Enterprise AI

words.OpenAI unveiled GPT‑5.2, a higher‑performing generative‑AI model aimed at professional tasks such as spreadsheet creation, presentation design, image interpretation, code generation, and extended‑context work. Released via ChatGPT and API, it comes in three tiers—Instant, Thinking, and Pro—tailored to speed, structured tasks, and high‑accuracy demands. GPT‑5.2 leads on benchmarks like SWE‑Bench Pro and GPQA Diamond, though Anthropic’s Opus 4.5 edges it on a narrower coding test. Built on a larger transformer, multimodal embeddings, and a near‑million‑token context window, the model emphasizes improved factuality, safety, and enterprise revenue potential amid intensified AI competition.

OpenAI has named Slack CEO Denise Dresser as chief revenue officer, tasking her with steering the company’s global revenue strategy and deepening enterprise relationships. Dresser, who previously helped scale Salesforce‑owned platforms, will oversee customer success and enterprise segments as OpenAI targets over $20 billion in annualized revenue and a long‑term goal of hundreds of billions by 2030. Facing mounting competition from Google and Anthropic, OpenAI is investing $1.4 trillion in infrastructure and pricing reforms to lock in large corporate accounts and expand its share of the projected $500 billion enterprise‑AI market.

final.Thomson Reuters and Imperial College London have created a five‑year Frontier AI Research Lab to solve enterprise AI deployment challenges. Leveraging Reuters’ curated data and Imperial’s computing power, researchers will co‑train large‑scale models, use retrieval‑augmented generation, and develop agentic, reasoning‑based systems for regulated domains such as law, tax and finance. The lab integrates PhD candidates, legal experts and industry scientists to ensure safety, transparency, compliance and economic impact, aiming to turn frontier research into trustworthy, deployable AI solutions.

words.Amazon launched Nova Forge, a $100,000‑annual service that lets enterprises inject proprietary data into Amazon’s generative‑AI models during early training, offering deeper customization than post‑training fine‑tuning. It supports both Amazon‑owned and open‑weight models but does not provide full training data or compute resources. Targeting firms that want a competitive edge without billion‑dollar R&D, early users include Reddit, Booking.com, and Sony. At AWS re:Invent, Amazon added Nova 2 Pro (advanced reasoning) and Nova 2 Omni (multimodal) models, aiming to grow its modest market share against Anthropic, OpenAI and Google.

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OpenAI has taken an equity stake in Thrive Holdings, a new operating platform launched by Thrive Capital, embedding its engineering, research and product teams within Thrive’s portfolio companies—primarily in accounting, IT services and other “core economy” sectors. The deal ties OpenAI’s equity upside to the growth of these businesses and creates a recurring revenue stream for its AI services, reflecting a broader “circular” strategy that mixes licensing with equity participation. Concurrently, OpenAI will deploy ChatGPT Enterprise to tens of thousands of Accenture employees.

.AI spending hit $252 bn in 2024, fueling a bubble debate. Yet only 5 % of firms profit from AI; they allocate >20 % of digital budgets, pursue transformational change, redesign workflows, and enforce strong governance. Building proprietary models is costly, so successful enterprises diversify across hyperscalers, validate alternatives, and mitigate supply constraints. Best practices focus on high‑impact use cases with measurable ROI, invest in talent, data pipelines, and agile delivery, and embed governance early. Pragmatic, value‑driven AI adoption yields competitive advantage regardless of market hype.

Alibaba’s Qwen AI app achieved 10 million downloads in its first week, surpassing the adoption rates of ChatGPT and others. Unlike Western subscription models, Qwen offers free access and integrates AI into Alibaba’s ecosystem. This “agentic AI” performs tasks across e-commerce, maps, and more. Qwen’s success, fueled by its open-source LLM, poses competitive implications for businesses, especially regarding cost and vendor lock-in. Enterprises must weigh free-access benefits against long-term sustainability and geopolitical dynamics when developing AI strategies.

Enterprise AI adoption faces hurdles due to high infrastructure costs and energy consumption. NTT’s tsuzumi 2, a lean LLM designed for a single GPU, offers a solution. Deployed by Tokyo Online University, it enhances learning support while ensuring data sovereignty. Performance matches larger models in specific domains like finance and healthcare, particularly for Japanese language tasks. This approach prioritizes data security and cost-effectiveness, presenting a viable alternative to resource-intensive LLMs, especially for organizations with specific sector needs and data privacy concerns.

Dell Technologies and NVIDIA are enhancing their AI partnership with updates to the Dell AI Factory with NVIDIA platform at SC25. These enhancements streamline AI workload deployment and management, addressing scalability complexities. Key integrations include NVIDIA’s NIXL library for faster inferencing and support for NVIDIA RTX Blackwell GPUs. The platform now includes Dell Automation Platform for pre-tuned deployments and expanded AI PC options. These updates aim to transition organizations from AI pilots to production deployments with greater confidence and efficiency, leveraging infrastructure, automation, and data tools.

IBM’s report identifies data silos as the primary obstacle to enterprise AI adoption, hindering seamless integration and collaboration. Fragmented data across departments leads to prolonged data cleansing projects, delaying insights and ROI. The report suggests distributed data architectures like data mesh and fabric, alongside “data products,” to improve accessibility. Talent shortages and governance complexities also pose challenges. Success hinges on breaking down silos, democratizing data literacy, and treating data as a strategic asset to scale AI across the organization.