News/Strategy
Why Tech Roles Should be Consolidated in the C-Suite in the AI Era
The rise and consolidation of technology leadership: Organizations are beginning to recognize the drawbacks of C-level proliferation in technology roles and are moving towards consolidating senior technology and data positions. The trend of creating numerous C-suite technology positions, which began in the 1980s with chief information officers (CIOs), has led to a proliferation of specialized roles such as chief technology officers, chief information security officers, and chief artificial intelligence officers. This expansion of technology-focused executive positions has resulted in potential inefficiencies and lack of cohesion in organizational digital strategies. In response, some companies are now opting for a consolidated approach,...
read Sep 13, 2024In-House Legal Teams See Major Productivity Gains From AI
AI-powered legal innovation: Unilever's in-house legal team is leading the charge in leveraging artificial intelligence to transform corporate legal operations, setting a new standard for efficiency and cost-effectiveness. Unilever has established three "powerhouses" or legal delivery centers in Barcelona, Mexico City, and Bengaluru, employing 85 staff to handle large volumes of legal contracts using advanced technologies, including generative AI. The implementation of AI tools like Microsoft's Copilot and CoCounsel has resulted in significant productivity gains, with lawyers saving an average of 30 minutes per day on tasks such as contract drafting and review. This increased efficiency has allowed Unilever's legal...
read Sep 12, 2024How Anywhere Real Estate Uses AI to Boost Leads and Listings
AI's growing role in real estate: Anywhere Real Estate, a New Jersey-based real estate services company, is harnessing artificial intelligence to enhance efficiency and deliver smarter services in the competitive property market. Cautious approach to AI implementation: Rudy Wolfs, CTO at Anywhere Real Estate, emphasizes the importance of setting realistic expectations due to the current immaturity of AI technology. The company acknowledges the potential of AI while remaining mindful of its limitations, ensuring a balanced approach to implementation. By carefully managing expectations, Anywhere Real Estate aims to build trust in AI systems among both its agents and clients. Two primary...
read Sep 12, 2024Why Good Data is the Foundation for Success in the AI Era
Generative AI's success hinges on high-quality data: Organizations face challenges in preparing and processing data effectively for AI initiatives. The data dilemma: Many businesses struggle with data preparation for AI projects, leading to potential setbacks in their artificial intelligence initiatives. Gartner analysts predict that at least 30% of generative AI projects will be abandoned after proof of concept through 2025, with poor data quality cited as a primary reason. Having data ready for AI can drive greater business outcomes by 20%, according to Gartner Senior Director Analyst Roxane Edijlala. Organizations often lack clarity on how to prepare their data, especially...
read Sep 11, 2024How SMB Leaders are Using Generative AI for Strategic Planning
The rise of generative AI in strategic planning: Generative AI is emerging as a powerful tool for business leaders, particularly in smaller companies, to enhance their strategic planning processes and decision-making capabilities. The potential of generative AI to revolutionize business decision-making has captured the attention of the business community, with some industry leaders predicting a new era of unprecedented possibilities. Smaller companies, in particular, are exploring the application of generative AI in strategic planning, recognizing its potential to provide valuable insights and overcome human biases. Case studies reveal AI's strengths and limitations: An examination of two disguised case studies highlights...
read Sep 11, 2024AI Model Merging Boosts Capabilities, Raises New Challenges
The rise of merged AI models: Researchers and developers are exploring ways to combine multiple generative AI systems, aiming to create more capable and versatile artificial intelligence. This emerging trend seeks to leverage the strengths of different models, such as merging text-focused systems with those specializing in mathematical computations. The goal is to develop AI that can handle a broader range of tasks and domains more effectively than single-purpose models. Key approaches to AI model merging: Several methods are being employed to combine the capabilities of different AI systems, each with its own advantages and challenges. The output combiner approach...
read Sep 11, 2024The Best Ways For Organizations to Fail When Implementing AI
The generative AI revolution presents unique challenges: The rapid evolution of generative AI technology since the launch of ChatGPT-3 in November 2022 has created a complex landscape for organizations seeking to implement AI assistants. Traditional approaches to corporate technology projects are ill-suited for generative AI initiatives due to the rapidly changing nature of the technology. Organizations face a high risk of making incorrect decisions in their AI implementations, potentially requiring significant rebuilds within a few years. The dynamic nature of generative AI necessitates a more flexible and adaptable approach to project planning and execution. Three major risk factors for generative...
read Sep 11, 2024How Enterprises Can Successfully Implement AI Copilots
AI copilots gain traction in organizations: Forrester's research reveals a surge in AI copilot adoption, with 51% of global information workers reporting their organizations are embracing solutions like Microsoft Copilot for Microsoft 365 and ChatGPT Enterprise. Despite widespread adoption, organizations are struggling to quantify the return on investment (ROI) for AI copilots, creating a dilemma for leaders. The challenge lies in balancing the potential future benefits of generative AI (genAI) against the difficulty of measuring immediate results. A pragmatic approach to AI copilot implementation: To resolve the genAI business case conundrum, organizations need to address four key areas: benefits, adoption,...
