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.
🔹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.
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
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.
Chat GPT 4 can pass the American state bar exam, but before you go expecting to see robot lawyers taking over the courtroom, hold your horses cowboys – we're not quite there yet. That being said, AI is becoming increasingly more human-like, and as a VC we need to start thinking about how this new wave of technology is going to affect the way we build and run businesses. What do we need to do differently? How can we make sure that our investment strategies are reflecting these changes? It's a brave new world out there, and we’ve got to keep the big picture in mind!
Sharing here with you what we at Cavalry Ventures found out during our Generative AI deep dive.
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
🔹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.
Exploring Opportunities in the Generative AI Value Chain.pdfDung Hoang
The article "Exploring Opportunities in the Generative AI Value Chain" by McKinsey & Company's QuantumBlack provides insights into the value created by generative artificial intelligence (AI) and its potential applications.
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.
Chat GPT 4 can pass the American state bar exam, but before you go expecting to see robot lawyers taking over the courtroom, hold your horses cowboys – we're not quite there yet. That being said, AI is becoming increasingly more human-like, and as a VC we need to start thinking about how this new wave of technology is going to affect the way we build and run businesses. What do we need to do differently? How can we make sure that our investment strategies are reflecting these changes? It's a brave new world out there, and we’ve got to keep the big picture in mind!
Sharing here with you what we at Cavalry Ventures found out during our Generative AI deep dive.
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 Use-cases for Enterprise - First SessionGene Leybzon
In this presentation, we will delve into the exciting applications of Generative AI across various business domains. Leveraging the capabilities of artificial intelligence and machine learning, Generative AI allows for dynamic, context-aware user interfaces that adapt in real-time to provide personalized user experiences. We will explore how this transformative technology can streamline design processes, facilitate user engagement, and open the doors to new forms of interactivity.
Research presentation on the impact of AI on the advertising and CX landscape.
We start with a short introduction of AI, the causes of recent focus and hype, as well as a simplified model to compartmentalise different AI models.
The presentation constructs a framework to assess the potential impact of AI against :
- the complexity of the work
- the type of work being done - analysis, decision-making, and execution.
Based on the framework, the presentation argues for four possible futures:
- Creativity at the centre
- Digitalization of marketing
- Efficiency of marketing
- Impact of marketing
Furthermore, the presentation lists dangers and limitations inherent in the technology, as well as how agencies or individuals can get started to navigate the unknown future.
At its conclusion, it's argued that AI will likely have a substantial impact on the advertising and marketing industry. The agency business model is already under strain and will need to quickly adapt in light of significant threats posed by continued advancements in automation and generative AI models.
A Framework for Navigating Generative Artificial Intelligence for EnterpriseRocketSource
Generative AI has dominated the headlines recently, which has caused many enterprises to put a full stop to implementing this technology until they can understand what’s behind the glitz and glamour. What if we shifted the conversation? What if the focus became a fresh, incremental approach to embracing the opportunities with generative artificial intelligence to keep organizations moving upward on the S Curve of Growth?
Brands stay relevant and solve complex problems by testing the barometer for one thing — will a new strategy, tool, or piece of technology improve humanity?
Human connections are more vital than using shiny new tools or technology. As your teams work to steer clear of the temptation to do what everyone else is doing in uniform, this post will highlight how to stand out, compete, and do so with less risk in today’s world of generative AI overload.
How can we use generative AI in learning products? A rapid introduction to generative AI. Presented at ED Games Expo 2023 at the U.S. Department of Education, September 22, 2023.
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.
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
Thabo Ndlela- Leveraging AI for enhanced Customer Service and Experienceitnewsafrica
Thabo Ndlela, from Accenture, delivered a keynote on Leveraging AI for enhanced Customer Service and Experience at Digital Finance Africa 2023 on the 2nd of August 2023.
An overview of the most important AI capabilities in marketing, advertising and content creation. I made this presentation to inform, educate and inspire people in the creative industries to familiarise themselves with the incredible toolsets that are already here and in development. I also explain how generative Ai works explore some possible new roles and business models for agencies. Hope you enjoy it!
