From Mega Rounds to Market Ripples – SwissCognitive AI Investment Radar

The AI Investment Radar is back with another Thursday-to-Thursday round-up of the most significant developments in global AI funding and strategic investment. This week, headline attention was dominated by Anthropic’s $3.5 billion round—March’s largest raise—marking a continued race among frontier model developers. Yet beyond that, capital movements spanned from public sector commitments to corporate scaling strategies.

EY-Parthenon announced a $250 million allocation toward AI-powered edge platforms, while Deloitte reaffirmed its $3 billion commitment by expanding its Global Simulation Center of Excellence. OpenAI’s plans for a $40 billion round, led by SoftBank, underscored how large-scale compute and model development remain critical funding priorities.

At a government level, the EU pledged €1.3 billion to develop AI and digital skills under its Digital Europe Programme. On a global scale, IDC projects that AI investments will add $22.3 trillion in economic value by 2030, equating to nearly $5 for every dollar spent. Meanwhile, philanthropic and regional efforts—from Google’s $10 million AI grant to nonprofits, to Mastercard’s investment in Singapore-based AIDA—highlight the growing importance of distributed innovation.

CoreWeave’s downscaled IPO, along with continued investor concerns about AI implementation gaps, also offer a more tempered look at the market’s momentum. Yet from drug discovery at Isomorphic Labs to AI-enabled supply chain optimization, the range and depth of AI deployments continue to grow.

Tune in next week for more updates of the world of AI investments.

Previous SwissCognitive AI Radar: Global AI Capital Moves at Full Speed.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

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$100B for AI Chips, $40B for AI Bets – SwissCognitive AI Investment Radar

AI investment shows no signs of slowing, with capital flowing across semiconductors, cloud AI, financial AI, and responsible AI initiatives. This week, TSMC is preparing a staggering $100 billion investment in U.S. chip production, reinforcing the U.S. AI supply chain. Meanwhile, Anthropic’s valuation tripled to $61.5 billion, after securing $3.5 billion in funding to keep pace with OpenAI and DeepSeek.

The private sector’s AI appetite remains insatiable. Blackstone’s Jonathan Gray emphasized AI’s dominance in global investment trends, while Guggenheim and billionaire investors assembled a $40 billion AI investment pool to fuel finance, sports, and media innovation. Meanwhile, Canva’s AI report revealed that 94% of marketers have now integrated AI into their operations, marking a fundamental shift in business strategy.

The global AI race is also drawing government interest. The European Commission announced a €200 billion mobilization for AI investments, alongside France’s €109 billion push, as President Macron aims to position Europe as a heavyweight in AI development. Across the globe, China’s Honor pledged $10 billion to AI investment, deepening ties with Google for a global expansion.

The infrastructure for AI applications continues to scale rapidly. DoiT announced a $250 million fund dedicated to AI-driven cloud operations, while Shinhan Securities backed Lambda Labs with a $9.3 million investment to advance NVIDIA GPU-powered AI cloud services. Meanwhile, Accenture is doubling down on AI decision intelligence, backing Aaru to improve AI-powered behavioral simulations.

Beyond the corporate sphere, responsible AI investments are gaining traction. Chinese firms are increasing spending on ethical AI as part of a broader strategy to align AI governance with innovation. Meanwhile, Blackstone committed $300 million to AI-driven Insurtech, supporting AI-powered safety solutions in insurance.

With tech giants, startups, and governments all placing massive bets on AI, the sector’s financial landscape is evolving faster than ever. Investors are watching closely as AI’s long-term ROI takes center stage.

How will the capital influx shape AI’s next phase? The coming months will bring more answers.

Previous SwissCognitive AI Radar: AI Expansion and This Week’s Top Investments.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag $100B for AI Chips, $40B for AI Bets – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dileep Kumar Pandiya – “AI for Transformative Enterprise Growth: Insights from a Principal Engineer”

You know, it’s amazing to think about. Imagine your sales team closing deals twice as fast. Or your supply chain just adapting on the spot when the market shifts. Honestly, it’s not something from the future—it’s happening now, all thanks to AI.

I have been working in tech for almost 18 years, and I’ve seen how these tools turn ambitious ideas into actual results. I want to show you what that looks like in real life—where AI didn’t just help businesses grow, it completely changed the game.

How AI Unlocks Growth in Enterprises

What if your business could predict customer needs before they even knew them? AI makes this possible. It’s no longer about guesswork or reacting late; it’s about proactive strategies powered by data.
Take a retail chain struggling with overstock issues. By implementing AI to forecast demand using real-time trends, they reduced inventory waste by 20% and increased availability of high-demand items by 15%. It’s a transformation that goes beyond efficiency—it’s about building smarter, more agile businesses.

