Search Results

Executive Workshop: Generative AI Accelerating business value creation with AI and Generative AI Learn More Half-Day Executive Generative AI Workshop This workshop, hosted by Best Practice AI, an AI management consultancy, is designed to empower board members, executives, and managers with an in-depth understanding of AI and Generative AI (GenAI), its relevance in the contemporary business environment, and specifically to your industry and firm. It is a practical and interactive Workshop that will provide actionable insights into how to create and capture value with GenAI. Learning Objectives The core objectives of the workshop include: Gain an understanding of Generative AI (GenAI). Assess the long-term strategic implications of GenAI for your industry and company. Identify and prioritize GenAI use cases relevant to your company. Learn practical strategies for deploying GenAI in organisations. Identify a risk management framework for overseeing GenAI applications. Develop a set of questions and actions to guide organizational discussions about GenAI. Webinar Agenda What is it? 60 minutes Format: Interactive Lecture Introduction to Generative AI This introductory session is a pr esentation that will cover: ​Basic concepts of AI, Machine Learning, Generative AI, and Foundational Models. Prompt engineering for utilizing Generative AI tools. Overview of the Generative AI industry value c hain, from BigTech to application start-ups. Key applications and case studies of Generative AI across various industries. Potential challenges and risks associated with Generative AI and strategies for mitigation Critical question for your team regarding Generative AI Questions and answers (Q&A). Where to use it? 60 minutes Format: Break-Out Groups Identify use cases for your company In this session, we aim to collaboratively explore how AI can impact your company. The aim will be to generate a list of potential use cases and start the process of ranking them for potential piloting. The structure for our session is as follows: Discuss criteria for identifying potential use cases in different company roles, focusing on creative, knowledge management, conversational, and action-based tasks. In break-out groups, identify Key use cases within your business that could be used to accelerate value creation. High-level assessment of impact versus difficulty of implementation. In the wider group, present your findings and discuss them with your colleagues. What will your industry look like in 5 years? 30 minutes Format: Interactive Lecture Identify the strategic impact of GenAI We will collaboratively explore how AI could transform your industries and businesses in the long term. The session will include a presentation on the strategic impact of Generative AI, followed by a group discussion focusing on its influence on corporate strategy, productivity, and business models. ​ How to manage the risks of AI? 20 minutes Format: Interactive Lecture Discuss the risks and mitigation of GenAI The risks of AI include hallucinations, bias, privacy and security, and privacy. This session will discuss what are the actual risks and how they can be managed effectively in enterprise environment. ​ How to make it happen? 30 minutes Format: Interactive Lecture Discuss best practices in deployment In this session, we will explore the opportunities, methodologies, and challenges associated with deploying AI. Our discussion will cover the vari ous types of AI investments and strategic approaches to implementation, including an analysis of benefits, risks, and le ading best practices. We will also delve into promoting bottom-up innovation, investing in third-party tools, and developing bespoke AI solutions for your organization. ​ Next steps 10 minutes ​ Conclusion Final thoughts, questions and action points ​ Presented by Simon Greenman and Tim Gordon, Founding Partners at Best Practice AI. Tim and Simon bring many years of board, executive, and managerial operational leadership experience in digital transformations and AI in international companies. Talk To Us! Testimonials Jeremy Gubbay, COO Fox Williams I thought your workshop was excellent, pitched at the right level and practical too.” Anton Hanley, CEO at TLA. Education Chair, YPO Greater London. Best Practice AI's workshop garnered strong YPO member feedback for its well-organized, informative, and impactful workshop, highlighted by the valuable breakout sessions. Tech CEO, YPO Greater London The best event I have been to on AI. You guys really know your stuff. Frequently Asked Questions? Who should attend? Generative AI will impact all functions and organisations, so we recommend cross-functional representation. It is designed for management, executives, and board members. It is not industry specific and we have run workshops for legal, industrial, consumer, technology and many other industries. How many people and companies should attend? We have run these Workshops for teams ranging in size from as little as 8 to 100. It can be run with attendees from multiple companies, but we find that this means longer workshops to ensure a breadth of industries and companies is covered. What is the length and format of the workshop? The Workshop contains a mixture of information sessions and break-out groups. It is highly interactive and we expect attendees to engage and present thinking. The Workshops can be expanded to full day and also run in as little as two hours. I want to learn more about your Workshops. Email Us. First Name Last Name Email Message Submit Thanks for submitting!