This session was presented at the AWS Community Day in Munich (September 2023). It's for builders that heard the buzz about Generative AI but can’t quite grok it yet. Useful if you are eager to connect the dots on the Generative AI terminology and get a fast start for you to explore further and navigate the space. This session is largely product agnostic and meant to give you the fundamentals to get started.
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
Let's talk about GPT: A crash course in Generative AI for researchersSteven Van Vaerenbergh
This talk delves into the extraordinary capabilities of the emerging technology of generative AI, outlining its recent history and emphasizing its growing influence on scientific endeavors. Through a series of practical examples tailored for researchers, we will explore the transformative influence of these powerful tools on scientific tasks such as writing, coding, data wrangling and literature review.
Generative AI art has a lot of issues:
Lack of Control: Generative AI art eliminates digital artists' control over their work. The results are unpredictable and often unsatisfactory, leaving artists feeling frustrated.
No Unique Signature: Generative AI art lacks a unique signature or style, making it difficult for digital artists to stand out.
Quality Control Issues: Generative AI art can be of poor quality and unsuitable for professional use. Digital artists who rely on their work to make a living may find that AI-generated work is not up to their standards.
Decreased Job Opportunities: As generative AI art becomes more popular, the demand for human digital artists may decrease, leading to fewer job opportunities.
No Emotional Connection: Generative AI art lacks the emotional connection artists can create through their work. This can make it difficult for digital artists to connect with their audience and make a lasting impact.
Limited Creative Potential: Generative AI art has limited creative potential based on algorithms and pre-defined parameters. Digital artists who seek to express their creativity and individuality may find it limiting.
Intellectual Property Concerns: Generative AI art can infringe on the intellectual property of others, leading to legal issues for the artist.
Lack of Personal Touch: Generative AI art lacks the personal touch that digital artists can bring to their work. This can result in a lack of emotion, connection, and engagement with the audience.
Decreased Income: Generative AI art is often available for free or at a low cost, making it difficult for digital artists to make a living through their work.
Loss of Craftsmanship: Generative AI art relies on technology, taking away the element of craftsmanship and hand-drawn skills that digital artists have honed over time.
AI, or artificial intelligence, is powering a massive shift in how engineers, scientists, and programmers develop and improve products and services. 85% of executives expect to gain or strengthen their competitive advantage through the use of AI, but is AI really poised to transform your research, products, or business?
Learn how AI system can be designed to perceive its environment, make decisions, and take action. Get an overview of AI for engineers, and discover the ways in which it fits into an engineering workflow. You will also learn how MATLAB and Simulink® are giving engineers and scientists AI capabilities that were once available only to highly-specialized software developers and Data Scientists.
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
This book presents and exploration of the impact and potential of generative AI in the business landscape. This compelling read takes readers on a journey through the world of generative AI, explaining its fundamental concepts, and showcasing its transformative power when applied in an enterprise setting.
The book delves into the technical aspects of generative AI, explaining its workings in an accessible way. It sheds light on how these models analyze large volumes of data to generate insights, identify trends, conduct sentiment analysis, and extract relevant information from unstructured data.
It also addresses the challenges and considerations when implementing generative AI, including ethical concerns, data privacy, and the need for custom fine-tuning to align with company values and norms. It provides practical guidance on how to overcome these challenges, ensuring a successful AI transformation in the enterprise.
"Unleashing Innovation: Exploring Generative AI in the Enterprise" is a must-read for business leaders, IT professionals, and anyone interested in understanding the revolutionary potential of generative AI in the business world.
Explore how different industries are embracing the utility of AI to create and deliver new value for their customers and organisation
* Discuss the state of maturity of AI across industries
* Get an appreciation of business posture to AI projects
We also review the utility of AI across several industries including:
* Healthcare
* Newsroom & Journalism
* Travel
* Finance
* Supply Chain / eCommerce / Retail
* Streaming & Gaming
* Transportation
* Logistics
* Manufacturing
* Agriculture
* Defense & Cybersecurity
Part of the What Matters in AI series as published on www.andremuscat.com
The numbers tell the story: 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale. With the stakes higher than ever, what can we learn from companies that are successfully scaling AI, achieving nearly 3X the return on investments and an average 32% premium on key financial valuation metrics?
