Latest news with #Think2025
Forbes
15-05-2025
- Business
- Forbes
Has IBM's IT Automation Software Gotten Better?
IBM Instana dashboard IBM There are two ways to answer the question posed in the headline. The simple answer is yes, IBM has continued to invest in acquisitions including HashiCorp and DataStax, leading to a more robust portfolio of products for enterprise IT shops. But, after attending IBM's Think conference in 2024, I walked away with concerns about this emerging portfolio of software. In particular, it seemed that IBM was struggling to develop an integrated product and go-to-market strategy. I was left scratching my head in terms of what advice I could give customers on how to engage with IBM and get some joint value out of these related but different solutions. Heading into last week's Think 2025, I wanted to be convinced that things were different despite more acquisitions. For the most part, I got what I was hoping for. (Note: IBM is an advisory client of my firm, Moor Insights & Strategy.) Last year I didn't think that IBM's software wasn't good or that it was lacking features. My concern was that IBM did not have a clear message about what made a number of point-products better together. For example, why would a longtime Apptio (IBM) customer consider switching to Instana (IBM) when they were perfectly happy with Instana competitor Dynatrace? Additionally, at Think last year IBM announced a new product called Concert that sounded kind of like Instana in some ways. So even if I did not already use a competing product, which IBM product should I buy? This year was quite different, and IBM was very clear about what it needed to change and what it ended up doing. I walked away from Think 2025 feeling much better than the previous year. But, I also think that for anyone evaluating IBM's IT Automation software, all factors need to be considered. Three of these stand out to me. As I stated earlier, I feel that a year has made a big difference in IBM's IT Automation software. And I think IBM gets what it needs to do to attract and satisfy customers. There were many more demos this year. The conversations were frank about how customers are using the technology in the real world. And I heard quite a bit about how much IBM has learned from these acquisitions, suggesting (I hope) that newer acquisitions may go smoother. On that front, I'm excited to see where we stand in another year with HashiCorp — which I'll be writing more about soon.
Fast Company
12-05-2025
- Business
- Fast Company
Maura Healey and Christina Romer reflect on how business can be more like government—yes, government
Hello and welcome to Modern CEO! I'm Stephanie Mehta, CEO and chief content officer of Mansueto Ventures. Each week this newsletter explores inclusive approaches to leadership drawn from conversations with executives and entrepreneurs, and from the pages of Inc. and Fast Company. If you received this newsletter from a friend, you can sign up to get it yourself every Monday morning. Long before the Trump administration tapped Elon Musk to cut federal costs and headcount via the Department of Government Efficiency (DOGE), business leaders and politicians have been trying to find ways to make government leaner, less bureaucratic, and more like a well-run corporation. In 1982, Ronald Reagan asked J. Peter Grace, CEO of W.R. Grace & Co., to lead a private sector committee to root our government waste. While campaigning for the presidency in 1992, Bill Clinton promised to 'radically change the way government operates—to shift from top-down bureaucracy to entrepreneurial government.' The notion that federal agencies and programs can be run more like businesses has animated the Oval Office aspirations of executives such as Michael Bloomberg, Howard Schultz, and Doug Burgum. Public-sector playbooks for CEOs But are there lessons that executives in the private sector can learn from their public counterparts? Businesses certainly have benefitted from government; tech companies owe a debt to DARPA, the U.S. Defense Advanced Research Projects Agency, for funding the predecessor to the internet, for example. Local governments can be particularly good at empowering employees at all levels to innovate, something that can confound large corporations. Rick Wartzman and Lawrence Greenspun, when they were with the Drucker Institute, shared the story of how a single front-line employee and two-middle managers in South Bend, Indiana, streamlined the city's application for tax-abatement to four pages from 22 and moved the process online. The mayor who challenged them to innovate? Pete Buttigieg, who went on to become U.S. Secretary of Transportation during the Biden administration. Government has produced and shaped other notable leaders, including Christina Romer, the former chair of the Council of Economic Advisers in the Obama administration; and Maura Healey, the current governor of Massachusetts, whom I happened to interview last week at Think 2025, IBM's annual event for senior business and technology leaders (Fast Company was a strategic media partner at Think). At a time when many forces are pushing government entities to be more like businesses, I asked both of them to reflect on what business can learn from government. Here's what they had to say: Maura Healey, governor, Massachusetts: 'Nobody has ever asked me that question. In many ways, government can do better by operating like a business, but in other cases that just doesn't hold. Government is the place where things have to get done that the market isn't going to do. As governor, I have to be attentive to the needs of seven million residents, some of whom voted for me and some of whom didn't, many of whom have competing interests. In government you have to find a way to account for all of that. It gets messy; it gets noisy; but at the end, it helps in terms of productive policy formulation when you have that kind of stakeholder incorporation. 