Unleash the power of AI and Generative AI in your organisation We are a boutique AI management consultancy that provides expert and practical guidance on AI strategy, implementation and governance SEE OUR WORK Best Practice AI thinking on Generative AI Best Practice AI Board and C-Suite guidance All Videos All Videos Play Video Share Whole Channel This Video Facebook Twitter Pinterest Tumblr Copy Link Link Copied Search video... Now Playing Funding the best AI-startups globally | CogX 2020 28:18 Play Video Now Playing Boardroom AI Briefing - an overview of AI for board members. CogX2020 Festival of AI Workshop. 59:24 Play Video Now Playing Session AI for the C Suite 55:33 Play Video Why Best Practice AI? Our global network of AI practitioners is here to give you strategic, operational, ethical legal and technical advice that is both practical and expert-driven. We offer a full 360 view of AI that you can quickly put it into practice. 01 A global network of AI strategy, implementation and legal practitioners 02 The world's first Medical Chatbot Explainability Statement - Healthily 03 The world's first HR Explainability Statement - HireVue 04 Contributing to the global AI governance agenda Best Practice AI is a key part of the team on the [World Economic Forum's] Empowering AI Leadership project...We look forward to continuing to collaborate with Best Practice AI. Kay-Firth Butterfield, Head of AI, World Economic Forum Select clients and partners We work on projects ranging from Explainability Statements to AI Strategy to Project Implementation. SEE OUR WORK Healthily Explaining AI Explore our free library of case studies, toolkits, videos, and presentations to help demystify AI 600+ AI Use Cases Examine over 600 AI uses across industries and functions. ​ 1,100+ Case Studies Explore over 1,000 real-world uses of AI in organisations. ​ Videos and webinars The Best Practice AI team speaking on AI strategy, implementation and governance. AI Toolkits & Reports Educate your board, C-Suite on AI strategy, implementation and governance Decks on AI Get copies of our favorite AI presentations. ​ Blog Read about what is happening at Best Practice AI and our latest thinking. ​ Our latest thinking and work - AI Explainability Statements We are the first to publish Explainability Statements reviewed by any regulator globally, the UK's Information Commissioner's Office (ICO). ​ See the Healthily and Best Practice Explainability Statement. Learn More 5 reasons to do AI Explainability How will you explain yours? AI Explainability Statements drive teamwork Lessons from AI Explaining decisions made with AI is regulated in the UK How the Information Commissioner's Office (ICO) thinks about AI Explainability 5 reasons to do AI Explainability How will you explain yours? 1/4

