resistant to change. Achieving the benefits of AI requires companies deeply understand more than its utility; it
requires preparedness across five critical areas.
The Struggle for Readiness
Kaleido Insights, “AI Readiness: Five Areas Businesses Must Prepare for Success in Artificial Intelligence” September, 2018.
6. 6
AI readiness isn’t merely a state of preparation, but a willingness and facility for implementation. This is a
particularly difficult endeavor in the context of AI, given we barely understand the implications of deploying
information systems to behave like biological systems.
FIGURE 2 THE FIVE AREAS OF AI READINESS
Today, most of enterprise discourse and activity around AI preparation is in the context of data preparedness.
Even if data is the paramount and most practical area “to ready” in order to deploy, it must be superseded with
intent, with human assessment for impact. Our research finds AI readiness results from practical assessment
across five areas: strategy, people, data, infrastructure, and ethics.
The Five Essential Areas of AI Readiness
Kaleido Insights, “AI Readiness: Five Areas Businesses Must Prepare for Success in Artificial Intelligence” September, 2018.
7. 7
For most organizations, the trouble isn’t usually how to get started with AI, but knowing where. Not only can
machine learning and other AI techniques be applied across virtually every single industry, business function,
and workflow, it is subject to immense hype, over-inflation, and promise. From chatbots to facial recognition,
from automated reports to predictive maintenance, from procurement management to simulations for
decision-making, AI’s applications are vast. But while identifying use cases may intrigue, the starting point for
any sustainable strategy is never the technology. The starting point begins with identifying the problems and
objectives and setting goals.
AI transformation is an extension of digital transformation
In corporate environments, objectives tend to start with high-level mission statements and parse into specific
departmental, functional, product, team, and individual objectives in support. How can the organization
improve its product experience, its employee retention, lead generation, its partner channels, or accelerate
R&D? In this way, AI becomes an extension, not a bolt-on to existing digital transformation efforts.
This distinction is an important one, as companies risk following the siren hype of “building an AI strategy”
rather than evaluating how existing programs could function better and letting problems guide solutions.
While, obviously, there would be no AI without data, many companies struggle to understand the differences
and similarities.
1: Strategy
INTENDED AUDIENCE READINESS ELEMENTS
• Executives • Digital Transformation vs. AI Transformation
• Innovation Strategists • Strategic Approaches
• AI Leaders • Foundations for Governance
• Measuring Early Success
8. 8
FIGURE 3 BRIDGING DIGITALTRANSFORMATION AND AI TRANSFORMATION
To date, digital transformation has been marked by digitization of information—a sort of “phase zero” for
bringing an organization’s programs and processes online. Born of the age of social media, and (often)
driven by marketing, digital transformation has evolved from external (customer-facing) programs to internal
and cross-functional. AI, on the other hand, is driven entirely by data. It is born of analytics, increasingly a
discipline within every department. Digital transformation has brought about a proliferation of points systems;
AI transformation benefits from systems integration. In both cases, people and culture aren’t just essential for
adoption, they are essential for activating insights and customer success in the digital age. At the end of the
day, thinking strategically about AI becomes synonymous with thinking strategically about data.
Strategic approaches to AI
Companies tend to design strategies based on current capabilities around data, where they sit in the value
chain, who is designing the strategy, and what practical limitations exist. Depending on who is driving the
program—business or technical leader—starting points can vary. Business leaders often look for areas of process
automation, visibility, or cost savings, while those more technically minded tend to first think bandwidth and
access to training data. Virtually every company we interviewed emphasized organizational structure as an
essential enabler of success—for short-term wins and over time.
Common Points of Origin for Enterprise AI
Bottom-up: Single spokes or business functions select a few workflows to which applying machine
learning will yield immediate benefits, then assess broader applications as early initiatives grow.
Top-down: Executive-driven efforts to transform the entire business into a fully automated hub in
which all interactions inform optimization and greater automation across every function, and related
products or workflows.
Cross-functional: Dedicated groups set up specifically to foster digital transformation efforts,
efficiencies, shared tools, and activate data and emerging technologies to enhance business
objectives are another point of origin for AI ideation and experimentation.
Kaleido Insights, “AI Readiness: Five Essential Areas Businesses Must Prepare for Success in Artificial Intelligence” September, 2018.
9. 9
Bottom-up approaches emerge from vertical business functions and solve one problem at a time
The most common testing beds for AI projects are business functions or vertical LOBs, namely their data
analyst teams as they are most accustomed to dealing with lots of data. In some cases, these are experimental
efforts driven by internal “change agents” or data-savvy employees. In other cases, these efforts are the fruits of
mandates and investments from leadership to modernize or extract more data-driven insights, often pushing
specific business objectives (cost reductions, higher conversions, etc.). These groups have the greatest context
for where AI techniques could be applied for maximum immediate impact.