'For purposes of creating a better world—I think in big terms—a world where there is an abundance of energy, of housing, of healthcare, of transportation, of economic opportunity and prosperity for every child, it has to come from a broader lens than sometimes might be incentivized by the bottom line.' Christina Romer, professor emerita, Graduate Division, University of California at Berkeley, former chair of the Council of Economic Advisers: 'Government policymaking is often chided for being slow, and it can indeed be frustratingly bureaucratic and incremental. But 'moving fast and breaking things' is not what Social Security recipients want when they are waiting for their checks or what the public expects when the FAA is reconfiguring flight patterns and deciding control-tower staffing. At their best, government actions are carefully researched, broadly vetted, and deliberately implemented. This approach wouldn't work in every business setting, but it could certainly help prevent many bad decisions and unintended consequences. 'Something else that impressed me during my time in government was the high quality of government workers. Far from being the lazy, overpaid bureaucrats they are often caricatured to be, I found government workers to be knowledgeable, hard-working, and committed to serving the public. Businesses would certainly benefit if they could generate that kind of loyalty and passion in their workers.' Good enough for government work Are you a business leader who has worked in government? What did you learn from your experiences in the public sector? Send your stories to me at stephaniemehta@ I may include insights in a future newsletter.
Forbes
07-05-2025
- Business
- Forbes
IBM Think 2025 Showcases Watsonx.data's Role In Generative AI
IBM's Think 2025 showcases as the cornerstone of its generative AI strategies. IBM One of the themes for IBM Think 2025 — this week's flagship event for customers, partners and analysts — is exploring how AI and automation are being put to work in the real world. One of the big product updates is for the platform, which is continuing to evolve to address common roadblocks in scaling generative and agent-based AI. At the event, IBM has emphasized how useful this is for its customers, especially when dealing with fragmented or hard-to-use enterprise data, including unstructured formats. By simplifying the data-for-AI stack with an open, hybrid architecture, IBM positions as a platform for enterprises looking to deliver faster, more accurate and scalable generative AI outcomes. In this article, I'll look at the challenges of enterprise AI adoption and how IBM is seeking to use capabilities to address these challenges and create value in day-to-day operations. (Note: IBM is an advisory client of my firm, Moor Insights & Strategy.) Before getting into the specifics announced at Think, it's important to understand the prevailing problems that is trying to address. Enterprise adoption of generative AI is accelerating, but many organizations are discovering that their legacy data environments are not equipped for the demands of AI. According to IBM, less than 1% of enterprise data is being used for generative AI initiatives today, while approximately 90% of data is unstructured — and scattered across diverse locations, formats and platforms. Despite significant investments in AI models and applications, the real barrier to generative AI success for most enterprises is not inference costs or model optimization. It is the data itself. Many enterprises are misaligned in their generative AI strategies, focusing on application development without first addressing the foundational data challenges that limit model performance. To overcome this, organizations require trustworthy, company-specific data to produce high-performing and accurate AI outcomes. However, in many enterprises, large volumes of unstructured data remain locked within e-mails, documents, presentations, videos and the like, making it inaccessible to large language models and generative AI tools. Unstructured data presents a unique challenge because it is dynamic, fragmented across systems, lacks clear labels, and often requires additional context for meaningful interpretation. Retrieval-augmented generation, while useful for structured knowledge retrieval, can be ineffective when attempting to extract and harmonize unstructured information at the enterprise scale. Meanwhile, enterprises are saddled with disjointed stacks of data lakes, warehouses, governance tools and integration platforms, adding complexity rather than reducing it. All of this creates a huge missed opportunity for companies, because there is enormous value to be gained if they can find a way to use their in-house enterprise data in their AI efforts to address their specific challenges. In a recent discussion I had with IBM's Edward Calvesbert, vice president for watsonx product management, he said, 'If everyone's using the same AI models trained on the same data, how do you stand out? The real edge comes from using your enterprise data — plugging it into your apps and systems to actually get work done and move the needle.' Role In AI Adoption IBM's strategic response for solving the enterprise's unstructured data problem is I wrote about the initial launch of this platform back in 2023. At IBM Think this week, IBM previewed the new generation of that transforms the platform into a hybrid, open data lakehouse with data fabric capabilities. Innovations include ' integration' (note IBM's lowercase nomenclature), which can make it easier to access and manage data in different formats, and ' intelligence,' which uses AI to automate data curation, management and governance. If IBM is allowed to complete its intended acquisition of DataStax, the company also hopes to incorporate DataStax's NoSQL and vector