Trusted partners for your AI & Generative AI journey AI Strategy | Technology | Governance Our Services Learn More! !! NEW!! Executive Generative AI Workshops Learn more! 01 AI Education & Keynote 02 AI Strategy & Planning 03 AI Technology & Delivery 04 AI Governance & Policy 05 AI Due Diligence Why Best Practice AI? 01 AI Consultancy Founded in 2018 We collaborate with Boards, C-suite, and management to formulate actionable AI strategies. Our partners have a proven track record in digital transformation across the US and Europe, and have previously held CEO positions. 03 Influence Global Responsible AI Agenda We work with global organizations, governments, and other bodies to shape the global AI agenda. For example, w e published the world's first AI Explainability Statements reviewed by regulators 02 Expert Network of Global Consultants We are part of a worldwide network of AI specialists in strategy, industry, technology, and governance. Our holistic approach to AI offers practical insights and solutions. We assemble the right experts tailored to your project requirements 04 Actionable and ROI driven AI advice We are seasoned executives and operators. The advice we give is designed to be understandable, practical, actionable and ROI driven. ​ ​ ​ ​ Meet the Partners We founded Best Practice AI based on many years of operational experience in digital transformation. We share a vision that successful innovation requires an alignment of strategy, technology, and governance. Partner Simon Greenman Simon has over 25 years in AI and digital innovation. He co-founded internet pioneer MapQuest. He served as a member of the World Economic Forum's Global AI Council focused on responsible AI deployment worldwide, and has worked on digital transformations of numerous private-equity and public businesses. He held roles at AOL, Accenture, HomeAdvisor, and more. He is active in the UK start-up environment and formerly President of the Harvard Business School Alumni Angels of the UK. He holds an MBA from Harvard and a BA in Computing and AI. He is often found running, paddle boarding, or geeking on the latest AI tech. Partner Tim Gordon Tim first deployed machine learning as a CEO a decade ago and has been working on digital transformation since he started his career at the Financial Times 25 years ago. He has since worked at the Boston Consulting Group, served on the board of private equity-backed businesses and advised the Deputy Prime Minister of the UK. He has studied at Cambridge, the College of Europe, and INSEAD. He worries about AI’s impact on misinformation (he is a Trustee at Full Fact) but is fascinated by the coming AI transformation. You can also talk to him about planting trees, walking dogs and medieval history. Thought Leadership We contribute to the global AI agenda by working with organizations globally at the intersection of AI strategy - technology - and governance. AI for the Board, C-Suite and Procurement Toolkits OpEd: How to reduce the risks from AI's original sin Webinar: who will profit from the Generative AI gold rush? Clients and Partners Strategy | Technology | Governance | Education | DD "Your Generative AI workshop was excellent, pitched at the right level and practical too. You helped make our Partner conference a great success. We have never seen such engagement from our partners before! " Jeremy Gubbay, COO Fox Williams Case Study Hirevue AI Explainability Statement Hirevue AI Explainability Statement 1/1 Workshop:Generative AI for Executives Explore We offer a range of executive webinars, workshops and presentations to help strategize, plan, and action AI and Generative AI. Example content above. Book an Appointment! Talk to us about your AI requirements now. Book Now “Best Practice AI was a key part of the team on the World Economic Forum’s Empowering AI Leadership project.” Kay Firth Butterfield, Former Head of AI and Executive Committee Member, World Economic Forum ** NEW ** ChatGPT 4-WEEK ACCELERATOR PROGRAM! 37% UP TO 37% WRITING PRODUCTIVITY IMPROVEMENT 35% 14 - 35% IMPROVEMENT IN CUSTOMER SERVICE PRODUCTIVITY 55% UP TO 35% CODING PRODUCTIVITY IMPROVEMENT 24% UP TO 24% IMPROVEMENT IN CONSULTANCY TASKS Explore Now

< back AI Case Study South32 trials first fully autonomous drone system from Airobotics for mine surveillance South32 has launched a trial using Airobotics' fully autonmous drones for surveillance at their mining site in Western Australia. The drone technology incorporates AI in order to function. Industry Basic Materials Mining And Metals Project Overview "South32 is commencing trials of an autonomous drone platform at its Worsley Alumina mine. It will be working with Israeli company Airobotics to conduct pre-programmed drone missions at the Western Australia site, to inspect equipment and map and survey the facility. The trial will be the first time the miner has used fully-automated, multi-purpose drone technology and is the first publicised commercial use of the Airobotics platform. The platform is made up of three parts: the drone, a completely automated base station from which the drone launches and lands without any human intervention, and cloud-based software, which enables users to control and manage missions. CEO Graham Kerr said the company would be increasingly focused on digitisation and technology as a means of boosting productivity." Reported Results Pilot; results not yet available Technol ogy Per Airobotics, their drones are piloted by "computer software and artificial intelligence". Function Operations Field Services Background South32 "which was spun out of BHP Billiton in 2015, has been using drones at its operations for around three years. Current applications include stockpile and pit scans, creating 3D models with point and photography data, inspecting confined spaces and infrastructure, incident investigations and environmental monitoring." Benefits ​ Data Undisclosed