Most companies interviewed recommend a bottom-up approach to support understanding and achieve
relatively faster results. This is helpful for procuring more resources to expand efforts, as most organizations
are funded at a project level. Many companies also point to the value of starting small because there are
plenty of development resources available at low or no cost. Image recognition, captioning, natural language
processing (NLP), clustering, recommenders, and text processing are reasonable starting places for enterprises
given the variety of open source frameworks, APIs, and other industry tools and libraries available to all.4
“Start with big thinking, but work on one brick at a time,” recommends Toby Cappello, VP of worldwide
expert delivery services at IBM who recommends beginning with projects that leverage both the business
perspective with the technical development perspective. “Use the business perspective to break down big
ideas into smaller initiatives that have substantial business value; leverage the development perspective to
tool each one and aggregate learnings from the technical side.” For example, if applying AI to a help desk
environment, start by offering service agents a recommendation system; use their feedback to improve the
model, aggregate learnings, and build trust. Then, look at tackling a consumer-facing application for customer
support.
Top-down approaches are driven by central organizational hubs
Hubs are responsible for designing corporate strategies and resource allocation. They are more commonly
seen in tech companies or legacy enterprises aggressively investing in becoming a tech company. In these
early days of AI, relatively fewer AI projects are mandated through top-down approaches. Instead, corporate
hubs are the enablers for broader education, investment, governance, strategic metrics, and communications.
While broader top-down, multi-disciplinary strategies are important for longer-term product innovation and
coordination across functions, AI is unique in that tangible value can be achieved even in point solutions. “The
nice thing about this machine learning vs. other tech like enterprise resource planning systems or even IoT is
that you can pluck off a small part of a relatively low-risk business process and immediately drive efficiencies,
eliminate tedium, or improve accuracies,” shares Eric Berridge, managing partner at Bluewolf, an IBM-owned
cloud consultancy. While these efforts can prove value and indeed empower individual LOBs, they risk hitting a
proverbial value ceiling if not integrated.
Cross-functional programs are another common catalyst for enterprise AI programs
Unlike hub or spoke, the core role of these groups is to connect the two, specifically to incubate new concepts,
drive innovations and business objectives more efficiently, reduce technology and vendor redundancy, and
increase collaboration, interdepartmental workflows, etc. As AI programs evolve (from very narrow efforts in
single spokes, to more enterprise-wide learning and optimization), these liaisons are critical.
Over time, more advanced organizations work toward an integrated and shared platform that provides AI-
related services across the organization. Facebook’s FBLearner Flow, an enterprise-wide machine learning
platform for Facebook’s optimization, is in an infamous example of what this looks like in a technology platform
10. 10
company.5
In other industries, it starts with people infrastructure. Capital One has a Digital Practice which
supports all LOBs by providing dedicated expertise in data science, mobile, APIs, and beyond to collaborate
with product leads on new solutions.
Wells Fargo recently developed an Enterprise AI Solutions group, whose charter is to identify both
opportunities (for new) and integration (of existing) AI applications across the entire company. “AI was starting
to emerge all over the business and we realized that we needed a dedicated group (beyond the Innovation
Group) to optimize Wells Fargo’s use of AI,” shares David Newman, SVP and strategic planning manager for
Innovation at Wells Fargo. This group:
• Takes inventory of all AI tools used
• Identifies areas of duplication and opportunities to standardize
• Surfaces best practices for idea-sharing and faster deployments
• Develops new partnerships and integrations with its technology organization
Starting small doesn’t mean thinking small: it means laying the foundation
for AI governance
While starting with smaller initiatives can yield relatively quicker wins, that doesn’t mean they are any less
strategic. While enterprises may have some governance processes in place to support digital, even single AI
initiatives introduce new considerations around workflows, onboarding, collaboration, program design, and
ongoing management. The broader subject of AI governance will be covered in a future report, but for the
purposes of readiness, companies we interviewed repeatedly articulate the need to conduct due diligence on
both people and processes associated with any AI initiative, no matter how small.
From a people perspective, user journey analysis is important for ensuring humans are comfortable using AI
in context, and to make sure AI is the best tool for solving the problem in question. To make AI work for the
business, you have to put it the path of existing work. The program has to be deployed in such a way that
doesn’t just invite feedback, but implements it in a manner that shows people how and why this way is better.