read Sep 10, 2024Why Analysts Predict an End to AI’s Over-Reliance on GPUs
AI's brute force era nears its end: Gartner analysts predict a shift away from specialized AI hardware, including GPUs, as more efficient programming techniques emerge. The big picture: Gartner's chief of research for AI, Erick Brethenoux, argues that the current reliance on powerful hardware for AI workloads is temporary, with generative AI applications likely to follow historical patterns of optimization. Brethenoux draws on 45 years of AI observation, noting that specialized AI hardware has consistently been rendered obsolete as standard machines become capable of handling AI tasks. The current "brute force" phase of AI is characterized by unrefined programming techniques...
read Sep 10, 2024How Baby Registry Babylist Navigates the AI Roadmap
AI's impact on marketing strategies: The rise of generative AI has captured the attention of marketers, prompting agencies and brands to develop new strategies for leveraging these technologies while ensuring data security and fairness. Major agencies are forming partnerships with leading AI companies such as OpenAI, Runway, and Perplexity, signaling a shift towards integrating AI into their service offerings. Companies are implementing auditing policies to address concerns about data security, stability, and fairness as AI-powered tools become more prevalent in the marketplace. Babylist, a baby registry company, is taking a cautious approach to AI adoption, prioritizing human oversight and quality...
read Sep 10, 2024Apple’s AI Strategy Seems to Be Largely a Game of Playing Catch-Up
Apple's AI push mirrors HomePod's late entry: Apple's recent Glowtime event unveiled a range of AI-powered features and products, reminiscent of its delayed entry into the smart speaker market with the HomePod. The event showcased Apple Intelligence, the company's much-hyped AI system, alongside new products like the iPhone 16 Pro. Many of the announced features closely resemble existing offerings from competitors such as Google, OpenAI, and Meta. This pattern of playing catch-up echoes Apple's late entry into the smart speaker market with the HomePod in 2018, years after Amazon's Echo (2014) and Google Home (2016) had established dominance. Siri's evolution...
read Sep 9, 2024How a Deflating AI Bubble Could Be Welcome News for Innovation
The AI hype cycle enters a cooling phase: The artificial intelligence industry is experiencing a natural correction after a period of intense excitement, mirroring previous technology boom-and-bust cycles. Nvidia, a key player in the AI hardware market, saw its stock price drop 20% over the summer after a 200% surge earlier in the year, indicating a shift in investor sentiment. Gartner's Hype Cycle, a widely respected industry barometer, has placed generative AI in the "Trough of Disillusionment," suggesting a period of reassessment and more realistic expectations. This cooling phase is reminiscent of other tech bubbles, such as the dotcom era,...
read Sep 8, 2024What iPhone 16 Must Do with Siri to Beat Google and Samsung in the AI Game
The AI race heats up in smartphones: Apple faces growing pressure to enhance Siri and AI capabilities in the upcoming iPhone 16 to compete with advanced features from Google Pixel and Samsung Galaxy devices. Seamless OS integration: Apple needs to improve Siri's interaction with the iPhone's operating system to provide a more cohesive user experience. Siri should be able to effortlessly access and manipulate data across various apps, particularly the calendar, to match the functionality offered by competitors. Enhanced integration would allow users to perform complex tasks through voice commands, streamlining daily operations and increasing productivity. ChatGPT collaboration: The planned...
read Sep 8, 2024AI Enthusiast Builds 192GB VRAM Server for Llama-3.1 in Basement
Cutting-edge AI infrastructure: A tech enthusiast has built a powerful LLM server in their basement, featuring 8 RTX 3090 GPUs with a total of 192GB VRAM, designed to run Meta's Llama-3.1 405B model. The project was motivated by the builder's need for more VRAM capacity than their previous 48GB setup, which had become insufficient for their LLM experiments. The custom-built server represents a significant investment in high-end hardware, reflecting the growing demand for powerful computing resources in AI research and development. Key components and specifications: The LLM server boasts impressive hardware specifications, carefully selected to maximize performance and capability for...
read Sep 8, 2024How to Prepare for Fluctuating GPU Costs as Demand for AI Tech Surges
The AI revolution's cost challenge: The increasing demand for Graphics Processing Units (GPUs) to power artificial intelligence applications is set to usher in an era of volatile costs, presenting a new challenge for businesses across various industries. GPUs are essential components for running large language models (LLMs) that drive chatbots and other AI applications. As demand for AI technologies grows, businesses will need to adapt to managing variable costs for these critical components. Industry impact and precedents: While some sectors are accustomed to managing fluctuating costs, the volatility in GPU prices will affect industries that have little experience with this...
read Sep 7, 2024How AI Is Personalizing Customer Service Experiences
The AI revolution in customer service: Businesses across industries are leveraging artificial intelligence to enhance customer experiences, boost operational efficiency, and address challenges like increased call volumes and shifting customer expectations. AI-powered customer service software is being deployed to improve agent productivity, automate interactions, and extract valuable insights from customer data. Retailers, telecommunications providers, financial institutions, and healthcare organizations are among the industries benefiting from AI-driven customer service solutions. The technology enables personalized service, product recommendations, and proactive support by tapping into an organization's collective knowledge and experiences. Key benefits of AI in customer service: Strategic implementation of AI can...