< back AI Case Study The US Department of Homeland Security to enhance security at borders through facial biometric technology The United States Homeland Security awarded a contract to UK-based firm, iProov, to implement its facial recognition and biometrics system at border controls. The Customs and Border Protection (CBP) will use the technology to enhance its operation of passenger entry process. Industry Public And Social Sector Government Project Overview "iProov, a leading provider of biometric facial verification technology, has become the first British – and overseas – company to be awarded a contract from the US Department of Homeland Security Science and Technology Directorate’s Silicon Valley Innovation Program (SVIP). The contract iProov has won will see the company develop technology that aims to enhance cross-border passenger travel at unmanned ports of entries while reducing processing time. It could help US CBP quickly, accurately and reliably identify travellers as they process through US border crossings. At the heart of this announcement is iProov’s Flashmark technology, deployed in its secure, cloud-based facial biometric verification system that can be accessed via a user’s mobile phone. In this case, iProov’s technology will work with existing CBP systems, on which travellers’ details are pre-registered. In the run up to arrival at the US border, whether at home or en-route, travellers would be able to self-serve the document check that normally happens at the point of border crossing, by authenticating themselves securely to their pre-registered photo, via their mobile phone. The process is simple, much quicker than waiting in line and more secure than traditional methods. The reason iProov was selected comes down to its ability to detect ‘spoofs’. In a self-serve identity verification environment, the system must be able to confirm whether or not the person presenting themselves for verification is genuinely the owner of the ID credential, not a photo, screen image or doctored video. iProov has 10 granted patents in the UK and US for the technology that can detect these spoofs, and in this way is uniquely able to help governments tackle the pressure they are under from increased traveller numbers to swiftly but securely verify each and every traveller." Reported Results Planned; results not yet available Technol ogy ​ Function Risk Security Background "The London-based firm was founded in 2011 by CEO Andrew Bud and the company’s facial biometric technology, patented in the US and UK, is used by banks and governments around the world for secure customer onboarding, log-on and authentication, to ensure new and returning users are genuine and guard against fraudulent attempts to gain access to personal data or use a stolen identity." Benefits ​ Data ​

< back AI Use Case Predict commodity requirements Predict commodity requirements - typically to optimise procurement strategy for large industrial organisations. Function Strategy Planning Benefits Operational Support - Demand forecasting,Revenue - Improve trading decisions (eg market demand estimates) Case Studies ​ Potential Vendors ​ Industry Energy Oil And Gas Data Sets Structured / Semi-structured AI Technologies Traditional AI,Machine Learning (ML),ML Task - Prediction - Regression

< back AI Case Study 7-Eleven improved customer marketing and in-store capacity planning in Indonesia and Mexico using machine learning to predict demand variations 7-Eleven decided to use AI to optimise capacity planning and marketing. They established an information analysis environment to analyse patterns and gather valuable insights from point-of-sale data. Industry Consumer Goods And Services Retail General Project Overview 7-Eleven worked with Oracle to build a data lake and perform predictive analytics to gather insights into business processes for better-informed decisions, reduce data analysis cycle and lead time for promotional campaigns. While the strategy found success in other markets, in Indonesia it proved insufficient. They closed down operations in Indonesia as sales were impacted by external factors. Reported Results 7-Eleven claimed an enhanced level of customer service and optimized customer experience. They have since withdrawn the brand from Indonesian market while continuing operations in Mexico. Technol ogy ​ Function Marketing Marketing Research Planning Background 7-Eleven is a Japanese-owned American international chain of convenience stores, headquartered in Texas. Benefits ​ Data ​

< back AI Case Study Adore Me generates product insights by determining customer sentiment with a 92% accuracy based on natural language processing analysis of thousands of reviews Adore Me, an ecommerce lingerie retailer, analyses 1000s of customer reviews and feedback through natural language processing with a 92% classification accuracy. This helps them understand popularity of products and opinions resulting in product insights and improvements. Industry Consumer Goods And Services Retail General Project Overview "The solution uses AI informed by customer shopping data to accurately and automatically understand the natural language people use when writing a review about a product. This capability enabled us to score customer sentiment with 92% accuracy, which is incredibly helpful in uncovering topics and opinions about our products. The solution also made it possible for us to see how we stack up against companies in our industry, so that we could establish a competitive path forward." Reported Results Analyses thousands of reviews weekly and identifies customer sentiments with 92% accuracy. This saves several hours of manual work and leads to a better product line. Technol ogy ​ Function R And D Product Management Background "Adore Me is a women’s intimates company based in New York City. The company manufactures and sells lingerie, sleepwear, swimwear, activewear, and other products. Customer reviews were taking too long to go through as Adore Me became more and more popular. But the brand's identity was built on being close to customers." Benefits ​ Data 1000s of customer reviews.