To answer that question, Accenture conducted a landmark global study involving 1,500 C-suite executives from organizations across 16 industries. The aim: Help companies progress on their AI journey, from one-off AI experimentation to gaining a robust organization-wide capability that acts as a source of competitive agility and growth.
Read the full report:
http://www.accenture.com/AI-Built-to-Scale-Slideshare
Senior leaders are feeling the push from shareholders to continue driving their organizations forward, but is AI the answer? Just because AI is front-page news, is it right for your organization? Are the implications truly understood? These questions and more are crucial for leaders as the AI evolution continues to shape the next wave of work. Clearly, AI will profoundly transform our lives in the years ahead. Finding the balance between opportunity and implications is key to our success as well as to our future.
This overview by Whynde Kuehn and Mike Clark explores these opportunities and implications, discusses how business design can be a crucial guide for AI, and provides key recommendations for moving into action.
Generative AI Use-cases for Enterprise - First SessionGene Leybzon
In this presentation, we will delve into the exciting applications of Generative AI across various business domains. Leveraging the capabilities of artificial intelligence and machine learning, Generative AI allows for dynamic, context-aware user interfaces that adapt in real-time to provide personalized user experiences. We will explore how this transformative technology can streamline design processes, facilitate user engagement, and open the doors to new forms of interactivity.
Research presentation on the impact of AI on the advertising and CX landscape.
We start with a short introduction of AI, the causes of recent focus and hype, as well as a simplified model to compartmentalise different AI models.
The presentation constructs a framework to assess the potential impact of AI against :
- the complexity of the work
- the type of work being done - analysis, decision-making, and execution.
Based on the framework, the presentation argues for four possible futures:
- Creativity at the centre
- Digitalization of marketing
- Efficiency of marketing
- Impact of marketing
Furthermore, the presentation lists dangers and limitations inherent in the technology, as well as how agencies or individuals can get started to navigate the unknown future.
At its conclusion, it's argued that AI will likely have a substantial impact on the advertising and marketing industry. The agency business model is already under strain and will need to quickly adapt in light of significant threats posed by continued advancements in automation and generative AI models.
A Framework for Navigating Generative Artificial Intelligence for EnterpriseRocketSource
Generative AI has dominated the headlines recently, which has caused many enterprises to put a full stop to implementing this technology until they can understand what’s behind the glitz and glamour. What if we shifted the conversation? What if the focus became a fresh, incremental approach to embracing the opportunities with generative artificial intelligence to keep organizations moving upward on the S Curve of Growth?
Brands stay relevant and solve complex problems by testing the barometer for one thing — will a new strategy, tool, or piece of technology improve humanity?
Human connections are more vital than using shiny new tools or technology. As your teams work to steer clear of the temptation to do what everyone else is doing in uniform, this post will highlight how to stand out, compete, and do so with less risk in today’s world of generative AI overload.
How can we use generative AI in learning products? A rapid introduction to generative AI. Presented at ED Games Expo 2023 at the U.S. Department of Education, September 22, 2023.
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.
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
Thabo Ndlela- Leveraging AI for enhanced Customer Service and Experienceitnewsafrica
Thabo Ndlela, from Accenture, delivered a keynote on Leveraging AI for enhanced Customer Service and Experience at Digital Finance Africa 2023 on the 2nd of August 2023.
An overview of the most important AI capabilities in marketing, advertising and content creation. I made this presentation to inform, educate and inspire people in the creative industries to familiarise themselves with the incredible toolsets that are already here and in development. I also explain how generative Ai works explore some possible new roles and business models for agencies. Hope you enjoy it!
This session was presented at the AWS Community Day in Munich (September 2023). It's for builders that heard the buzz about Generative AI but can’t quite grok it yet. Useful if you are eager to connect the dots on the Generative AI terminology and get a fast start for you to explore further and navigate the space. This session is largely product agnostic and meant to give you the fundamentals to get started.
