Latest news with #StateofAI
Yahoo
4 days ago
- Business
- Yahoo
Digital Advertising Alliance Announces Review of Application of DAA Self-Regulatory Principles to AI Systems and Tools
DAA's Principles and Communications Committee Will Consider New Guidance Around AI Collection and Use of Interest-Based Advertising Data WASHINGTON, June 4, 2025 /PRNewswire/ -- The Digital Advertising Alliance (DAA) today announced the launch of a process to consider developing new guidance around the application of the DAA Self-Regulatory Principles to the collection and use of interest-based advertising (IBA) data by the rapidly-expanding universe of AI systems and tools that might have access to such data. "Few, if any, industries change faster than advertising, and the DAA has stayed ahead of those changes for the last 15 years by routinely reviewing, developing and releasing timely policy guidance around new technologies and business practices, from IoT to CTV, mobile data, and cross-device data," said Lou Mastria, CEO of the DAA. "As the advertising industry increasingly looks to AI tools and systems, it's vital that industry codes of conduct reflect that reality to serve companies and their consumers." "This review will look at the steps companies can take to ensure they are providing appropriate information and control to consumers around the collection and use of IBA data by those systems, thus enabling responsible and sustainable consumer engagement and growth," Mastria continued. The DAA's Principles and Communications Committee will manage the process for the review and development of potential AI-related guidance for the industry. Among the issues it will consider are the appropriate industry participants and process to develop such guidance, the current and anticipated use cases for IBA data by AI systems and tools, consumer expectations around the collection and use of such data, and the legal and regulatory gaps/overlaps with any such guidance. "Companies across the advertising supply chain are moving quickly to integrate AI to better reach their customers, deliver more effective messages, and strengthen their businesses," said Michael Signorelli, Partner, Venable LLP, which will help draft any AI guidance. "As the industry deploys AI-powered tools and systems, we need to ensure that the industry's preeminent self-regulatory regime keeps pace with those changes and continues to provide individuals with information and choices around covered use of IBA data by AI systems." According to the McKinsey "State of AI" survey released in March 2025, AI has been broadly deployed across marketing organizations, with marketing/sales tied with IT as the top business function at surveyed companies to have adopted AI in its work. Similarly, a survey conducted last year by SurveyMonkey found that 56% of marketers say their company is taking an active role in implementing and using AI. In the coming weeks, the DAA Principles and Communications Committee and participating stakeholders representing key trade associations, advertisers, publishers, ad tech providers, and agencies will convene to start the process of evaluating and potentially setting AI guidance for the DAA Principles. About the Digital Advertising Alliance The Digital Advertising Alliance (DAA) is an independent not-for-profit organization which establishes and enforces responsible privacy practices for relevant digital advertising, while giving consumers information and control over the types of digital advertising they receive. The DAA runs the YourAdChoices, mobile AppChoices and PoliticalAds programs. Underlying the DAA's efforts are the DAA Self-Regulatory Principles, including updates to address changing technologies and business models such as multi-site, mobile, and cross-device data. Compliance with the DAA Principles is independently enforced for all companies in digital advertising by BBB National Programs and the Association of National Advertisers (ANA). The DAA is managed by a consortium of the leading national advertising and marketing trade groups, including the 4As; American Advertising Federation; ANA; Interactive Advertising Bureau; and Network Advertising Initiative, with the advice of BBB National Programs. Media Contact:Andrew Weinstein202-667-4967396040@ View original content to download multimedia: SOURCE Digital Advertising Alliance
Mint
26-04-2025
- Business
- Mint
Companies are struggling to drive a return on AI. It doesn't have to be that way.