AI Copilot: Redefining Sales with AI

Sales has always been about timing and relationships. But what if AI could help you focus on the right opportunities at exactly the right moment? That’s the promise of AI Copilot.
When we launched Copilot, the goal was simple: empower sales teams to act smarter and faster. By integrating AI, I built a platform that could analyze millions of data points in seconds to identify high-potential accounts. The result: Sales teams were no longer overwhelmed by data they were driven by insights.
Here’s what stood out most to me: within three months, Copilot wasn’t just saving time—it was generating millions in additional revenue. Seeing the tangible impact on businesses and hearing feedback like “I can’t imagine working without this” made every late night worth it.

Scaling Smarter with AI and Microservices

Think of a system that can process thousands of real-time events every second, with no downtime. That’s what we built with the Phoenix Project, a scalable platform that uses AI and microservices to empower B2B clients.
One client used this platform to optimize marketing campaigns dynamically. Instead of waiting weeks for data analysis, they could adjust strategies on the fly, improving lead quality by 30% and cutting acquisition costs dramatically. It’s proof that scalability isn’t just a technical goal—it’s a business imperative.

Lessons for Enterprises Ready to Embrace AI

Here’s a story I often share: A small business hesitant to invest in AI started with a single pilot project—automating customer inquiries with AI chatbots. Within six months, they expanded the system to handle order tracking, inventory checks, and even personalized product recommendations. Today, they credit AI for a 25% increase in customer retention.
My takeaway is to start small, but think big. AI’s value compounds over time, so even small steps can lead to significant transformations.

Future Trends in AI and Enterprise Growth

The future isn’t just about doing things faster—it’s about doing them smarter. Imagine systems that can explain their decisions clearly or tools that work alongside humans to tackle complex problems.
One trend I’m particularly excited about is real-time decision-making. For example, picture a global logistics company rerouting shipments during a storm, avoiding delays and cutting costs. This kind of agility is becoming the new standard, and businesses that embrace it early will set themselves apart.

Final Thoughts

AI is the foundation for building the future of business. Whether it’s transforming sales strategies, driving efficiency, or enabling agility, the opportunities are immense. My advice: Don’t wait for the perfect moment to start. Take a step, learn, and grow with AI.

About the Author:

Dileep Kumar Pandiya is a globally recognized Principal Engineer with over 18 years of groundbreaking work in AI and enterprise technology. He has pioneered transformative AI-driven platforms and scalable systems, driving innovation for Fortune 500 companies like ZoomInfo, Walmart, and IBM. His leadership has redefined sales technology and digital transformation, earning him prestigious awards and international acclaim for his contributions to business growth and industry advancement. Known for his ability to blend visionary thinking with practical solutions, Dileep continues to shape the future of enterprise technology.

Der Beitrag AI for Transformative Enterprise Growth: Insights from a Principal Engineer erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “How Countries Are Using AI to Predict Crime”

Industry 4.0 and 5.0 is using robotics to bring supply chains into the future. The last decade has been fraught with challenges, including delays, worker shortages and market volatility. Mitigating costs and enhancing the workforce are the goals of swarm robotics, and artificial intelligence (AI) is making them even more competent. See how these workers make supply chains resilient and competitive.

What Are Swarm Robotics?

Swarm robotics is a field focusing on large quantities of simple yet practical robots. These robots work best in groups to achieve straightforward tasks, making them optimal for reducing labor burdens. They also shine in industries like supply chains, where repetitive tasks take up a major portion of the working day.

Supply chains need to use swarm robotics because they are easy to manage simultaneously. They are autonomous, respond to environmental stimuli and are easy to reprogram to new tasks. The collective efforts of these machines can make decisions on the fly, covering ground from last-mile delivery to utilizing resources in a smarter way.

How Do Supply Chains Use Swarm Robotics?

These robots enhance operations while allowing supply chains to overcome common pain points. Each application for swarm robots is also made better by AI. What does this look like?

Dynamic Operations

Because swarm robots take tedious tasks away from workers, they allow people to focus on more high-level processes. In the meantime, the bots can tally inventory, navigating complex warehouses in large numbers. They are immediately deployable to do automatic updates, sending instant notifications to procurement, fulfillment and distribution teams.

Swarm robots are also ideal in changing, unstructured environments. With AI and sensor technology, they can map areas no matter how complicated they are. As they learn to navigate, they become more proficient when interacting with similar environments because of machine learning algorithms. This informs routing and navigation and allows perpetual scaling potential.

