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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

NFT sales exploded from roughly 250 million dollars in 2020 to over 25 billion dollars in 2021, a growth rate exceeding 10,000 percent in a single year that has drawn comparisons to historic speculative manias. Whether this represents a lasting shift in how digital ownership works or a bubble waiting to deflate, the technology underpinning non-fungible tokens merits a closer look at how the market actually functions today.
The fundamental mechanics of NFTs, how they differ from cryptocurrencies, and the current state of adoption across marketplaces and collector categories come first, before the key drivers and structural challenges shaping near-term growth. The research also identifies four areas expected to have the greatest impact on the market from 2022 onward, giving collectors, creators, and enterprise teams a shared vocabulary for a technology still finding its footing.
Early findings point to a market still far from democratised, with trading activity heavily concentrated among a small share of participants, alongside rapid enterprise adoption spanning marketing, brand-building, and new digital presence strategies. Security remains an open question too, with an unregulated environment leaving room for fraudulent activity that could shape how quickly institutional and mainstream adoption accelerates.
<ul><li>Adoption trends across NFT types and leading marketplaces</li><li>Enterprise use cases spanning fan engagement, branding, and digital presence</li></ul>
<figure><img src="https://cdn.twimbit.com/uploads/2022/03/09083659/MicrosoftTeams-image-6.jpg" alt="Uncovering the NFT Global Market report contents"/></figure>

Uncovering the Global NFT Market

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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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NFT sales exploded from roughly 250 million dollars in 2020 to over 25 billion dollars in 2021, a growth rate exceeding 10,000 percent in a single year that has drawn comparisons to historic speculative manias. Whether this represents a lasting shift in how digital ownership works or a bubble waitin

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Uncovering the Global NFT Market

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