Latest news with #businessstrategy
Forbes
19 hours ago
- Business
- Forbes
Is Your Business Ready For AI Or Just Chasing Hype?
Tod Loofbourrow, Chairman and CEO, ViralGains. Generative AI is rewriting the rules of marketing, and many brands are scrambling to keep up. But here's a reality check: by the end of 2025, Gartner predicts that 30% of GenAI projects will be abandoned before delivering any business value. This is because adopting AI isn't about deploying shiny new tools; it's about aligning technology with real business problems, building the right data foundation, managing risk and, most importantly, understanding where AI can actually move the needle. Right now, too many marketers are chasing AI for the optics, not the outcomes. Across the C-suite, "AI strategy" has become the must-have line item, especially in marketing. Promises of hyper-efficiency, real-time targeting, infinite personalization and generative scale have leaders greenlighting AI projects left and right. But a lot of these initiatives stall before launch. Worse yet, they fail to perform to expectations. This is because while the tech is impressive, the implementation is messy. Core questions—How will this drive ROI? Do we have the right data? Is this even the right use case?—often go unasked. This is the danger of AI FOMO: investing reactively, not strategically. The antidote? A framework that aligns AI opportunity with actual business relevance. If businesses want to cut through the hype, they need to ensure they have a thorough framework in place. Harvard Business Review, for example, introduced the WINS framework—a powerful lens to assess where GenAI can drive the most impact. WINS refers to the four dominant types of work GenAI can enhance: • Words (like ad copy, scripts and reports) • Images (visual assets, design and branding) • Numbers (analytics, forecasting and segmentation) • Sounds (audio branding, voice AI and podcasts) For marketers and advertising agencies, this should be viewed as core work. Your campaigns, strategies, insights and outputs all live squarely in this framework, which means your teams are on the front lines of AI transformation. But there's a second, equally important layer, which is evaluating how digitized your processes already are and how urgently you need to transform. Are you still manually pulling campaign data from siloed systems? Are your audience insights stuck in spreadsheets? Are your creative workflows bottlenecked? If your answer is "yes," you're likely in what HBR calls the "Next in Line" quadrant—where GenAI could deliver massive impact, but only if you prioritize the right foundations and partnerships. MIT's Andrew McAfee and others have discussed the importance of using AI to augment human performance—not replace it. The goal isn't to eliminate your creative, strategic or analytical talent. The goal is to amplify it. The real promise of GenAI lies in things like helping a junior copywriter draft 10 good ideas instead of two, letting a strategist test 50 creative variants in the time it once took to run A/B tests and giving insights teams real-time visibility into shifting audience sentiment. This isn't about automating your team out of existence. It's about enabling them to use GenAI as a co-pilot that elevates creative thinking, accelerates iteration and sharpens decisions. In working with large advertisers, I've seen how GenAI delivers value when combined with zero-party data—insights that consumers willingly share. Take the case of a national bank entering new markets. Rather than relying on assumptions about brand perception, they launched interactive video ads featuring a simple, embedded question: "Which word best describes our brand?" That single question became a rich source of insight. By applying GenAI-powered sentiment analysis to the responses, the bank quickly learned which creatives inspired trust, which messages resonated in specific regions and how to optimize the campaign in real time. This is WINS in action—words, images and numbers working together not just to capture attention, but to deepen understanding and accelerate performance. A common pitfall in adopting GenAI is the belief that everything must be built in-house to truly "own" the strategy. But in reality, GenAI success is less about technological ownership and more about making timely, strategic decisions that align with business goals. Deploying GenAI effectively often requires capabilities that span data engineering, model fine-tuning, regulatory alignment and creative integration. While some organizations may choose to develop these from scratch, others accelerate progress by leveraging existing tools, frameworks or external expertise—especially when it comes to domain-specific models or ready-to-use data pipelines. Ultimately, this is about avoiding unnecessary reinvention. Internal teams stay focused on differentiation and core value while relying on proven methods to get there faster and with less risk. The question is: where does your team add the most value, and where can others help remove friction so you can move with purpose, not just speed? To avoid AI regret and maximize impact, I suggest that CMOs and agencies answer the following three questions: 1. Where in our marketing operation do WINS apply—and how digitized are those processes? 2. How can we use AI to augment human creativity and decision-making—not just automate tasks? 3. Where might strategic collaboration help us accelerate time-to-value without compromising control or insight? AI has the power to reshape marketing—but only if it's tied to real outcomes, implemented strategically and grounded in the data and workflows that actually drive business value. The winners won't be those who jumped in first. The winners will be those who used AI wisely, with the right frameworks, the right partners and the right expectations. Don't chase the hype. Find your zero bit, deploy for impact and watch the wins follow. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Yahoo