AI adoption among companies is stunningly high, but most of them are struggling to put it to good use. They intuit that AI is essential to their future. Yet intuition alone won't unlock the promise of AI, and it isn't clear to them which key will do the trick. As of last year, 78% of companies said they used artificial intelligence in at least one function, up from 55% in 2023, according to global management consulting firm McKinsey's State of AI survey, released in March. From these efforts, companies claimed to typically find cost savings of less than 10% and revenue increases of less than 5%. While the measurable financial return is limited, business is nonetheless all-in on AI, according to the 2025 AI Index report released in April by the Stanford Institute for Human-Centered Artificial Intelligence. Last year, private generative AI investment alone hit $33.9 billion globally, up 18.7% from 2023. The numbers reflect a 'productivity paradox," in which massive improvements in AI capabilities haven't led to a corresponding surge in national-level productivity, according to Stanford University economist and professor Erik Brynjolfsson, who worked on the AI Index. While some specific projects have been enormously productive, 'many companies are disappointed with their AI projects." For companies to get the most out of their AI efforts, Brynjolfsson advocates for a task-based analysis, in which a company is broken down into fine-grained tasks or 'atomic units of work" that are evaluated for potential AI assistance. As AI is applied, the results are measured against key performance indicators, or KPIs. He co-founded a startup, Workhelix, that applies those principles. Companies should take care to target an outcome first, and then find the model that helps them achieve it, says Scott Hallworth, chief data and analytics officer and head of digital solutions at HP. A separate report from McKinsey issued in January helps explain why AI adoption is racing ahead of associated productivity gains, according to Lareina Yee, senior partner and director at the McKinsey Global Institute. Only 1% of U.S. companies that have invested in AI report that they have scaled their investment, while 43% report that they are still in the pilot stage. 'One cannot expect significant productivity gains at the pilot level or even at the company unit level. Significant productivity improvements require achieving scale," she said. The critical question then, is how companies can best scale their AI efforts. Ryan Teeples, chief technology officer of 1-800Accountant, agrees that 'breaking work into AI-enabled tasks and aligning them to KPIs not only drives measurable ROI, it also creates a better customer experience by surfacing critical information faster than a human ever could." The privately held company based in New York provides tax, booking and payroll services to 50,000 active clients, with a focus on small businesses. The company isn't a Workhelix customer. Additionally, he says, companies should look beyond individualized AI usage, in which employees use GenAI chatbots or AI-equipped productivity tools to enhance their work. 'True enterprise adoption…involves orchestration and scaling across the organization. Very few organizations have truly reached this level, and even those are only scratching the surface," he said. The use of AI at 1-800Accountant begins with an assessment of whether the technology improves the client experience. If the AI provides customers with answers that are as good, better or faster than a human, it's a good use case, according to Teeples. In the past, the company scheduled hourlong appointments with advisers who answered simple client questions, such as the status of their tax return. Now, the company uses an AI agent connected to curated data sources to address 65% of customer inquiries, with 30% arranging a call with a human. (The remaining 5% drop out of the inquiry process for various reasons.) The company uses Salesforce's Agentforce to handle customer inquiries and its Einstein platform for orchestration across 1-800Accountant's back end. Teeples said the company is saving money on the cost of human advisers. 'The ROI in this case was abundantly clear," he said. Orchestrating AI across the enterprise requires the right infrastructure, especially when it comes to data, according to Gabrielle Tao, senior vice president for data cloud at Salesforce. It is important, she said, to harmonize data, for example, by creating a consistent way to refer to business concepts such as 'orders" and 'transactions," regardless of the underlying data source. AI deployments should target tasks that are both frequent and generalizable, according to Walter Sun, global head of artificial intelligence at SAP. Infrequent, highly specific tasks such as a marketing campaign for a single event might benefit from AI, but applying AI to regularly occurring tasks will achieve a more consistent ROI, he said. Historically, it has taken years for the world to figure out what to do with revolutionary general-purpose technologies including the steam engine and electricity, according to Brynjolfsson. It isn't unusual for general-purpose models to follow a 'J-curve," in which there's a dip in initial productivity, as businesses figure things out, followed by a ramp-up in productivity. He says companies are beginning to turn the corner of the AI J-curve. The transformation may occur faster than in the past, because businesses—under no small amount of pressure from investors—are working to quickly justify the massive amount of capital pouring into AI. Write to Steven Rosenbush at
Forbes
09-04-2025
- Business
- Forbes
What OpenAI's $40 Billion Raise Reveals About The Future Of Work
SANTA MONICA, CA - APRIL 5: OpenAI CEO Sam Altman (R) and Oliver Louis Mulherin (L) attend the 11th ... More Annual Breakthrough Prize Awards and Ceremony at the Barker Hangar in Santa Monica of Los Angeles, California, United States. (Photo by Tayfun Coskun/Anadolu via Getty Images) When OpenAI closed its record-breaking $40 billion funding round—led by SoftBank and rumored to include Microsoft and a syndicate of big-name investors—it didn't just rewrite the playbook for tech financing. It signaled the dawn of a radically different future for work. With a valuation now topping $300 billion, OpenAI has positioned itself not just as a leader in AI but as a force capable of reshaping the way organizations think, operate, and grow. This is not a tech sideshow—it's the main event. And for every HR leader, CEO, team manager, and frontline worker, the implications are immediate and transformative. The next generation of AI won't just live in sidebars or take notes in meetings. It's gunning for the core of how businesses function—and it's armed with $40 billion in runway