Too often, organizations focus on building AI strategies without first strengthening the foundation that truly makes AI work — their data. Ask yourself: are you relying on flat tables, or have you invested in fully relational, traceable datasets that give your models real depth and context? And just as importantly, does your AI tooling integrate human logic and understanding, or is it operating in isolation? Andy Brennan explores these critical questions in IBISWorld’s latest article on building smarter AI through smarter data. 👉 Read more here: https://lnkd.in/e5H3s-Hz
How to build smarter AI with smarter data
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Senior Client Relationship Manager @ IBISWorld | Customer Advocacy, Growth, & Customer Success | DaaS Integration
📊 Smarter AI Starts with Smarter Data! Check out this great read from IBISWorld: Smarter AI Starts with Smarter Data: https://lnkd.in/e_vWGiKV Here are a few quick takeaways: 🔹 Data quality is everything. AI is only as strong as the verified, contextualized data behind it. 🔹 Context creates clarity. Raw data isn’t enough! Structure and context turn information into insight. 🔹 Trust builds advantage. Reliable data reduces risk and fuels AI that drives real business outcomes. The article is a great reminder that true AI success starts with a strong data foundation, not hype. #AI #DataStrategy #BusinessIntelligence #DigitalTransformation #DataDriven #IBISWorld #Daas
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Open-source AI just pulled off what many thought impossible. Moonshot AI released Kimi K2 Thinking last week, an open trillion-parameter model that's outperforming closed systems like GPT-5 and Claude Sonnet 4.5 on reasoning benchmarks. The community's reaction has been swift: observers are calling this the closest open models have ever come to matching proprietary frontiers, reminiscent of DeepSeek's r1 moment earlier this year.[https://lnkd.in/eiYYrBae] Here's what stands out. The efficiency story matters more than scale. Kimi K2 activates just 32 billion parameters per token, trained for $4.6 million. It scores 44.9% on Humanity's Last Exam compared to GPT-5's 41.7%. The real takeaway isn't that it's bigger, it's that smaller teams with smarter architecture and focused training can compete when they stop chasing infinite compute. [https://lnkd.in/edr3i84U] Agentic AI is becoming table stakes. The model handles 200-300 sequential tool calls autonomously while maintaining reasoning coherence. It's ranked #1 among open-source models on complex benchmarks like SWE-Bench Verified (65.8% vs GPT-4.1's 54.6%). This signals a shift in what we actually need from AI systems, agents that act, not just chat. The open-source momentum is real, but it's also normalizing. The benchmark leaderboard was once the main story. Now it feels routine, each month another release closes the gap with closed models. What developers are actually noticing is deployment practicality: quantization speeds, inference costs, and the freedom to fine-tune locally. What this tells us: the competitive advantage has moved from model capability to systems thinking, infrastructure, training efficiency, and integration depth. The frontier isn't staying still, but neither is open-source. Resources to read more: https://lnkd.in/eiYYrBae https://lnkd.in/e3uiXdTB https://lnkd.in/edr3i84U https://lnkd.in/eJG-PSFJ https://lnkd.in/eSJw-qWi https://lnkd.in/eVrYkgVN https://lnkd.in/ehkKzyNz https://lnkd.in/ej_AfH4w https://lnkd.in/e-fw4D2H https://lnkd.in/eahKef-i https://lnkd.in/eT37ppba https://lnkd.in/eWwSgJSs https://lnkd.in/eMZ6iv6q https://lnkd.in/ebxmjU5J
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New inspiring AI insights from our colleague, Branislav Popović, AI & ML Expert and Principal Research Fellow! Learn how the model context protocol enhances AI’s strategic agility through context-aware orchestration, and why choosing the right client, balancing performance trade-offs, and ensuring strong governance are essential for effectively deploying adaptive, intelligent AI systems. Find out more here: https://lnkd.in/dtwmJNjw
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AI Innovation, AI Risk & Commercialization, MIT Labs, GTM & Industry Relations. Consortia Founder. MIT Alum Data Science Lab, MIT Risk Group. Founder MaSS Systems — Digital Twin/Asset platform acquired by OpenText
This has always been true but an untapped goldmine and more important than ever. 🚨Models will continue to commoditize, data strategy + ingenuity is your competitive advantage and moat. “Your AI advantage won’t come from your model budget — it’ll come from your data strategy.”