a day ago
- Business
- Yahoo
1 Cash-Producing Stock with Promising Prospects and 2 to Be Wary Of
While strong cash flow is a key indicator of stability, it doesn't always translate to superior returns. Some cash-heavy businesses struggle with inefficient spending, slowing demand, or weak competitive positioning. Not all companies are created equal, and StockStory is here to surface the ones with real upside. Keeping that in mind, here is one cash-producing company that reinvests wisely to drive long-term success and two that may struggle to keep up. Trailing 12-Month Free Cash Flow Margin: 7.6% Started by three friends in Seattle's historic Pike Place Market, Starbucks (NASDAQ:SBUX) is a globally-renowned coffeehouse chain that offers a wide selection of high-quality coffee, beverages, and food items. Why Does SBUX Give Us Pause? Lagging same-store sales over the past two years suggest it might have to change its pricing and marketing strategy to stimulate demand Estimated sales growth of 4.3% for the next 12 months implies demand will slow from its six-year trend Costs have risen faster than its revenue over the last year, causing its operating margin to decline by 3.6 percentage points Starbucks is trading at $91.90 per share, or 29.1x forward P/E. Dive into our free research report to see why there are better opportunities than SBUX. Trailing 12-Month Free Cash Flow Margin: 12.7% Originally started as a farm water drainage company, Advanced Drainage Systems (NYSE:WMS) provides clean water management solutions to communities across America. Why Are We Wary of WMS? Customers postponed purchases of its products and services this cycle as its revenue declined by 2.8% annually over the last two years Earnings per share have dipped by 2.2% annually over the past two years, which is concerning because stock prices follow EPS over the long term Free cash flow margin dropped by 6.1 percentage points over the last five years, implying the company became more capital intensive as competition picked up Advanced Drainage's stock price of $115 implies a valuation ratio of 18.3x forward P/E. To fully understand why you should be careful with WMS, check out our full research report (it's free). Trailing 12-Month Free Cash Flow Margin: 4.1% Started as a mail-order tractor parts business, Tractor Supply (NASDAQ:TSCO) is a retailer of general goods such as agricultural supplies, hardware, and pet food for the rural consumer. Why Are We Positive On TSCO? Bold push to open new stores demonstrates an ambitious strategy to establish itself in underpenetrated territories Sales outlook for the upcoming 12 months implies the business will stay on its desirable six-year growth trajectory Stellar returns on capital showcase management's ability to surface highly profitable business ventures At $51.71 per share, Tractor Supply trades at 23.5x forward P/E. Is now the time to initiate a position? Find out in our full research report, it's free. Donald Trump's victory in the 2024 U.S. Presidential Election sent major indices to all-time highs, but stocks have retraced as investors debate the health of the economy and the potential impact of tariffs. While this leaves much uncertainty around 2025, a few companies are poised for long-term gains regardless of the political or macroeconomic climate, like our Top 5 Growth Stocks for this month. This is a curated list of our High Quality stocks that have generated a market-beating return of 183% over the last five years (as of March 31st 2025). Stocks that made our list in 2020 include now familiar names such as Nvidia (+1,545% between March 2020 and March 2025) as well as under-the-radar businesses like the once-micro-cap company Kadant (+351% five-year return). Find your next big winner with StockStory today for free. Find your next big winner with StockStory today. Find your next big winner with StockStory today
Globe and Mail
2 days ago
- Business
- Globe and Mail
AI Strategy Course Integrates Real-World Case Studies into AI Curriculum for Hands-On Learning