The Future of AI is Generative not Discriminative 5/26/2021Steve Omohundro
The deep learning AI revolution has been sweeping the world for a decade now. Deep neural nets are routinely used for tasks like translation, fraud detection, and image classification. PwC estimates that they will create $15.7 trillion/year of value by 2030. But most current networks are "discriminative" in that they directly map inputs to predictions. This type of model requires lots of training examples, doesn't generalize well outside of its training set, creates inscrutable representations, is subject to adversarial examples, and makes knowledge transfer difficult. People, in contrast, can learn from just a few examples, generalize far beyond their experience, and can easily transfer and reuse knowledge. In recent years, new kinds of "generative" AI models have begun to exhibit these desirable human characteristics. They represent the causal generative processes by which the data is created and can be compositional, compact, and directly interpretable. Generative AI systems that assist people can model their needs and desires and interact with empathy. Their adaptability to changing circumstances will likely be required by rapidly changing AI-driven business and social systems. Generative AI will be the engine of future AI innovation.
Let's talk about GPT: A crash course in Generative AI for researchersSteven Van Vaerenbergh
This talk delves into the extraordinary capabilities of the emerging technology of generative AI, outlining its recent history and emphasizing its growing influence on scientific endeavors. Through a series of practical examples tailored for researchers, we will explore the transformative influence of these powerful tools on scientific tasks such as writing, coding, data wrangling and literature review.
Generative AI art has a lot of issues:
Lack of Control: Generative AI art eliminates digital artists' control over their work. The results are unpredictable and often unsatisfactory, leaving artists feeling frustrated.
No Unique Signature: Generative AI art lacks a unique signature or style, making it difficult for digital artists to stand out.
Quality Control Issues: Generative AI art can be of poor quality and unsuitable for professional use. Digital artists who rely on their work to make a living may find that AI-generated work is not up to their standards.
Decreased Job Opportunities: As generative AI art becomes more popular, the demand for human digital artists may decrease, leading to fewer job opportunities.
No Emotional Connection: Generative AI art lacks the emotional connection artists can create through their work. This can make it difficult for digital artists to connect with their audience and make a lasting impact.
Limited Creative Potential: Generative AI art has limited creative potential based on algorithms and pre-defined parameters. Digital artists who seek to express their creativity and individuality may find it limiting.
Intellectual Property Concerns: Generative AI art can infringe on the intellectual property of others, leading to legal issues for the artist.
Lack of Personal Touch: Generative AI art lacks the personal touch that digital artists can bring to their work. This can result in a lack of emotion, connection, and engagement with the audience.
Decreased Income: Generative AI art is often available for free or at a low cost, making it difficult for digital artists to make a living through their work.
Loss of Craftsmanship: Generative AI art relies on technology, taking away the element of craftsmanship and hand-drawn skills that digital artists have honed over time.
AI, or artificial intelligence, is powering a massive shift in how engineers, scientists, and programmers develop and improve products and services. 85% of executives expect to gain or strengthen their competitive advantage through the use of AI, but is AI really poised to transform your research, products, or business?
Learn how AI system can be designed to perceive its environment, make decisions, and take action. Get an overview of AI for engineers, and discover the ways in which it fits into an engineering workflow. You will also learn how MATLAB and Simulink® are giving engineers and scientists AI capabilities that were once available only to highly-specialized software developers and Data Scientists.
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
This book presents and exploration of the impact and potential of generative AI in the business landscape. This compelling read takes readers on a journey through the world of generative AI, explaining its fundamental concepts, and showcasing its transformative power when applied in an enterprise setting.
The book delves into the technical aspects of generative AI, explaining its workings in an accessible way. It sheds light on how these models analyze large volumes of data to generate insights, identify trends, conduct sentiment analysis, and extract relevant information from unstructured data.
It also addresses the challenges and considerations when implementing generative AI, including ethical concerns, data privacy, and the need for custom fine-tuning to align with company values and norms. It provides practical guidance on how to overcome these challenges, ensuring a successful AI transformation in the enterprise.
"Unleashing Innovation: Exploring Generative AI in the Enterprise" is a must-read for business leaders, IT professionals, and anyone interested in understanding the revolutionary potential of generative AI in the business world.