Cost Reduction

Delegating tasks to robots saves supply chains tons of money. Human error costs corporations between $50-$300 for every mistake. The increased accuracy is only one aspect of the financial savings. The robots save businesses time and money in talent acquisition processes, which take efforts away from fulfilling client needs.

However, the most prominent financial gain may be from warehouse savings. Refined inventory management prevents objects from taking up square footage and energy as they collect dust. Instead, there is detailed metadata on each item, their expiration date, market values and more, which swarm robots can collect with AI.

Productivity Gains

ot only do AI-powered swarm robots save money, they make everything more efficient. Preventing errors, defects and more can shorten lead times from suppliers. In one study, several industries experienced shortened fulfillment lead times by an average of 6.7 days.

They can also allow parallel task execution. While some robots pick up objects, others can transport them and even more can pack them. This yields numerous time savings across lengthy processes with multiple intermediaries.

There are also other productivity gains because swarm robots make supply chain environments safer for workers. They can constantly monitor unsafe conditions in real time, saving employees the trouble of entering dangerous circumstances. This means fewer workers experience injuries and incidents, allowing them to work with higher morale in safer conditions.

Preparing the Swarm

Much like swarms of ants group together to achieve a common goal, these types of robots optimize supply chains. Combining them with AI makes them even more powerful. As they advance, swarm robotics consistently prove they are a must-have fixture for supply chain management in the future.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How AI Enables Swarm Robotics in the Supply Chain erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

AI in Corporate Budgets and National Strategies – SwissCognitive AI Investment Radar

The UK government is making a $17 billion commitment to AI, setting the stage for large-scale adoption with its AI Opportunities Action Plan. Meanwhile, Microsoft has confirmed a staggering £65.1 billion AI infrastructure investment, reinforcing the tech industry’s reliance on expanding AI data centers. In the U.S., Amazon is allocating $11 billion toward cloud and AI infrastructure in Georgia, further cementing its role as a key player in AI development.

The private sector is also making significant moves. Blackstone’s $300 million investment into AI data company DDN positions the firm at the forefront of AI-driven data storage and analytics. Meanwhile, Singapore secures a $7 billion Micron investment to strengthen its role in the AI supply chain. In the automotive industry, Hyundai is investing $16.6 billion to integrate AI into electric vehicle production, signaling a shift in manufacturing strategies.

Retail and consumer brands are also embracing AI, with spending projected to rise by 52% in 2025. A Honeywell survey reveals that over 80% of U.S. retailers plan to expand AI investments to improve customer experience and operational efficiency. However, while enterprises are willing to invest up to $250 million in generative AI, questions about return on investment persist.

AI is increasingly shaping global markets, not just as a technological tool but as a key driver of economic strategy. Whether through national policies, corporate spending, or AI-driven supply chains, investments in AI are becoming a defining force for the future of business and innovation.

Stay tuned for next week’s AI investment updates.

Previous SwissCognitive AI Radar: AI Investment Opportunities Worldwide.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag AI in Corporate Budgets and National Strategies – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: fastcompany.com – “25 Experts Predict How AI will Change Business and Life in 2025”

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Copyright: cio.com – “AI data readiness: C-suite fantasy, big IT problem”

Business leaders may be confident that their organizations’ data is ready for AI, but IT workers tell a much different story, with most spending hours each day massaging the data into shape.

Nearly nine in 10 business leaders say their organizations’ data ecosystems are ready to build and deploy AI at scale, according to a recent Capital One AI readiness survey. But 84% of the IT practitioners surveyed, including data scientists, data architects, and data analysts, spend at least one hour a day fixing data problems.

Seventy percent of those IT pros spend one to four hours a day remediating data issues, while 14% spend more than four hours each day, according to the survey.

The survey points to a fundamental misunderstanding among many business leaders regarding the data work needed to deploy most AI tools, says John Armstrong, CTO of Worldly, a supply chain sustainability data insights platform.

“There’s a perspective that we’ll just throw a bunch of data at the AI, and it’ll solve all of our problems,” he says. “It says our job as technology leaders can help educate our audience on what is possible and what it will take to get to their goal.”

The implications of the ongoing misperception about the data management needs of AI are huge, Armstrong adds. When he talks to other IT leaders, they all are struggling with pressure to adopt AI, Armstrong says.

“It’s a big, big issue, because if not done right, your organization could spend literally millions of dollars on the wrong solution set to achieve the wrong outcome,” he says.[…]

Read more: www.cio.com

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Copyright: intelligencebriefing.substack.com – “Making AI Adoption Less Uncomfortable – For All”

In October, Gartner reported that 74% of CEOs believe AI will transform their industry in 2024. Yet, 70-80% of AI projects still fail. But that’s hardly because of technology. Slack’s recent Fall 2024 Workforce Index sheds more light on a growing disconnect between executives and employees. It’s especially one data point that I can’t stop thinking about. (More on that in a second.)