< back AI Case Study Rolls Royce plans to predict maintenance requirements for jet engines to improve aircraft efficiency using Microsoft Azure's machine learning Rolls Royce and Microsoft have announced a partnership to monitor its jet engines and aggregate other data to predict maintenance needs and optimise scheduling. Other uses for the data analysis will be fuel consumption explanations for Rolls Royce's airline clients. Industry Transportation Airlines Project Overview "Starting with Azure IoT solution accelerators, Rolls-Royce will be able to collect and aggregate data from disparate and geographically distributed sources at an unprecedented scale... Initially, the types of data being processed include snapshots of engine performance that the planes send wirelessly during a flight, massive downloads of comprehensive “black box”–type data, technical logs, and flight plans as well as forecast and actual weather data provided by third parties. Using Microsoft Cortana Intelligence Suite, Rolls-Royce will be able to analyze a rich set of data and perform data modeling at scale to accurately detect operational anomalies and help customers plan relevant actions... By looking at wider sets of operating data and using machine learning and analytics to spot subtle correlations, we can optimize our models and provide insight that might improve a flight schedule or a maintenance plan and help reduce disruption for our customers. In expanding the scope of services Rolls-Royce offers its customers, fuel efficiency is one of the first and highest-yield areas that the company is targeting. By analyzing new data against existing forecasts, reference tables, and historical trends, Rolls-Royce will be able to help airlines understand exactly which factors—including flight plans, equipment maintenance, weather, and discretionary fuel—have the most impact on fuel performance". Reported Results Planned; results not yet available Technol ogy ​ Function Strategy Strategic Planning Background "Flight delays are a familiar headache for most people who fly on commercial airlines. At a personal level, they are disruptive and costly, but for the airlines the impact is exponentially larger. Minimizing the cost and disruption of maintenance activities is a key focus for these businesses. Rolls-Royce has over 13,000 engines for commercial aircraft in service around the world, and for the past 20 years, it has offered customers comprehensive engine maintenance services that help keep aircraft available and efficient. As the rapidly increasing volume of data coming from many different types of aircraft equipment overtakes the airlines' ability to analyze and take insight from it, Rolls-Royce is using the Microsoft Azure platform to fundamentally transform how it uses data to better serve its customers. Today, each engine has many sensors and generates thousands of signals, with a corresponding increase in the number of data points produced. As a leading provider of aviation engine services, Rolls-Royce examined these growing data analysis challenges and emerged with a plan to address the changing market with a more compelling set of services to meet the broader needs of the marketplace". Benefits ​ Data According to Microsoft: "Initially, the types of data being processed include snapshots of engine performance that the planes send wirelessly during a flight, massive downloads of comprehensive “black box”–type data, technical logs, and flight plans as well as forecast and actual weather data provided by third parties".

< back AI Case Study Riot Games detects anomalies negatively impacting project and software performance in real time to improve product delivery Using Time-series data analytics, New Relic's platform identifies patterns of usage to notice issues before it starts impacting users. The platform facilitates real-time monitoring, instant identification of root cause of issues and dynamic resource allocation. Industry Consumer Goods And Services Entertainment And Sports Project Overview "Riot Games has a wide array of applications, but it’s hard to fully absorb and prioritize actions from all the data we gain from monitoring. An automated tool like New Relic Radar has been valuable in guiding us to issues for services that operate in the dark. For example, Radar proactively directed us to a disk space utilization issue for a machine we don’t pay much attention to, enabling us to add disk space before it impacted users." According to New Relic, "Radar is a personalized feed designed to provide engineering and operations teams predictive and prescriptive insights into the critical services they are responsible for within their company’s software ecosystem. Radar analyzes the data from these services to identify patterns and potential issues—often previously undetectable—and provide actionable ways to resolve the issue. Learning from user engagement and actions, Radar constantly improves to ensure the most relevant recommendations are surfaced to meet user needs. Radar provides analytic recommendations via cards which contains interesting, relevant, and actionable data about a specific service or the ecosystem as a whole. " Reported Results Proactively identify issues before they impact customer experience. E.g: Predict disk space utilization issues enabling adding disk space before it impacted users Technol ogy ​ Function Information Technology Network Operations Background "Riot Games, Inc. is an American video game developer and eSports tournament organizer based in West Los Angeles, California." Benefits ​ Data ​

