AI Automation Challenges: Navigating Complexity with Databricks and Glean

For the last two years, enterprise AI strategies have centered on one question: which model should we use? That phase is coming to an end. As organizations move from pilots to scaled deployments, it’s becoming clear that models alone don’t create value. Real impact comes from AI that can act reliably within enterprise workflows, data environments, and governance structures. Meta’s acquisition of Manus highlights this shift, from copilots to agents, and from model performance to execution at scale. Read the blog to understand why orchestration, not model choice, will define the next phase of enterprise AI. https://okt.to/SWKTLB Reach out to discuss this topic in depth: Priya Bhalla #EnterpriseAI #AIAgents #GenerativeAI #DigitalTransformation #AIStrategy #EnterpriseTechnology

2025 saw more AI experiments make it into production. 2026 will be the year organisations figure out how to make it all work together. I’m increasingly convinced that orchestration is going to be a defining theme. Cradle to grave, from user need to working outcome. Not isolated capabilities, but connected systems that deliver something tangible. Alongside this, expect a quiet movement back towards more traditional machine learning. Classification, prediction, anomaly detection. Not everything needs a language model, and plenty of use cases are better served by approaches that have been working reliably for years. Organisations are also starting to properly grasp something fundamental: none of this works without solid data management, governance and control. The AI ambitions that seemed transformational are increasingly looking like automation initiatives and business workflow reimagination. Which isn’t a criticism. That’s where the real value sits. I’d expect investment to be more evenly distributed in 2026. Less of a singular focus on LLMs, more balanced spend across agents, data governance, traditional ML capabilities, and the infrastructure to tie it all together. There’s some irony here. In places we’re retreading historical RPA efforts. But that’s fine. We’re building on lessons learned. LLMs will settle into what they’re genuinely good at for now: knowledge discovery, research, summarisation, drafting content, writing code. Which is plenty if you really know how to maximise their multi modal capabilities in your every day working practices! But then again, I could be wrong. 🙃 #ai #enterprise #webuildai

📈 Good AI starts with good data Most AI conversations focus on models. In practice, data quality matters more than model choice. Common blockers we see: • Data spread across too many tools • Unstructured documents and conversations • No clear ownership or access control Before AI can automate decisions, it needs: • Clean inputs • Contextual memory • Real time access to business systems Teams that invest in data readiness see faster AI adoption and higher ROI. At UtomateAI, we help companies connect their data, workflows, and AI so automation is practical and secure from day one. Strong data foundations turn AI from an experiment into infrastructure. #DataEngineering #AIInfrastructure #Automation #EnterpriseAI #UtomateAI

In recent #AI discussions, I notice a recurring pattern. The focus often lands on the model. Accuracy, latency, benchmarks. Those things matter, but they are rarely where enterprise outcomes are decided. In real delivery environments, AI shows up as part of a larger flow. A request enters the system. Data is fetched, filtered, and shaped. One or more models are invoked. Business rules apply guardrails. Confidence thresholds determine whether automation proceeds or a human steps in. Downstream systems still need clean inputs, audit trails, and predictable behavior. This is where orchestration becomes the real work. #Orchestration is not about chaining models together. It is about managing decision paths. Knowing when to trust an output, when to fall back to deterministic logic, and when to stop and ask for approval. It is about handling partial failures gracefully and ensuring that one slow or uncertain step does not destabilize the entire process. In practice, I have seen systems with strong models struggle because orchestration was weak. Retries were blind. Escalations were unclear. Monitoring focused on model metrics but missed business impact. Trust eroded quietly. When orchestration is done well, AI becomes dependable. Not perfect, but predictable. Teams understand its limits. Stakeholders know where control sits. In enterprise environments, success with AI is less about intelligence in isolation and more about how well that intelligence is governed, sequenced, and contained. #AIArchitecture #EnterpriseAI

Will AI Agents grow 'Business Sentience' in 2026? AI Agents witnessed amazing growth in 2025. But as we settle into 2026, the market sentiment has shifted from excitement to Scrutiny. We know that "Chatting" with data isn't enough. The goal is "Acting" on data. However, the early promise of universal agents has met the hard reality of enterprise complexity. The consensus among top analysts is that 2026 is the year we move from general-purpose assistants to Specialized Agents. The data supports this pivot: - Gartner - "By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously" - Deloitte - "In 2026, businesses will likely work on their readiness to orchestrate agents with a specific degree of autonomy (estimating AI Agents market to reach $8.5 billion by 2026)" While the predictions are bullish, the on-ground reality is different. Many leaders I speak with, are finding that generic agents struggle with deep business context. They find agents have basic IQ (Intelligence) but lack BQ (Business Quotient). At Circulos AI, we believe the winner of 2026 won't be the company with the smartest model. It will be the company with the most Aligned model. We are moving beyond simple automation to building agents that can decode and replicate your business sentiments. This requires a fundamental shift in how we architect AI—moving from static prompts to building dynamic learning systems and much more. Checkout how we are solve these gaps here: circulos.co #AIStrategy #AgenticAI #EnterpriseTech #FutureOfWork #2026Predictions #CirculosAI