“It’s about using the interface to surface insights employees didn’t have, or had to spend searching for before,”
shares Gregg Spratto, VP of Operations with Autodesk. Einat Haftel, VP product management at Informatica
articulates why these early steps are strategic. “Lowering the learning curve and speeding up productivity with
data widens access, and the more people that can become experts in data management, the more value to the
organization.” Informatica includes the following in their onboarding process with customers:
• Human support: sitting down directly with users to gain feedback on what is and is not important to
show; where they can easily (or not so easily) make changes, and how
• Iteration to reduce false positives; in early phases of deployment, this is essential for both model
optimization (supervised learning involving users) as well as improving trust
• Tailor UX/UI: Interfaces and dashboards, displaying or ranking confidence levels for outputs surfaced
(e.g. 78% confidence these are product SKUs)
• Make requested changes to User Interface (UI) or user experience (UX)—doesn’t mean you have to
change the core model
From a process perspective, assess how AI will impact existing governance structures such as triage, queues,
feedback loops, employees’ access to data, etc. Assessing this early on informs where breakdowns are
occurring in the process and if automation would help or hurt.
11. 11
For instance, if applying machine learning to routing particularly irate customers in a service context, you
wouldn’t want a chatbot to route them to the end of the normal queue, effectively putting them to the back of
another line.
As is the case with digital transformation efforts, top-down and ground-up efforts combined create the best
results. Initiatives that try to take a bolted-on, very technical, or altogether new approach will face greater
challenges and steeper barriers to adoption. The art here is aligning AI workflows with existing workflows and
behaviors to drive trust and adoption.
Measuring AI’s success must go beyond measuring financial impacts
It is important that early efforts yield tangible value, which naturally leads to the question of measurement.
Although small prototypes are useful for demonstrating ‘the art of the possible” to decision-makers in concrete
monetary impacts— e.g. cost reductions, lifts in conversion, pipeline optimization—companies we interviewed
reinforced the need to begin by measuring more than ROI.
“Even though we started with traditional cost metrics, we quickly realized we had to expand how we measured
value,” explains Gregg Spratto, VP of Operations with Autodesk. “Success should really be measured by the
ability to move an initiative from RD concept to deployment in production and scale in the real world.” This
means measuring the impact on people. Begin with metrics that assess value, often in the form of productivity
or sentiment, to user or function. For example:
• Time saved, spent (for agents, customers, analysts, etc.)
• Accuracy gained
• # successful outputs (e.g. cases resolved, conversions, transactions, referrals)
• Identification of fake, garbage, or fraudulent inputs (e.g. referrals, threats)
• Improved sentiment (e.g. shopping, sales, support, invoicing process)
• Improved Net Promoter Score (NPS)
• Feedback from customers or partners
• Impact on other existing metrics
Whether building or buying, businesses should beware of vendor promises involving metrics, warn some
vendors in the space. When you hear about other companies touting “97 percent improvements,” know there
are two general buckets for measuring AI model performance: General metrics such as ‘of the millions of
samples or questions asked; how many could be addressed given the ontology of the system; vs. Specific
metrics such as ‘for a specific case, e.g. customer request for an address change, 97 percent of the time AI can
resolve that one request.”
As efforts evolve and programs mature, measurement itself must too. As AI constantly learns, “lift” can be a
moving goalpost, and optimization in one area, (e.g. risk reduction) can impact another area (e.g. how to shift
from reactive risk analysis to proactive risk mitigation).
“You don’t want to automate processes that were crappy to begin
with. Good candidates for automation can sometimes be good
candidates for policy change.”
Greg Spratto, VP of Operations with Autodesk
12. 12
Regardless of application, CX is true north
Just because AI is applicable across business functions does not mean businesses should de-prioritize
customer experience (CX) within strategic planning efforts. Impact on CX—ease, security, agility, access, speed,
meaningful personalization, improvement across user journey—is the paramount assessment of value for
any AI program, B2B or B2C. “No matter which lens you’re assessing readiness— of data, of infrastructure, of
employees-— the ultimate question must always be how will this impact the end-to-end customer experience?”
shares Gianni Giacomelli, Chief Innovation Officer at Genpact. Regardless of consumer, employee, or business
partner, humans are the architects and consumers of AI in every application.
Strategy: Key Readiness Questions
How will AI initiatives support existing digital transformation efforts and data strategy?
Where will design, development, and management for AI initiatives sit in the organization?
What are adjacent use cases to which we can apply learnings, metrics, or similar techniques?
How does our internal culture facilitate and enable innovation?
How will organizational structures support governance and scale of AI deployments
and collaboration?
What is the level of awareness we have in data inventory, pipeline, and integration?