Explore how different industries are embracing the utility of AI to create and deliver new value for their customers and organisation
* Discuss the state of maturity of AI across industries
* Get an appreciation of business posture to AI projects
We also review the utility of AI across several industries including:
* Healthcare
* Newsroom & Journalism
* Travel
* Finance
* Supply Chain / eCommerce / Retail
* Streaming & Gaming
* Transportation
* Logistics
* Manufacturing
* Agriculture
* Defense & Cybersecurity
Part of the What Matters in AI series as published on www.andremuscat.com
The numbers tell the story: 84% of C-suite executives believe they must leverage artificial intelligence (AI) to achieve their growth objectives, yet 76% report they struggle with how to scale. With the stakes higher than ever, what can we learn from companies that are successfully scaling AI, achieving nearly 3X the return on investments and an average 32% premium on key financial valuation metrics?
To answer that question, Accenture conducted a landmark global study involving 1,500 C-suite executives from organizations across 16 industries. The aim: Help companies progress on their AI journey, from one-off AI experimentation to gaining a robust organization-wide capability that acts as a source of competitive agility and growth.
Read the full report:
http://www.accenture.com/AI-Built-to-Scale-Slideshare
Senior leaders are feeling the push from shareholders to continue driving their organizations forward, but is AI the answer? Just because AI is front-page news, is it right for your organization? Are the implications truly understood? These questions and more are crucial for leaders as the AI evolution continues to shape the next wave of work. Clearly, AI will profoundly transform our lives in the years ahead. Finding the balance between opportunity and implications is key to our success as well as to our future.
This overview by Whynde Kuehn and Mike Clark explores these opportunities and implications, discusses how business design can be a crucial guide for AI, and provides key recommendations for moving into action.
Building Data Science into Organizations: Field ExperienceDatabricks
We will share our experiences in building Data Science and Machine Learning (DS/ML) into organizations. As new DS/ML teams are created, many wrestle with questions such as: How can we most efficiently achieve short-term goals while planning for scale and production long-term? How should DS/ML be incorporated into a company?
We will bring unique perspectives: one as a previous Databricks customer leading a DS team, one as the second ML engineer at Databricks, and both as current Solutions Architects guiding customers through their DS/ML journeys.We will cover best practices through the crawl-walk-run journey of DS/ML: how to immediately become more productive with an initial team, how to scale and move towards production when needed, and how to integrate effectively with the broader organization.
This talk is meant for technical leaders who are building new DS/ML teams or helping to spread DS/ML practices across their organizations. Technology discussion will focus on Databricks, but the lessons apply to any tech platforms in this space.
LoQutus helps organisations to innovate with analytics and to get insights with data visualisation. We also build large scale data layers to enable interaction with core data, and develop data-driven applications to deliver the insights our customers need. During this session we’ll share what we have learned along the way. We’ll show you our framework for self-service analytics & insights, and some successful case studies.
Translating AI from Concept to Reality: Five Keys to Implementing AI for Know...Enterprise Knowledge
Lulit Tesfaye explains how foundational knowledge management and knowledge engineering approaches can play a key role in ensuring enterprise Artificial Intelligence (AI) initiatives start right, quickly demonstrate business value, and “stick” within the organization. The presentation includes real world case studies and examples of how organizations are approaching their data and AI transformations through knowledge maturity models to translate organizational information and data into actionable and clickable solutions. Originally delivered at data.world Summit, Spring 2022.
At Axtria, we provide world-class training, support and growth prospects - all crafter to build on your unique skills and outline your success. You will be in a highly collaborative culture among a bunch of the most talented and visionary folks in the industry.
An AI Maturity Roadmap for Becoming a Data-Driven OrganizationDavid Solomon
The initial version of a maturity roadmap to help guide businesses when adopting AI technology into their workflow. IBM Watson Studio is referenced as an example of technology that can help in accelerating the adoption process.
Resume vivek mohan - Data & Analytics Chief ArchitectVivek Mohan
17 Years of Experience Summary - Vivek Mohan - Data and Analytics, Digital Transformation, Business Intelligence, Data Visualization, Digital Marketing, Campaign management, Customer Journey analytics, Artificial Intelligence, IOT, Big Data etc..