Raw data doesn’t drive growth. 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 𝗱𝗼.   Generative AI is only as powerful as the data pipelines behind it. Governed ingestion, real-time context, and automation turn data into 𝗱𝗮𝗶𝗹𝘆, 𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 not static reports.   When pipelines power intelligence, AI stops analyzing and starts 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴.   Want AI that works where decisions happen? Contact us: contactus@pibythree.com for more information Visit: https://genaiinabox.ai/   #GenerativeAI #DataEngineering #DecisionIntelligence #EnterpriseData #AIAtScale #Automation #GenAIInAction #PiByThree  

𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗶𝘁’𝘀 𝗮 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺. Most teams experimenting with AI arrive at the same conclusion sooner or later: results don’t come from the model in isolation. They come from everything around it working together. None of this is entirely new. What is new is how these layers are now being composed to handle real operational work, not demos. A practical AI stack usually involves: • Interpreting intent where interaction begins • Establishing context before decisions are made • Guiding agent behavior rather than scripting rigid paths • Planning and reasoning from live signals • Executing through tools, APIs, and enterprise systems • Learning from outcomes to continuously improve • Sitting on infrastructure that can sustain scale without fragility When these elements are aligned, AI stops feeling experimental. It behaves more like an operating system for work — reliable, repeatable, and end-to-end. No hype. No reset button. Just the structure that allows AI to actually run inside enterprises today. #AI #EnterpriseAI #AgenticAI #Automation #AIArchitecture

🔎 Trend Alert: The Shift from Monolithic AI to Multi-Agent Orchestration The AI landscape is undergoing a fundamental architectural shift. Organizations are moving beyond single, monolithic models toward multi-agent systems, where specialized AI agents collaborate to solve complex, end-to-end problems. At the center of this transformation is the orchestration layer—the control plane that governs planning, coordination, execution, and feedback. In modern agentic AI systems: Individual agents focus on specific capabilities such as reasoning, data retrieval, validation, or tool execution The orchestration layer dynamically assigns tasks, manages dependencies, and enforces governance Machine learning models provide prediction, optimization, and domain intelligence across the workflow This approach reflects how effective human teams operate and enables: • Greater scalability and modularity • Improved reliability, observability, and auditability • Faster adaptation to changing goals and data • Enterprise-ready automation with built-in control mechanisms Key takeaway: The differentiator in next-generation AI systems is no longer the model alone, but the architecture that enables coordinated intelligence at scale. As we move into 2026, organizations that invest in agent orchestration—integrated with ML and real-time data—will be better positioned to transition from experimental AI deployments to production-grade, autonomous systems. The future of AI lies not in isolated models, but in well-orchestrated, goal-driven agent ecosystems. #AgenticAI #MultiAgentSystems #AIArchitecture #OrchestrationLayer #MachineLearning #EnterpriseAI #DigitalTransformation

AI at Scale: What’s Driving the Next Wave of Enterprise Value By 2026, AI will shift from experimentation to enterprise-wide deployment, driving measurable value in areas like customer service and engineering. Organizations must overcome integration and governance hurdles while treating AI as foundational infrastructure. Strategic focus on operational excellence, data readiness, and responsible workforce planning will separate hype from real impact in the next phase of enterprise AI. #EnterpriseAI #AI2026 #OperationalExcellence #AIInfrastructure #AIDeployment #ScalableAI #AIInProduction #AIandROI #AITransformation #AILeadership #FutureOfWork #AIIntegration Post link: https://lnkd.in/gfXK8EqX Source: https://lnkd.in/gNpPR-aX AND https://lnkd.in/giAVQZWh

"The AI Mesh approach brings together domain experts and AI specialists in a distributed model that embeds intelligence directly into business processes." Extreme Networks CTO of EMEA Markus Nispel shares why enterprises that embrace #AIMesh will be positioned to invent the customer experiences of tomorrow.