Across Australia and New Zealand, finance and operations teams are straining under the weight of manual processes. Closing the books could seem like running a marathon; reconciling accounts and chasing variances consume precious hours that could be well spent analyzing the business.
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Why your enterprise needs MCP to master Agentic AI
Written by Sonakshi Pattnaik
Picture this: Your company has just deployed a dozen AI agents to handle customer service, inventory management and financial approvals. Day one feels like magic – agents are working autonomously, handling complex tasks and delivering results. Then day two arrives. Agent conflicts emerge. Data gets duplicated. Security protocols are bypassed. Your digital workforce has turned into digital chaos.
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Autonomous finance: Turning CFO vision into reality with Copilot and Agentic AI
Written by Kasturi Sinha - Senior Manager (Business Consulting)
The Monday morning blues. Your finance team is already in firefighting mode – chasing down cost spikes, reconciling invoices across three regions and piecing together reports from siloed systems. Spreadsheets are open, Slack is buzzing and deadlines are looming. By the time the team tracks the source of a spend overrun, the impact is already baked into the month-end numbers.
Now, imagine a different Monday.
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How AI agents are collaborating to transform clinical trial analysis
Written by Saachi Talwai – Senior Digital Engineer
In life sciences, speed and rigor often pull in opposite directions, especially in early-stage studies where data is sparse, stakes are high, and every decision must be defensible. At Sonata Software, we bring these forces together with an AI-agentic approach that pairs statistical depth with clinical context.
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Why enterprise leaders must act on Agentic AI now
Written by Sonakshi Pattnaik
Artificial Intelligence has undergone a significant transformation since its inception in the 1950s. From early rule-based systems to today's powerful deep learning models, AI has grown more intelligent and responsive. However, most systems still share one thing in common: they wait for a command. Traditional AI is reactive, performing tasks when prompted, usually within narrow constraints. As a result, they struggle with multi-step reasoning, context awareness and autonomous decision-making.
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