How will ongoing efforts be driven from the ground-up but empowered top-down?
13. 13
This concludes the first of five essential areas businesses must prepare for artificial
intelligence. While Strategy sets the course of an organization’s AI journey, it
is only a plan on a page without anchoring readiness across PEOPLE, DATA,
INFRASTRUCTURE, and ETHICS. Download the full research report to discover how
to prepare your organization for AI across each of these areas.
PURCHASE THE FULL RESEARCH REPORT
Report Purchase Includes:
• 54-page Research Report AI Readiness: Five Areas Businesses Must Prepare
for Success in Artificial Intelligence, including real-world examples, pragmatic
recommendations and best practices, frameworks to activate, and endnote
resources, sourced from more than 27 research interviews
• Twelve (12) high-resolution graphics and frameworks visualizing research findings
• Two (2) Downloads per purchase
• One (1) Complimentary Call with Lead Analyst, report overview discussion
To learn more about this report, Kaleido Insights, or our ongoing AI coverage:
• Visit the report page here
• Contact us directly with questions or package inquiries
• Find us at an upcoming event
• Look out for more AI coverage and research by subscribing to our newsletter
Download the Full Research Report
14. 14
RESEARCH METHODOLOGY
This research was developed through extensive primary and secondary qualitative research methods. We interviewed 27
market influencers, vendors, and adopters between September 2017 – June 2018. We also conducted countless briefings
and discussions with industry innovators in the artificial intelligence, big data, cloud and related software and hardware
markets. Input or mention in this document does not represent a complete endorsement of the report by the individuals or
the companies listed herein.
ECOSYSTEM INPUTS
• Gregg Spratto, VP Operations at Autodesk
• Ashish Bansal, Senior Director Data Science Merchant Products Lead at Capital One
• David Newman, SVP and Strategic Planning Manager for Innovation at Wells Fargo
• Jeetu Patel, Chief Product Officer at Box
• Clayton Clouse, Senior Data Scientist at FedEx
• Tatiana Mejia, Group Product Marketing Leader at Adobe
• Gianni Giacomelli, Chief Innovation Officer at GenPact
• Toby Cappello, Watson and Cloud Platform, VP WW Expert Delivery Services at IBM
• Einat Haftel, VP of Product Management at Informatica
• Jeff Kavanaugh, Senior Partner at Infosys
• Helena Carre, EMEA Omnichannel Analytics Lead, Kimberly-Clark
• Julien Sauvage, Senior Director of Product Marketing, Einstein at Salesforce.com
• Kumar Srivastava, AI Fintech Expert, Stealth-mode AI start-up
• Jay Klein, Chief Technology Officer Sahar Dolev-Blitental, Director of Marketing at Voyager Labs
• Brian Schwarz, VP Product Management at Pure Storage
• Ian Collins, CEO at Wysdom.ai
• Tom Kraljevic, VP Engineering with H20.ai
• Mary Beth Ainsworth, AI and Language Strategist at SAS
• Robin Hauser, Documentary Film-maker, and Director of “Bias”
• Eric Berridge, Managing Partner at Bluewolf
• Veena Gundavelli, CEO at Emagia
• Girish Mutreja, CEO Founder at Neeve Research
• Jem Davies, VP ARM Fellow GM of Machine Learning Rhonda Dirvin, Director of IoT and Embedded Systems at ARM
• Srikanth Velamakanni, Group Chief Executive Executive Vice-Chairman at Fractal Analytics
• Alan Anderson, Director of Enterprise Solutions at IPSoft
• Ted Shelton, Chief Customer Officer at Catalytic
• Martin Stoddard, Principal Director of Webscale Services at Accenture
With additional inputs from Lyft, Western Union, Discover, Visa, Jet.com, Uber, Kia Motors, Microsoft, and Oracle
ABOUT KALEIDO INSIGHTS
Kaleido Insights is a research and advisory firm focused on the impacts of disruptive technologies on
humans, organizations, and ecosystems. Our industry analysts provide business leaders with clarity
amidst a fragmented technology landscape. Kaleido advisory relationships, webinars, speeches, and
workshops are grounded in research rigor, impact analysis, and decades of combined expertise.
Innovators are realizing that implementing each new technology isn’t enough, especially as business
models are disrupted. Keeping up is becoming more dif cult. Our mission is to enable organizations
to decipher foresee, and act on technological disruption with agility, based on our rigorous original
research, trends analysis, events, and pragmatic recommendations.
If you’re interested in building a relationship with our analysts, we’d love to hear from you. Please
email info@kaleidoinsights.com to start a conversation, or visit www.kaleidoinsights.com to learn
more about our offerings.