AI Strategy Course, led by instructor Dan O'Donnell, has enhanced its curriculum by integrating real-world case studies to help professionals apply AI strategically in business. Learn how businesses can incorporate AI into business workflows and strategy to reduce costs, be more efficient and increase revenue. Automate business tasks and integrate AI into business processes building a stronger workforce through AI literacy and strategies for getting products and services to market faster. Artificial intelligence is revolutionizing every industry and the AI Strategy Course has taken a bold step to bridge the gap between theory and real-world application. Founded and instructed by AI strategist Dan O'Donnell, the course has unveiled a major curriculum enhancement by integrating real-world case studies, equipping students with hands-on learning experiences that mirror practical business challenges. This shift underscores the program's commitment to preparing professionals to implement AI solutions effectively and responsibly in diverse organizational settings. As demand continues to grow for AI-literate professionals who understand both the strategic and operational aspects of artificial intelligence, the AI Strategy Course has positioned itself at the forefront of this educational evolution. The newly integrated case studies draw directly from real business scenarios across sectors such as healthcare, finance, logistics, retail, and marketing. Each case is designed to illustrate how AI technologies such as machine learning, natural language processing, and predictive analytics can be applied to solve specific business problems and optimize processes. Dan O'Donnell, the course's lead instructor and an experienced AI consultant, emphasized the importance of translating abstract AI concepts into actionable business strategies. 'Theoretical knowledge only gets professionals so far. By embedding real-world case studies into the AI Strategy Course, we are enabling learners to think critically and apply AI tools in ways that drive actual business results,' said O'Donnell. 'The goal is to create leaders who can confidently advocate for, implement, and manage AI initiatives that align with their company's strategic goals.' The hands-on component allows students to engage in scenario-based problem solving, including crafting AI implementation plans, analyzing AI ROI, and making ethical decisions around data usage and model bias. These experiences are particularly beneficial for business leaders, analysts, and project managers who may not have a deep technical background but are responsible for overseeing digital transformation within their organizations. Each case study within the course is paired with structured exercises that challenge learners to assess context, identify opportunities for AI integration, and propose scalable, ethical solutions. This approach enhances the course's accessibility to a wider range of professionals, not just engineers or data scientists. The modular nature of the content also allows users to revisit and refine their understanding of AI as they progress through various scenarios. One recent case study explores how a global logistics company used computer vision and real-time tracking to streamline its supply chain, reducing delivery delays by 28% and improving warehouse efficiency. Another focuses on how a regional healthcare network leveraged predictive analytics to reduce patient no-show rates and optimize appointment scheduling. These examples help students connect the dots between AI capabilities and measurable business value. In addition to the course content, students benefit from ongoing access to a dedicated Facebook community, AI Course Facebook, where they can discuss case studies, share insights, and network with other professionals. The course's companion YouTube channel also hosts visual breakdowns of AI trends, tools, and strategies, helping learners stay current with the fast-paced advancements in the field. The case study update comes at a pivotal time as AI adoption surges globally. According to recent industry reports, nearly 70% of enterprises are either piloting or actively deploying AI, yet a lack of strategic knowledge remains one of the top barriers to successful implementation. The AI Strategy Course addresses this gap by equipping learners with the critical thinking and decision-making frameworks needed to lead AI projects with confidence. Feedback from students who have completed the updated course has been overwhelmingly positive. Many have cited the case studies as instrumental in helping them apply AI concepts in boardroom discussions and project planning sessions. One learner, a mid-level marketing manager at a Fortune 500 company, remarked, 'The case study approach made it real for me. I could see how AI is already transforming my industry and where the opportunities lie for my team.' Beyond providing foundational AI knowledge, the AI Strategy Course aims to foster a mindset of innovation and ethical leadership. O'Donnell's instruction emphasizes not only the power of AI but also the responsibility that comes with deploying it. Issues such as data privacy, fairness, and model transparency are woven into each case, prompting learners to think beyond utility and toward impact. The newly enriched curriculum marks a significant step forward in how online education can support professional development in emerging technologies. With the proliferation of generative AI tools and enterprise-level deployments, businesses are seeking talent that understands both the 'why' and the 'how' of AI. The AI Strategy Course's case-study-driven model ensures that learners graduate with practical insights and a strategic framework to lead effectively in the AI era. Professionals interested in exploring how AI can transform their organization—and their career trajectory, are encouraged to enroll via where additional information about the course structure, outcomes, and community support is available. With this curriculum evolution, the AI Strategy Course continues to raise the bar for what modern AI education should look like: actionable, ethical, and aligned with the real needs of today's professionals.