Northern New England Tableau User Group (TUG) May 2024patrickdtherriault
Join us live in Portland or over the wire for networking and two fantastic presentations! Data viz freelancer Desireé Abbott will demonstrate how adding interactivity to your dashboards will delight and spark curiosity in your users. Then, Charlotte Taft & Laurie Rugemer will reprise their TC24 presentation on the keys to building a successful analytics team.
Northern New England TUG May 2024 - Abbott, Taft, Rugemerpatrickdtherriault
Join us live in Portland or over the wire for networking and two fantastic presentations! Data viz freelancer Desireé Abbott will demonstrate how adding interactivity to your dashboards will delight and spark curiosity in your users. Then, Charlotte Taft & Laurie Rugemer will reprise their TC24 presentation on the keys to building a successful analytics team.
Big data jobs are taking the highest rankings in the job market. Learn how you can excel in big data job roles as analysts, scientists, or engineers here.
40 ° advises and supports companies and institutions to generate real added value from data and to generate data-driven innovations and new business models. We help to reinvent your business with data. 40 ° is the expert for data driven business transformation
How the Analytics Translator can make your organisation more AI drivenSteven Nooijen
Today, about 80% of companies considers data as an essential part of their strategy. However, although most of these companies are taking models into production, they still have trouble turning their data and insights into valuable AI solutions. With businesses heavily invested in data and AI, what is it that actually makes the difference for being successful with AI?
In this talk, I will argue that the extent to which AI is embedded in the organisation is crucial to success. Furthermore, I will show why the Analytics Translator is the designated person to drive AI adoption by the business and what his or her tasks should look like. The insights shared come from our own experience as consultants as well as interviews with top Dutch enterprises about their AI maturity.
AI Maturity Levels and the Analytics TranslatorGoDataDriven
Buzzwords like Big Data, Cloud, and AI have been out there now for a couple of years. But today, businesses have a clear focus on the application of data use cases and the challenges around that such as metadata management, governance, security, and maintainability in general. Everybody seems to have some version of a data lake and wants to consolidate it into something (more) useful, or move from an on-premise version to the cloud. There is a general need to streamline current practices while also attempting to give multiple segments of users (data scientists, analysts, marketeers, business people, and HR) access in a way that is tailored to their needs and skills. In other words: businesses today are heavily invested in data and AI, but many have a hard time knowing how to mature it to the next level.
This is exactly where a "maturity model" comes into play. The goal of a maturity model is to help businesses in understanding their current and target competencies. This helps organisations in defining a roadmap for improving their competency. A maturity model is therefore one way of structuring progression, whether the company already embraces data science as a core competency, or, if it is just getting started.
In this presentation on maturity models, we answer the following questions:
1. What exactly is a maturity model and why would you need it? We address this by sharing GoDataDriven's maturity model and describing the different phases we have identified based on our experience in the field.
2. How can you use a maturity model to advance your organisation? Having a maturity model alone is not enough, in order for it to be valuable you need to act upon it. This paper provides concrete examples on how to do act based on practical stories and experiences from our clients and ourselves.
Change does not happen in isolation –
it impacts the whole organization around it,
and all the people touched by it
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• Four (4) workplace discipline methods you should consider
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2. 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.
FROM TO[ ] [ ]
Interdisciplinary Collaboration
VISION
Siloed Working Culture
Agile, Experimental and
Adaptable
Rigid and Risk-Averse
Data-driven decision
making at frontline
Experience based leader-
driven decisions
3. HUMAN LED AI STRATEGY
Determ
ine
business outcom
es
Tap
into
talent
D
efine
the
roadm
ap
Explore
the
artofpossible
+
Human Centred
DATA
Models and
Algorithms
Technology
Talent
AI Capabilities
=
Better
Products
Better
Services
Efficient
Process
Gain a Competitive
Advantage
4. • Do you want to use pattern recognition
to monitor the energy efficiency of your
manufacturing plant?
• Is your goal to improve the functionality
and reduce downtime of your
equipment?
• To improve quality management
processes?
• To identify areas where waste could be
reduced?