As frontier models get smarter, they’re also becoming indistinguishable. GPT-4, Claude, and Gemini now differ by single-digit percentage points on most benchmarks. Your competitive advantage is no longer which model you choose — it’s the proprietary data only you possess. This convergence is creating a quiet crisis for enterprises. When OpenAI, Anthropic, Google, and others offer near-equivalent capabilities, how do you meaningfully differentiate? A pattern is becoming unmistakable across our portfolio at C10 Labs — and Alembic Technologies’s $145M Series B at a $645M valuation is the latest proof point: Proprietary data + specialized models = durable advantage. Alembic’s work in causal AI — mapping cause-effect relationships unique to each company — shows exactly where value is shifting. They’re even building private supercomputing clusters because, as CEO Tomas Puig puts it, they work with: “the type of data that nobody in the world wants to give somebody else access to.” Alembic isn’t an outlier. This reflects a broader strategic shift: Old paradigm: The race to build bigger, more general models New reality: Specialized AI trained on proprietary, private data Key insight: Your data moat is more valuable than any model subscription And this isn’t just about privacy. Your customer behaviors, operational logic, and historical patterns are irreplaceable assets — no foundation model can replicate them. At ekai, one of the teams we invested in earlier this year, they are solving this by capturing the “tribal knowledge” buried across the enterprise — the unwritten rules of how deals close, why customers churn, and what drives product decisions. Because without your context, even GPT-5 will give you generic answers. Connect with them here: https://lnkd.in/e5jjTkT5. The uncomfortable truth: If you’re using the same AI as your competitors, on generic data, you’ll get generic results. Your AI advantage won’t come from your model budget — it’ll come from your data strategy. The winners: Those who turn proprietary data into proprietary intelligence. So stop asking: “Which LLM should we use?” Start asking: “What unique intelligence can we build that no one else can?” That’s the future we’re building across the C10 Labs portfolio. #AI #AINexus #EnterpriseAI #DataStrategy #CompetitiveAdvantage #C10Labs #AppliedAI ekai Snowflake Moatassim (Mo) Aidrus Patricia Geli C10 Labs David Berlin Beth Porter
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👀 Read our latest blog from our CEO Rachel Tidmarsh DL. Rachel has reflected on a panel discussion she recently participated in at INTERGEO Expo & Conference - Data to Decisions: The Role of AI and Machine Learning in Transforming Large Datasets into Actionable Insights for Better Governance. She says: “There’s no doubt that AI and machine learning are transforming our industry. The scale, speed, and sophistication of these technologies are unlike anything we’ve seen before. Change can sometimes feel daunting, but in truth, it’s also an incredible opportunity. We’ve always adapted to new ways of working, and AI represents the next evolution, one that’s already reshaping how we think about data and decision-making.” 👉 Read the full article here: https://lnkd.in/e-KJ8-TG Woolpert #AI #machinelearning #innovation #insights
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"By using AI to analyse and interpret the aerial survey data we collect, we can now extract insights on everything from road markings and roof types to tree species and street furniture, all at a scale and level of precision that was once unimaginable." Great insights on the expanding capabilities of AI and machine learning within the geospatial industry from Rachel Tidmarsh DL. Read the full blog here: https://lnkd.in/e-KJ8-TG #AI #MachineLearning #Geospatial
👀 Read our latest blog from our CEO Rachel Tidmarsh DL. Rachel has reflected on a panel discussion she recently participated in at INTERGEO Expo & Conference - Data to Decisions: The Role of AI and Machine Learning in Transforming Large Datasets into Actionable Insights for Better Governance. She says: “There’s no doubt that AI and machine learning are transforming our industry. The scale, speed, and sophistication of these technologies are unlike anything we’ve seen before. Change can sometimes feel daunting, but in truth, it’s also an incredible opportunity. We’ve always adapted to new ways of working, and AI represents the next evolution, one that’s already reshaping how we think about data and decision-making.” 👉 Read the full article here: https://lnkd.in/e-KJ8-TG Woolpert #AI #machinelearning #innovation #insights
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React & JS Developer | 3rd Year CSE-AIML Student | Building Secure & Scalable Web Interfaces
Excited to share my latest Medium article — “The Hidden Engine of AI: A Deep Dive into MCP” ⚙️ In this piece, I uncover how the Model Context Protocol (MCP) is transforming AI models into powerful, connected systems — bridging tools, APIs, and data for smarter automation and seamless integration. If you’ve ever wondered how AI actually interacts with the real world — this is for you! 👉 Read here: https://lnkd.in/gqCBN2Wt #AI #MachineLearning #MCP #Technology #ArtificialIntelligence #Medium #Innovation
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2026 Belongs to AI Meaning Builders, Not Model Builders - For the better part of a decade, enterprises have been racing to build bigger models and gather more data, believing scale alone would unlock artificial intelligence at full capacity. Yet despite remarkable breakthroughs in generative AI, most organizations still find themselves stuck at the same frustrating juncture: the last mile between technical capabilities and accurate outputs that agentic systems can be built off of. Models horsepower can be 10X but if it can’t perform at high accuracy, it’s doomed to a life of shelfware. The reason is no longer a mystery. The bottleneck to enterprise AI isn’t data or compute […] - https://lnkd.in/evhncAcU
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2026 Belongs to AI Meaning Builders, Not Model Builders - For the better part of a decade, enterprises have been racing to build bigger models and gather more data, believing scale alone would unlock artificial intelligence at full capacity. Yet despite remarkable breakthroughs in generative AI, most organizations still find themselves stuck at the same frustrating juncture: the last mile between technical capabilities and accurate outputs that agentic systems can be built off of. Models horsepower can be 10X but if it can’t perform at high accuracy, it’s doomed to a life of shelfware. The reason is no longer a mystery. The bottleneck to enterprise AI isn’t data or compute […] - https://lnkd.in/evhncAcU
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2026 Belongs to AI Meaning Builders, Not Model Builders - For the better part of a decade, enterprises have been racing to build bigger models and gather more data, believing scale alone would unlock artificial intelligence at full capacity. Yet despite remarkable breakthroughs in generative AI, most organizations still find themselves stuck at the same frustrating juncture: the last mile between technical capabilities and accurate outputs that agentic systems can be built off of. Models horsepower can be 10X but if it can’t perform at high accuracy, it’s doomed to a life of shelfware. The reason is no longer a mystery. The bottleneck to enterprise AI isn’t data or compute […] - https://lnkd.in/evhncAcU