In conversations with CIOs and VPs of IT, I hear that their companies already use AI—often developed and implemented by their IT teams. These are real business scenarios in which AI adds value or helps the business team do a task they were unable to do before, from supply chain optimization to product descriptions and document processing. But it’s not without struggles. Organizational dynamics, politics, and resistance to change come up more often as adoption barriers. So, how does all that fit together?

A Common Perception: Using AI Makes You Seem “Lazy”

According to Slack’s Fall 2024 Workforce Index report, 48% of respondents feel that admitting they use AI at work will make them seem “lazy.” They will also likely end up with just more work due to their newly unlocked efficiency.

Nearly half (48%) of all desk workers would be uncomfortable admitting to their manager that they used AI for common workplace tasks. The top reasons for workers’ discomfort are 1) feeling like using AI is cheating 2) fear of being seen as less competent and 3) fear of being seen as lazy.

But here’s the thing: it says more about the state of corporate culture than about AI technology if the most innovative employees—who are evidently creating new efficiencies with AI(!)—are afraid to share that they know how to use AI (and do it). By the way, they still use AI but they just won’t tell you.[…]

Read more: www.intelligencebriefing.substack.com

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Copyright: cio.com – “Agentic AI: 6 promising use cases for business”

AI agents will play a vital role in software programming and cybersecurity, but they will also change enterprise workflows and business intelligence, experts say.

Agentic AI is having a moment, as proponents see the benefits of using autonomous AI agents to automate manual tasks across organizations.

Agentic AI, which Forrester named a top emerging technology for 2025 in June, takes generative AI a step further by emphasizing operational decision-making rather than content generation. The promise the approach has for impacting business workflows has organizations such as Aflac, Atlantic Health System, Legendary Entertainment, and NASA’s Jet Propulsion Laboratory already pursuing the technology.

CRM leader Salesforce has since centered its strategy around agentic AI, with the announcement of Agentforce. IT service management giant ServiceNow has also added AI agents to its Now Platform. Microsoft and others are also joining the fray.

With AI agents popping up in so many situations and platforms, organizations interested in the technology may find it difficult to know where to start. A handful of use cases have so far risen to the top, according to AI experts.

Agentic AI will integrate smoothly with ERP, CRM, and business intelligence systems to automate workflows, manage data analysis, and generate valuable reports, says Rodrigo Madanes, global innovation AI officer at EY, a consulting and tax services provider. AI agents, unlike some past automation technologies, can make decisions in real-time, making process automation a primary use case.

“AI agents can automate repetitive tasks that previously required human intervention, such as customer service, supply chain management, and IT operations,” Madanes says. “What sets the technology apart is its ability to adapt to changing conditions and handle unexpected inputs without manual oversight.”[…]

Read more: www.cio.com

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Copyright: cio.com – “Beyond the Hype: Key Components of an Effective AI Policy”

In today’s rapidly evolving technological landscape, artificial intelligence (AI) plays a pivotal role in transforming businesses across various sectors. From enhancing operational efficiency to revolutionizing customer experiences, AI offers immense potential. However, with great power comes great responsibility. Creating a robust AI policy is imperative for companies to address the ethical, legal and operational challenges that come with AI implementation.

Understanding the need for an AI policy

As AI technologies become more sophisticated, concerns around privacy, bias, transparency and accountability have intensified. Companies must address these issues proactively through well-defined policies that guide AI development, deployment and usage. An AI policy serves as a framework to ensure that AI systems align with ethical standards, legal requirements and business objectives.

For instance, companies in sectors like manufacturing or consumer goods often leverage AI to optimize their supply chain. While this leads to efficiency, it also raises questions about transparency and data usage. A clear policy helps ensure that AI not only improves operations but also aligns with legal and ethical standards.

Key components of an effective AI policy

Ethical principles and values

It’s important to define the ethical principles that guide AI development and deployment within your company. These principles should reflect your organization’s values and commitment to responsible AI use, such as fairness, transparency, accountability, safety and inclusivity. If your company uses AI for targeted marketing, for example, ensure that its use respects customer privacy and prevents discriminatory targeting practices.
Data governance

Strong data governance is the foundation of any successful AI strategy. Companies need to establish clear guidelines for how its data is collected, stored and used, and ensure compliance with data protection regulations like GDPR in the EU, CCPA in California, LGPD in Brazil, PIPL in China and AI regulations such as EU AI Act.[…]

Read more: www.cio.com

Der Beitrag Beyond the Hype: Key Components of an Effective AI Policy erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.