Insights

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AI is becoming standard in CX; execution is becoming the differentiator. The competitive gap is shifting from who has the most AI features to who can connect intelligence across customer context, enterprise systems and workflows to resolve outcomes end to end.

The Twimbit AI-CX Vendor Landscape 2026 therefore evaluates vendors across both AI capability and innovation and market execution and adoption, revealing who can turn AI from impressive technology into scalable enterprise impact.

AI-CX Vendor Landscape 2026

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Asia's telecommunications industry has weathered a demanding stretch of disruption, regulatory change and rapid technology investment, and how the sector has evolved through that period comes into focus here. Manoj Menon, Founder and Managing Director of Twimbit, works through three central questions shaping how telecom operators across the region are repositioning themselves for the years ahead.

Telecom operators first adapted their operations, customer engagement models and network investment priorities in response to pandemic-era disruption, before the broader competitive landscape comes into view. Regulatory developments across Asian markets, the technology investments operators are prioritising, and where genuine innovation is emerging versus where operators are simply keeping pace with peers all factor in.

A third question centers on 5G — examining not just the technical rollout of next-generation mobile networks but its broader ripple effects across industries that rely on connectivity, from manufacturing and logistics to healthcare and smart infrastructure. Both the opportunities 5G unlocks for telecom operators seeking new revenue streams and the operational challenges that come with deploying and monetising the technology at scale come under scrutiny.

Framed as an accessible video immersion rather than a dense written report, Menon's analysis offers a concise entry point into the state of Asia's telecom industry for executives, investors and technology leaders who want a grounded, expert-led overview without wading through a full market report.

<blockquote>How is Asia's telecom industry using this period of disruption to reposition itself for a 5G-driven future?</blockquote>

State of Telecoms in Asia

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Managed access to Chinese frontier AI models is reshaping enterprise AI strategy. The arrival of Moonshot AI’s Kimi K3 on Amazon Bedrock signals a shift in how large organizations approach AI model selection, procurement, and governance. Enterprises can now leverage advanced, open-weight Chinese AI through familiar AWS APIs, billing, and security frameworks, reducing operational friction and enabling new options for cost management and deployment flexibility.Kimi K3’s integration with Amazon Bedrock introduces prompt caching and supports multi-model architectures, allowing organizations to distribute workloads between open-weight and proprietary models based on cost, complexity, and business needs. This development prompts a re-examination of AI infrastructure, balancing performance, cost efficiency, and governance across diverse use cases.Key considerations for CIOs, CFOs, and CISOs include model provenance, licensing, regulatory compliance, and data residency—especially as regional availability and legal obligations differ across the United States, Europe, and Asia Pacific. The managed environment provides strong infrastructure-level security, but enterprises must still evaluate the governance of Chinese-origin models before adoption.<ul><li>Explores managed deployment options for open-weight AI on AWS</li><li>Assesses security, compliance, and procurement implications</li><li>Compares regional constraints and opportunities for global enterprises</li></ul><blockquote>How will the availability of Chinese frontier AI models like Kimi K3 on Amazon Bedrock change enterprise AI procurement and governance strategies?</blockquote>

The Managed Front Door to Chinese Frontier AI: What Kimi K3 on Amazon Bedrock Means for Enterprise Buyers

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AI compute and chips are undergoing rapid transformation as enterprises shift from a focus on peak training performance to a more nuanced approach that weighs workload requirements, utilisation economics, and infrastructure flexibility. The landscape now extends beyond general-purpose GPUs to include hyperscaler-designed silicon, specialised processors, and regionally controlled architectures. This evolution is driven by growing demand for inference, AI agents, real-time applications, and distributed deployment models, all of which are reshaping how compute is sourced and deployed.Key considerations now include not only processing power, but also cost per workload, energy efficiency, software compatibility, supply resilience, and long-term adaptability. Enterprises must evaluate chip architectures and suppliers across technical, financial, strategic, and operational dimensions, with an increasing emphasis on matching infrastructure to specific application needs rather than standardising on a single chip type.<ul><li>Coverage of general-purpose GPUs, custom cloud silicon, inference specialists, and regional AI chip providers</li><li>Analysis of workload-driven procurement strategies for AI infrastructure</li><li>Examination of supplier risk, software lock-in, and multi-chip strategies</li></ul>For CIOs and technology leaders, the central question is no longer which chip is the fastest, but which combination of compute, software, and supplier ecosystem best supports evolving business and AI workload requirements.<blockquote>How are enterprise AI infrastructure decisions shifting from performance-led to workload-led strategies, and what forces are redefining the AI chip market?</blockquote>

AI Compute & Chips​: Trends and analysis in a fast-evolving market​

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Asia's telecommunications industry has weathered a demanding stretch of disruption, regulatory change and rapid technology investment, and how the sector has evolved through that period comes into focus here. Manoj Menon, Founder and Managing Director of Twimbit, works through three central question

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State of Telecoms in Asia

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