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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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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Enterprise business has become a central growth engine for Asia-Pacific telecom operators, as non-connectivity revenue streams, cloud, data centres, IoT, managed services, and cybersecurity, reshape operator business models through the first half of 2025. This marks a structural repositioning of telcos from pure connectivity providers into digital infrastructure and technology enablers serving enterprise and government clients.
Enterprise revenue contribution is broadening geographically across APAC markets, reflecting widespread corporate demand for cloud migration, AI integration, cybersecurity, and digital transformation services rather than growth concentrated in one or two economies. A clear performance divide has also emerged: operators that scaled digital platforms and monetised technology adjacencies pulled ahead, while those still reliant on legacy voice and data services lagged behind peers.
Execution quality, capital discipline, and the speed of strategic pivot stand out as the deciding factors separating enterprise growth leaders from laggards, offering a comparative view of which adjacent technology bets are translating into durable enterprise revenue across the region.
<ul><li>Enterprise revenue share of total telecom industry revenue in H1-2025</li><li>Cloud, IoT, managed services, and cybersecurity as fastest-growing enterprise segments</li></ul>
<blockquote>Which telcos are converting enterprise technology bets into durable revenue, and which are still selling connectivity to businesses that want a platform?</blockquote>

APAC telcos enterprise business update: H1-2025

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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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FutureTelco

Enterprise business has become a central growth engine for Asia-Pacific telecom operators, as non-connectivity revenue streams, cloud, data centres, IoT, managed services, and cybersecurity, reshape operator business models through the first half of 2025. This marks a structural repositioning of tel

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APAC telcos enterprise business update: H1-2025

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