database capabilities to further enhance the unstructured data management in IBM is a hybrid, open data lakehouse architecture that facilitates AI applications by ... More creating a flexible data platform to manage, secure and use various data types across cloud and on-premises systems. IBM The architecture emphasizes separation of storage and computing, support for open formats such as Apache Iceberg and Presto, hybrid deployment across clouds and on-premises environments and deep integration with governance and security tools. With it, IBM wants to give enterprises the ability to ingest, govern and retrieve both structured and unstructured data at scale. According to the company, this could enable the creation of generative AI applications and agentic AI models that are 40% more accurate and performant, and much faster than before. As Calvesbert put it, 'Today's generative AI tools mostly help employees find and summarize information. What's next is unlocking real impact — by strengthening the data layer so AI can deliver accurate, trusted results at scale.' IBM Integrates Watsonx With Db2 IBM is continuing to modernize Db2 by embedding watsonx capabilities directly into Db2 12.1 (as I wrote about late in 2024) to enhance the platform with AI-powered automation. At IBM Think, the company introduced new features such as the Database Assistant — a natural-language tool that acts as a real-time advisor for DBAs, helping to monitor performance, diagnose issues and optimize system operations. These operational updates reflect a broader evolution underway within Db2. With the announcement of Db2 version 12.1.2, the platform now plays a broader role in IBM's hybrid, AI-ready data strategy. The new version includes native support for vector embedding and similarity search to enable faster development of AI applications that blend curated, structured data with unstructured sources like documents and logs. Through Db2 workloads can now participate in AI pipelines with shared governance, unified metadata and federated access. Enhancements also include support for open table formats (this is where Apache Iceberg comes in) and integration with vector databases, allowing Db2 to bridge structured and unstructured data. In doing so, Db2 is evolving from a traditional relational database into a foundational component of the enterprise AI stack — one that by design supports automation, observability and scalability across hybrid environments. How Is Delivering Business Results At a time when many companies — and their shareholders — are skeptical about the real-world effects of enterprise AI, IBM is ready with examples of how the business impact is already measurable across different industries. For example, BanFast, one of the largest construction firms in Sweden, used to reduce manual data input by 75% and then leveraged that data to enhance worker health and safety. A U.S.-based financial services firm saved $5.7 million by creating a unified view of its operational IT data using enabling self-service access, consistent governance and automated processing. Meanwhile, a global manufacturing client partnered with IBM and EY to automate the ingestion and consolidation of indirect tax data across 34 source systems in 73 countries, improving compliance efficiency. IBM and EY also recently launched for tax, a product that integrates EY's tax expertise with IBM's AI technology, including In sports and media, IBM is a key partner for The US Open and The Masters, where millions of data points are processed in real time to generate AI-driven player commentary and fan insights. These deployments highlight how is helping modernize data infrastructure to enable faster insights, greater operational efficiency and competitive differentiation for enterprises that are under pressure to scale AI initiatives quickly and responsibly. Challenges For IBM's offers a promising approach to managing enterprise data, especially by making both structured and unstructured data more accessible and usable for generative AI. However, managing data comes with challenges, especially for organizations that are still early in their data modernization efforts. Integrating unstructured and structured data across cloud and on-prem environments remains complex. Many customers will face issues with data sprawl, inconsistent governance policies and internal silos that slow adoption. Even with a unified platform, getting data ready for AI — clean, labeled and trustworthy — is a major effort. Another challenge is organizational readiness. Teams may not have the skills or processes in place to take full advantage of capabilities, particularly when it comes to aligning the teams that manage data with those responsible for AI application development. There's also the question of cost and operational complexity. While is designed for flexibility, deploying it in a hybrid environment with multiple components and tools — such as data processing engines, storage systems and governance frameworks — can stretch already limited IT resources. These components often require careful integration and ongoing coordination across teams. That said, as the examples offered by IBM show, the business impact could be significant for companies that can work through these issues. provides a way to connect siloed systems, reduce reliance on brittle point-solutions and make better use of internal data — especially the 90% that's unstructured and often ignored. But the path forward will require more than just technology. It will take coordination across teams, clear ownership of data quality and a realistic view of what's needed to move from pilot to production. IBM's position is straightforward: solving the data problem is the first step toward making AI useful at scale. For customers, the choice isn't just about tools — it's about whether they're ready to do the work needed to get their data in shape.