National Post
2 days ago
- Business
- National Post
CB Insights Unveils Industry's First AI Agent Workforce for M&A, Partnerships, Competitive Intelligence, Deal Sourcing, GTM, and More
Article content NEW YORK — CB Insights, the leading provider of predictive market intelligence, today announced the launch of its Team of Agents – the industry's first comprehensive AI agent workforce designed specifically to enable business leaders with always-on market intelligence for strategic decision-making. CB Insights' growing team of agents starts with 11 specialized agents that work continuously to uncover insights, translate proprietary data into action, and accelerate smart decisions by providing the external intelligence layer that enterprises need to compete effectively across business development, investments, partnerships, GTM, and competitive intelligence. Article content While generic AI assistants struggle with business context and lack reliable external data, CB Insights' Team of Agents delivers validated intelligence and data science on over 10 million companies and 1,500 tech markets through purpose-built specialists that understand the nuanced requirements of strategic business functions. Article content Enterprise clients are already hiring CB Insights' Team of Agents to streamline their workflows and accelerate decision-making across research, diligence, briefings, and more – saving months of work. Article content 'The Scouting Report Agent is amazing – it's a huge time saver and lets us respond quickly with enough insight to get back to a client or be dangerous with,' said Bethany Boutin, Business & Market Intelligence Lead: Commercial Solutions + Digital at Evernorth Health Services, a subsidiary of The Cigna Group. Article content 'Most AI agents today are generalists trying to do everything – but when it comes to strategic decisions, you need specialists who understand your business context and have access to reliable external intelligence,' said Manlio Carrelli, CEO of CB Insights. 'Our Team of Agents represents the evolution from generic AI assistants to purpose-built intelligence operatives. They work around the clock on your behalf, each one an expert in their domain, all powered by our double-verified Business Graph and data science on the world's companies and markets – insights and data that are not available on ChatGPT, the Internet, or in any other agent.' Article content While most enterprise strategic decisions require external market intelligence about competitors, markets, and potential partners, existing AI tools often lack the specialized context and validated data necessary for high-stakes business decisions. CB Insights' growing Team of Agents solves this, starting with 11 specialized agents that deliver: Article content The agents listed are not exhaustive and CB Insights' clients can create their own with Microsoft Copilot, MCP, and soon directly in ChatCBI. Article content Agents Powered by the World's Most Comprehensive Business Graph Article content Unlike AI agents that rely on public web data or limited datasets, CB Insights' Team of Agents operates on the proprietary Business Graph – a continuously validated dataset with patented data science covering management teams, funding rounds, partnerships, deals, earnings calls, hiring patterns, patents, and more across global business activity. Article content 'The quality of AI output depends entirely on the quality of data input,' said Carrelli. 'Our agents don't just scrape the web – they work with double-verified intelligence based on proprietary information collected from companies and their customers directly, unique datasets like their commercial relationships, and proprietary data science that scores future potential. This means when our Competitive Sentinel tells you a rival is planning a major acquisition, or when our Acquisition Hunter identifies a strategic target, you can make decisions with confidence.' Article content Designed for Enterprise Integration Article content The Team of Agents operates across multiple platforms where strategic work happens: Article content Native integration with CB Insights' Strategy Terminal for comprehensive market intelligence workflows Microsoft 365 Copilot integration for seamless incorporation into existing productivity tools Model Context Protocol (MCP) compatibility for any MCP-compatible AI agent to tap into CB Insights' datasets and tools – including ChatCBI – without a single line of code API and custom solutions for embedding intelligence into enterprise applications and workflows CB Insights' forward-deployed strategists work with you to automate your research and strategy flows with custom agents Article content 'We're not asking enterprises to change how they work – we're making their existing workflows dramatically more intelligent,' said Carrelli. 'Whether you're in Excel building a market model, in PowerPoint preparing for a board meeting, or doing deep work inside the CB Insights terminal, our agents provide the external intelligence you need exactly when and where you need it.' Article content Availability and Pricing Article content CB Insights' Team of Agents is available immediately to enterprise customers through flexible deployment options. Individual agents can be deployed for specific use cases, or enterprises can access the full team for comprehensive market intelligence automation. Article content Article content Article content Article content
Fast Company
2 days ago
- Business
- Fast Company
AI readiness is no longer optional. Here's how to make it real in the next 18 months
