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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Advanced Info Service, Thailand's largest mobile operator, faced a pandemic-era squeeze on its core mobile business even as fixed broadband and enterprise services picked up the slack. AIS is managing that shift across shareholder value, customer experience, and employee experience, with its performance measured against regional rivals in the competitive Thai telecom market.
On the customer side, AIS runs a self-service app using machine learning for personalised offers, a simplified KYC process enabling doorstep SIM delivery, and early moves in standalone 5G network rollout across a growing share of Thailand's population. The operator's loyalty programme and flagship retail concept, featuring an AI robot ambassador, round out the brand-experience picture.
On the employee side, AIS's human capital strategy reshapes its organisation, compensation, and hiring model, alongside digital tools built to support flexible work arrangements and internal engagement, with learning and development investment benchmarked against global telecom peers. Workforce composition and representation at senior levels stand out as an area where the operator still has ground to cover relative to its overall gender balance.
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<li>AIS's revenue mix across mobile, fixed broadband, and enterprise segments</li>
<li>Network experience benchmarks against Thailand's other major operators</li>
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<blockquote>How does a dominant mobile operator sustain growth when its core business slows and new segments must carry the weight?</blockquote>

Advanced Info Service (AIS) PCL

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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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Advanced Info Service, Thailand's largest mobile operator, faced a pandemic-era squeeze on its core mobile business even as fixed broadband and enterprise services picked up the slack. AIS is managing that shift across shareholder value, customer experience, and employee experience, with its perform

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Advanced Info Service (AIS) PCL

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