to make it happen. Here's why. For years, AI has played a supporting role—answering emails, summarizing documents, organizing calendars. But OpenAI's ambitions, now turbocharged by this new funding round, signal a shift from support to strategy. We're about to see AI embedded at the heart of business decision-making, moving from 'assistive' to 'autonomous.' Generative AI, in particular, is evolving rapidly—stepping up from simple content generation to a deeper level of context awareness. According to McKinsey's State of AI report published in March of this year, 78% of organizations now use AI in at least one business function—up from just 55% a year earlier. Even more telling is the growing adoption of generative AI by C-level executives themselves, signaling a rising level of trust at the highest levels of leadership. This shift is also evident in more technical domains. Avi Freedman, CEO of the network intelligence company Kentik, explains that historically, resolving complex network issues required network engineers to have years—if not decades—of experience. However, as Freedman told me through his representative, 'Now anyone—a developer, SRE, or business analyst—can ask questions about their network in their preferred language and get the answers they need.' In environments where CEOs directly oversee AI governance, McKinsey's data shows the strongest EBIT impact. In other words: when leadership takes AI seriously, it drives measurable results. And that's before AI starts proposing strategic options, simulating market scenarios, or intervening in budget conversations. Perhaps the most misunderstood impact of AI isn't about job displacement, but job deconstruction. AI is allowing organizations to break traditional roles into tasks, optimize those tasks individually, and then reassemble them into more adaptive workflows. According to McKinsey, 21% of organizations using gen AI have already redesigned at least some workflows to accommodate it. That may sound modest, but it's a leading indicator. What starts with marketing and IT—currently the most AI-integrated departments—will inevitably bleed into HR, legal, operations, and finance. Imagine the marketing role of the near future: part campaign strategist, part prompt engineer, part analyst. Or consider HR: emotional coaching and performance feedback delivered by humans; talent forecasting and compliance handled by AI. Every function is up for reimagining. This doesn't mean humans are obsolete. It means the value of human work will shift. People will move up the value chain—to judgment, creativity, empathy, and relationship-building. But that shift will be uncomfortable, especially for those whose work has historically relied on predictability, repetition, or procedural expertise. Beneath the surface of OpenAI's war chest lies a deeper story: infrastructure. The Stargate project—OpenAI's joint $500 billion initiative with SoftBank and Oracle—is designed to build massive next-gen data centers that can power AI at unprecedented scale. The first $100 billion is already being deployed, with Texas as the flagship site. This isn't just about model training. It's a geopolitical and industrial race. Compute power is the oil of the AI era. Whoever controls it, controls the tempo of innovation—and the workplace implications are huge. Access to this infrastructure will increasingly determine which companies can afford to run real-time AI agents across business functions. In turn, this will drive widening disparities in productivity, competitiveness, and even job satisfaction. Organizations that fall behind may find themselves rapidly outpaced by competitors already embedding AI agents throughout every layer of their operations. Freedman argues that this shift is no longer just a matter of tech investment—it's fundamentally about real estate and energy, with fiber connectivity and cooling capacity at the core. In his view, the scalability of AI is now limited less by algorithms and more by physical deployment: where data centers are located, how quickly fiber can be installed, and whether the surrounding energy infrastructure can handle rising demand. Ultimately, Freedman suggests, control over this physical layer will determine not only which AI models perform best, but also which companies, cities, and countries will lead in the future of work. One of the most profound implications of AI at work is the need to renegotiate the social contract between employers and employees. In a world where AI handles more of the planning, execution, and reporting, what's left for humans? McKinsey reports that 38% of companies are already repurposing time saved by AI automation toward entirely new activities. But they also note a quiet trend: some large organizations are reducing headcount, particularly in customer service and supply chain roles, where AI's efficiency is highest. At the same time, a wave of new roles is emerging—AI compliance officers, ethics specialists, prompt engineers, and data translators. The report also shows a growing emphasis on reskilling: many firms are already retraining portions of their workforce, with more planning to follow over the next three years. The workplace is splitting in two: those who know how to collaborate with AI, and those who don't. And while McKinsey notes that most executives don't expect dramatic workforce reductions across the board, they do expect shifts in required skills, team structures, and workflows. If you're not learning, you're lagging. Here's a bold prediction: in the next five years, a company's culture will increasingly be mediated by AI. Not just supported by it—but shaped by it. As AI becomes embedded in performance reviews, hiring processes, customer interactions, and even Slack conversations, it begins to influence what is praised, what is corrected, and what is ignored. AI is not neutral—it reflects the data it's trained on, the goals it's optimized for, and the boundaries it's been given. McKinsey's report highlights that organizations with clear AI roadmaps, defined KPIs, and internal messaging around AI's value are seeing better outcomes. In other words, culture isn't being built by all-hands meetings anymore—it's being built in the feedback loops of your AI systems. This shift raises urgent considerations for HR and leadership teams. As AI systems begin to influence team dynamics, how can organizations effectively audit for bias? How can they ensure that AI-driven feedback tools amplify—rather than silence—diverse and dissenting voices? When the interface between managers and employees is mediated by algorithms, ethics and inclusion can't be afterthoughts—they need to be embedded from the start. The workplace of 2030 is being shaped today. The questions now are: will your organization lead, follow, or fall behind?
