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

EE-Part-2

1-15

Building on the idea that time is the currency of value in the experience economy, organizations can design experiences that are both functionally efficient and emotionally resonant, turning everyday interactions into lasting brand loyalty. Beyond simply defining the concept, the practical question is how businesses should actually stage these experiences across digital and physical touchpoints alike.Two complementary paths stand out — delivering 'time well saved' through frictionless digital functionality such as hyper-personalized recommendations and flexible payment options, and cultivating 'time well spent' by targeting consumers' psychological basic needs for predictability, acceptance, and competence. Sensorial triggers and hybrid online-offline experiences can deepen emotional engagement with a brand, turning routine transactions into moments customers actively remember and share.Examples from grocery retail, banking, aviation, and hospitality across Asia show how leading brands blend functional convenience with moments of delight — from personalized in-store recommendations and hyper-personalized product pairings to life-stage guidance in financial services and immersive airport environments that double as lifestyle destinations rather than mere transit points.<ul><li>Functional experience design: personalization, omnichannel journeys, and frictionless payments</li><li>Emotional experience design: psychological basic needs and sensorial triggers</li><li>Case examples spanning grocery retail, banking, aviation, and hospitality</li></ul><blockquote>How can businesses combine good functionality with genuine emotional resonance to win in the experience economy?</blockquote>

The experience economy – Part 2

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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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theexperienceeconomy

Building on the idea that time is the currency of value in the experience economy, organizations can design experiences that are both functionally efficient and emotionally resonant, turning everyday interactions into lasting brand loyalty. Beyond simply defining the concept, the practical question 

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The experience economy – Part 2

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