AI isn't coming. It's here. And the next 18 months will be a proving ground, separating the organizations still testing the waters from those moving full steam ahead toward real transformation. Seventy-three percent of companies now spend over $1 million annually on AI, yet only about one-third have realized significant ROI from those investments. The gap between ambition and execution continues to widen. Many organizations still approach AI as a side project, guided by checklists rather than strategic vision. True AI readiness calls for something deeper: a clear strategy, modernized systems, and the commitment to scaling what works. The good news is, with the right focus, it's possible to go from fragmented efforts to enterprise-scale momentum—quickly. For leaders willing to act, the next 18 months offer a defining window to move from experimentation to integrated, business-aligned AI impact. WHY AI READINESS, NOT AI HYPE, IS THE REAL DIFFERENTIATOR The AI conversation is loud, but real transformation depends on readiness —the ability to scale AI with speed, discipline, and measurable value. That readiness goes beyond infrastructure. It requires: A strategy that connects business priorities with AI opportunities Teams equipped to move quickly without compromising trust or compliance Leadership alignment that drives adoption across functions The advantage now lies with those who move from intent to execution—who build the infrastructure, alignment, and cultural readiness to scale AI across the business. This isn't a time for isolated pilots or disconnected innovation efforts. It's a time to institutionalize AI as a core business capability. THE EXECUTIVE AI PLAYBOOK: WHAT LEADERS MUST GET RIGHT AI transformation doesn't hinge on a single capability. It requires readiness across five key dimensions, each critical to moving forward with confidence, speed, and accountability. 1. AI Strategy Alignment Too many AI initiatives lack a clear connection to business goals. Leading organizations define a strategic roadmap that prioritizes high-impact opportunities, aligns AI investments with enterprise objectives, and uses measurable outcomes to track progress. Without strategic alignment, even the most advanced models deliver limited value. 2. Data And Infrastructure Readiness Foundational systems often determine whether AI efforts scale or stall. Readiness in this area means having clean, accessible data, cloud infrastructure that can support high-volume workloads, and deployment processes built for speed, security, and resilience. Without this layer, innovation stays stuck in the laboratory. 3. Governance And Compliance For AI Responsible AI demands enforceable practices. Readiness in this area means having clearly defined structures, policies, and safeguards in place to ensure that AI systems are ethical, transparent, and legally compliant. Key components include role-based access controls, audit trails, model monitoring, bias mitigation processes, and risk escalation protocols. Organizations that embed governance early reduce exposure, maintain oversight, and build trust as they expand AI across the enterprise. 4. Talent And Culture Development AI adoption depends on more than technical skills. Teams need to trust the technology, understand its value, and feel equipped to use it in their day-to-day work. Readiness includes attracting and developing AI-literate talent while fostering a culture that supports experimentation, accountability, and continuous learning. Moving beyond isolated tool adoption requires a broader shift in culture—one that redefines how work gets done across the organization. 5. AI Integration Into Operations Scattered pilots and disconnected initiatives won't move the needle. Mature organizations embed AI into core business processes, scale solutions across functions, and continuously optimize based on performance. When AI is integrated into how the business runs—not just where it experiments—it becomes a driver of long-term performance, efficiency, and innovation. The belief that AI transformation demands massive, multi-year investments is outdated. Organizations that are moving fast have already proven the value of AI in isolated use cases. The next step isn't more experimentation—it's scaling what works. Getting out of the proof-of-concept mindset starts with identifying high-impact opportunities, integrating them into core systems, and measuring outcomes from day one. When AI improves efficiency, lowers costs, or enhances decision-making, momentum builds and resistance fades. Execution at speed doesn't require sacrificing control. With strong governance, technical readiness, and workforce alignment in place, organizations can accelerate adoption while maintaining clarity and accountability. What sets leaders apart isn't the number of pilots they've launched, but how consistently they turn early wins into enterprise-wide impact. Competitive advantage from AI won't come from early adoption alone. It will come from the organization's ability to scale strategically, operationally, and culturally. That kind of readiness doesn't happen by accident. It requires committed leadership, aligned priorities, modern infrastructure, and a workforce equipped to deliver impact. Technology may open the door, but leadership determines whether the organization is ready to walk through it. In the months ahead, the most meaningful gains will go to those who move beyond experimentation and create the conditions for AI to thrive. The real question isn't whether you've started. It's whether you're ready to lead.