< back AI Use Case Determining threshold level at which to flag compliance risks Determining threshold level at which to flag compliance risks with the aim to minimise both 'false positives' and 'false negatives' both of which carry a cost Function Legal And Compliance Compliance Benefits Risk reduction - Reduced compliance risk,Risk reduction - Evaluate counter-party risk (eg credit scores),Cost - Reduce wastage Case Studies HSBC~HSBC reduces false positives for money laundering detection by 20% using AI to automate the system rules Potential Vendors ​ Industry Financial Services Banking Data Sets Structured / Semi-structured AI Technologies Machine Learning (ML),ML Task - Grouping - Anomaly Detection

< back AI Case Study Ross Goodwin trained a system to generate literary travel fiction using neural networks Ross Goodwin carried out a project to generate a 'novel' based on his cross-country trip from Brooklyn to New Orleans. He equipped the car with a surveillance camera, an old GPS unit and a microphone, all connected to its laptop which was in turn connected to a printer. Goodwin has trained a long short-term memory (LSTM) neural networks on hundreds of books and Foursquare location data, which the three devices and and the computer’s internal clock would feed with data along the trip. As the system was fed data, the 'results' were printed on paper and that is how the 'novel' was created. Industry Consumer Goods And Services Entertainment And Sports Project Overview "On March 25, 2017, a black Cadillac with a white-domed surveillance camera attached to its trunk departed Brooklyn for New Orleans. An old GPS unit was fastened atop the roof. Inside, a microphone dangled from the ceiling. Wires from all three devices fed into Ross Goodwin’s Razer Blade laptop, itself hooked up to a humble receipt printer. This, Goodwin hoped, was the apparatus that was going to produce the next American road-trip novel. The aim was to use the road as a conduit for narrative experimentation, in the tradition of Kerouac, Wolfe, and Kesey, but with the vehicle itself as the artist. He chose the New York-to-NOLA route as a nod to the famous leg of Jack Kerouac’s expedition in On the Road. Underneath the base of the Axis M3007 camera, Goodwin scrawled “Further.” Along the way, the four sensors—the camera, the GPS, the microphone, and the computer’s internal clock—would feed data into a system of neural networks Goodwin had trained on hundreds of books and Foursquare location data, and the printer would spit out the results one letter at a time. By the end of the four-day trip, receipts emblazoned with artificially intelligent prose would cover the floor of the car. They’re collected in 1 the Road, a book Goodwin’s publisher, Jean Boîte Éditions, is marketing as “the first novel written by a machine.” (Though, for the record, Goodwin says he disagrees it should bear that distinction—“That might be The Policeman’s Beard Is Half Constructed by a program from the ’80s,” he tells me.) Regardless, it is a hallucinatory, oddly illuminating account of a bot’s life on the interstate; the Electric Kool-Aid Acid Test meets Google Street View, narrated by Siri. Throughout the journey, data from the different sensors produced sentences of varying poeticism: Latitude and longitude coordinates were printed verbatim and appended with mysticism (“35.415579526 N, -77.999721808 W, at 154.68504432 feet above sea level, at 0.0 miles per hour, and the first flat of the story in the country is the first in part of the world”). Images were converted into ghostly prose (“A ski lift business for the last time the train was already being darkened and the street was already there”). Locations recognized from the Foursquare dataset were surrealistically remarked upon (“Eagles Nest Diner: a american restaurant in Goldsborough or the Marine Station, a place of fish seemed to be a man who has been assembled for three days”). Dialogue from the mic was captured and mutated (“I somewhat when i’m on why i didn’t get hurt yeah my car is an every down i know?”)." Reported Results The system generated a 'novel'. Technol ogy ​ Function R And D Product Development Background "A former ghostwriter for the Obama administration, Goodwin describes himself as “a writer of writers.” Using neural networks, he generates poetry, screenplays, and, now, literary travel fiction. I first encountered his work when his algorithms transformed the Senate’s 2014 torture report into a novel. In Narrated Reality, his master’s thesis at NYU, Goodwin loaded his backpack with devices (a compass, a punch clock, and a camera) that fed their data into long short-term memory (LSTM) neural networks as he walked around the city, churning out weird associative poetry. A sample: “All the time the sun / Is wheeling out of a dark bright ground.” So, when a machine hacker in Biloxi finished fabricating a custom piece of hardware for him, Goodwin decided to upgrade his nascent AI and take it cross-country." Benefits ​ Data "The four sensors—the camera, the GPS, the microphone, and the computer’s internal clock—would feed data into a system of neural networks"