Align corporate strategy with your objectives
Organisations should pick AI use cases which solve their big business challenges. They should move from thinking how to
improved customer segmentation, to how to optimise the entire customer journey. It’s important to align your corporate
strategy with measurable goals and objectives to guide your AI deployment.
They should know WHY they are doing WHAT they are doing?
5. The Governing Coalition
Make investments into new capabilities
SPOK
E
SPOK
E
SPOK
E
SPOK
E
SPOK
E
Cross Functional
Team
SPOK
E
Cross Functional
Team
HUB
HUB: Aligns strategy, operating model and
execution framework necessary for achieving
business-wide AI adoption
SPOKE: A business unit, function or
geography which oversees the execution of
delivery teams.
GRAY AREA: Work that can be owned by
HUB or SPOKE or can be shared with IT.
6. Make investments into new capabilities
Cross Functional Team
A
B C
D F
G
E
A. Data Scientist: Creates data structures suitable for analysis and runs advanced-analytics
models to generate insights and predict future event
B. IT Specialist: Manages the
technical aspects of
automation projects and
technology landscape
E. AI Consultant: Helps to decide what to build and what is feasible and valuable
F. Analytics Translator: With
deep domain expertise,
identifies digital opportunities
and facilitates interface
between data scientists in
iterating model.
C. Product Owners: Provides
business input to development
and later owns the use cases.
D. Data Engineers: Manages
data infrastructure (eg: data
lake), ensuring robustness of
pipeline and building new
features.
G. Digital Change Lead:
Shapes improvement and
organises resources and
requirements to deliver
business impacts.
New Capabilities
Multiple roles can be fulfilled by one
person.
7. Choose the right problems
TEAM SIZE
BUSINESS SPONSORSHIP
PROJECT DURATION
HIGH
LOW
HIGH
LOWHIGH
LOW
LOW
HIGH Lighthouse Project
Small lighthouse projects which can be
delivered within 10 weeks and have a large
impact on business success.
These provide an immediate and tangible
benefit for the business and customers.
These small wins are then multiplied to sow
the seeds of transformation that act as a
beacon for the capabilities.
8. EXECUTING 1st POC
FRAME PREPARE ANALYZE INTERPRET COMMUNICATE
Develop a
hypothesis-driven
approach to the
analysis
Select, import, explore,
and clean the data
Structure, visualise and complete
the analysis
Make recommendations and business
decisions from the data
Present insights from the data audience
• Identify problem
statement
• Identify business
objectives
• Quantify business goals
• Technical diligence
• Identify & Hypothesise
Goals and Criteria for
success.
• Identify data sources and
owners
• Engage with Data SME
• Collect data
• Classify data
• Define and structure data
• Clean data
• Sampling the data
• Appropriately address
missing values.
• Identify trends and
outliers
• Decide how to deal with
outliers
• Document and capture
knowledge
• Select model
• Design, build and test
the model
• Evaluate and refine the
model
• Predict outcomes and
actions.
• Engage SME to reach
conclusion
• Develop
recommendations based
on predictions
• Take decisions
• Present insights from the data
to different audience
Pick the right use case for Lighthouse Project
10. START SMALL SCALE LATER
BusinessConfidence
Value generation
Business Hackathons
Workshops
Pipeline Development
Business Awareness
Provide training
Identify AI/ML use case 360 Data Story
Define MVPUnderstand the business
problem
Experimentation
Test & Learn
Present the insights
Measure
the success
Set up AI CoE
Develop ways of working
Define standard and frameworks
Define governance and operating model
Implement new ways of working
Track adoptionFacilitate adoption
Provide incentive for changeAlign goals of cross-functional
teams
Communicate
Increasing
adoption
Organising
for scale
Execute
POC
Business
Led
Initiative
11. Continuous Journey
Iterative model for Enterprise scale
1
2
3
4 5
ALIGN
PLAN
PROVE
SCALE
MILESTONE 1
• AI Foundation
• Light House
initiative
2
3
4
MILESTONE 2
• AI Community of
practice
• Wider initiatives
Lighthouse Project
IMPROVE
2
MILESTONE 3
• Churning AI projects
at scale
North
Star
AI First Organisation
TIME
VALUE