read Sep 6, 2024How Effective Metadata Management Unlocks AI Potential for Enterprises
Metadata management is becoming increasingly crucial for enterprises as they navigate the complexities of AI and ML implementation, offering a pathway to streamlined data operations and enhanced security. The big picture: As AI and ML reshape industries, effective data management has become essential for organizations, with metadata management emerging as a critical component for driving success in these technologies. AI and ML require large amounts of accurate data, necessitating comprehensive data management strategies that address security, regulations, efficiency, and architecture. A Cloudera study reveals that 73% of enterprise IT leaders report their company's data exists in silos and is disconnected,...
read Sep 6, 2024Why This Startup Banned The Use Of AI Chatbots
The AI chatbot dilemma in documentation: Mux, a video technology company, recently tested AI chatbots for their documentation but ultimately decided against implementing them due to concerns about accuracy and potential user confusion. Mux explored AI chatbot solutions to enhance their documentation experience, hoping to provide tailored answers to user queries and bridge gaps in their information architecture. The company's initial tests with AI chatbots trained on their documentation and blog posts yielded disappointing results, with responses that were often inaccurate or misleading. Mux's team was particularly concerned about the chatbots' inability to provide nuanced information about complex topics, such...
read Sep 6, 2024How LLMs are Shifting Knowledge Organization to Mirror Human Cognition
The emergence of Large Language Models (LLMs) is fundamentally altering our approach to knowledge organization and understanding, shifting from static, hierarchical structures to dynamic, context-driven webs of information. A paradigm shift in knowledge representation: LLMs are redefining the ontological framework of knowledge, moving away from rigid, predefined categories towards a more fluid and adaptive structure that mirrors human cognition. Traditional ontologies in computer science and AI have relied on fixed, hierarchical systems to categorize and relate concepts within specific domains. LLMs, in contrast, operate on a "latent ontology" where relationships between ideas are inferred through exposure to vast amounts of...
read Sep 5, 2024How to Use AI to Improve Your Advertising Strategy
AI revolutionizes advertising: Artificial intelligence is transforming the advertising industry, offering new opportunities for precision targeting, creative optimization, and data-driven insights. The advertising industry has continuously adapted to technological advancements, with AI representing the latest frontier in its evolution. While AI supporters hail it as a solution to industry inefficiencies, skeptics question its true value, suggesting the reality likely falls somewhere in between. To effectively harness AI's power, brands must develop a strategic roadmap that addresses their unique challenges before implementing technological solutions. Precision audience identification: AI enables highly refined audience segmentation based on detailed customer profiles and location-based targeting....
read Sep 4, 2024Meta Challenges OpenAI with Ambitious AI Assistant Push
AI assistant race heats up: Meta's CEO Mark Zuckerberg aims to make MetaAI the most used AI assistant by the end of the year, challenging OpenAI's dominance in the field. Meta has rapidly pushed its AI assistant to its 3 billion users since its debut, reaching 400 million monthly users and 40 million daily users earlier this month. OpenAI's ChatGPT currently leads with 200 million weekly active users, setting a high bar for competitors in the AI space. The aggressive promotion of MetaAI has raised concerns within the company about potential user fatigue and retention issues. User adoption challenges: Meta's...
read Sep 4, 2024OpenAI is Changing and it Will Have Big Implications for the Entire AI Industry
AI pioneer's transformation: OpenAI, the creator of ChatGPT, is undergoing significant changes in its management, organization, and investment strategy as it seeks to evolve from a research lab into a profit-driven company at the forefront of artificial intelligence. The company has been hiring top tech executives, disinformation experts, and AI safety researchers to bolster its leadership and expertise. Seven new board members have been added, including a former National Security Agency chief, to provide guidance and oversight. OpenAI is in talks with major tech companies and investment firms for a potential deal that could value the company at $100 billion....
read Sep 4, 2024HBR: Gen AI Amplifies Business Strengths But Won’t Create Unique Edge
The rise of generative AI in business: Generative artificial intelligence (gen AI) is poised to significantly transform business operations, creating substantial value across various sectors. Companies are leveraging gen AI to uncover novel product opportunities and business models, potentially reshaping entire industries. Gen AI is being used to automate routine decisions, allowing human employees to focus on tasks requiring ethical considerations, empathy, or creativity. The technology is democratizing access to customized professional services, making them available to a broader audience beyond the wealthy. Gen AI is enhancing customer communication by delivering product recommendations and information faster, more cost-effectively, and more...
read Sep 2, 2024AI Engineering Breakthrough Slashes Project Failure Rates
Engineered Intelligence: A New Approach to AI Implementation: The concept of "engineered intelligence" is emerging as a potential solution to the high failure rate of AI projects and the looming threat of another AI winter. The current approach to AI implementation often involves data scientists attempting to engineer real-world solutions, resulting in an 87% failure rate for AI projects. Engineered intelligence aims to create a distinct discipline for applied artificial intelligence, similar to how other scientific breakthroughs are handed off to specialized engineers for practical application. The Problem with Current AI Implementation: The lack of a dedicated field for applying...
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