VeriPark has been honored with a place in the Microsoft AI Business Solutions Inner Circle for the year 2026-2027, recognizing its innovative contributions.

Sonata Software has achieved the prestigious 2026-2027 Microsoft AI Business Solutions Inner Circle Award, highlighting their exceptional role in AI-driven business transformation.

Hitachi Solutions has been inducted into the prestigious Microsoft AI Business Solutions Inner Circle for 2026-2027, recognizing their exceptional sales and innovation.

Sunrise Technologies has been recognized for its sales excellence and innovation, earning a spot in Microsoft’s prestigious Inner Circle for 2026-2027.

Simform has achieved the Microsoft Solutions Partner designation for AI Business Solutions, enhancing its capabilities in enterprise AI deployment.

株式会社ギブリーが、日本マイクロソフト主催のAIエージェントピッチイベントで最優秀賞を獲得。監査業務を1週間から1日に短縮する革新的なエージェントを開発しました。

ITSO has announced the launch of 'ITSO-MS Connect', a comprehensive service that supports businesses in integrating Microsoft's AI products from planning to operation, starting August 19, 2026.

Korcomptenz has been recognized as a Contender in the ISG Provider Lens® study in three categories, showcasing their expertise in AI and cloud services.

RSM US LLP has proudly earned the Microsoft Frontier Partner badge, showcasing its leadership in AI consulting and commitment to delivering impactful AI solutions.

Quisitive has been awarded Microsoft Frontier Partner status, showcasing expertise in AI transformation and secure technology solutions for organizations worldwide.

A recent survey indicates that three out of five business leaders believe AI will replace most white-collar jobs within 18 months. This shift may push workers to gain AI skills or consider trades.

Discover how Stibo Systems is revolutionizing retail experiences by integrating Microsoft AI solutions with robust data management to boost customer satisfaction and conversion rates.

Youseful's recent training on AI agents has yielded impressive results, reducing internal inquiries by 24%. Discover how non-engineers are leading this change.

Alithya Group inc. has been recognized for its outstanding performance and innovation in the AI Business Solutions sector by Microsoft, earning a spot in the 2025-2026 Inner Circle.

KnowledgeNet.ai has joined the Microsoft AI Cloud Partner Program and ISV Success to enhance its application development capabilities and market presence.

Lightbeam Health Solutions has been awarded Microsoft's AI Cloud Partner Program certification for its AI-driven healthcare technology, confirming its commitment to innovative solutions.

Tampa General Hospital unveils new AI-driven ambient listening technology for nurses, enhancing patient care and reducing documentation time. Learn how TGH is transforming nursing workflows.

Circlace recently participated in the AI Hackathon 2025 Spring, fostering next-gen talent through practical knowledge sharing and technical support.

Dynamic Communities announces the AI Agent & Copilot Summit, featuring 65 sessions led by AI experts from March 17-19, 2025 in San Diego.

At CES 2025, MAXHUB earned the Microsoft AI Innovation Award, recognizing its advanced Teams Rooms solutions and commitment to enhancing collaborative technology.

Microsoft Research has announced the release of Phi-4-reasoning-plus, an open-weight language model built for tasks requiring deep, structured reasoning. Building on the architecture of the previously released Phi-4, the new model integrates supervised fine-tuning and reinforcement learning to deliver improved performance on benchmarks in mathematics, science, coding, and logic-based tasks. Phi-4-reasoning-plus is a 14-billion parameter dense decoder-only Transformer model that emphasizes quality over scale. Its training process involved 16 billion tokens—about 8.3 billion of them unique—drawn from synthetic and curated web-based datasets. A reinforcement learning (RL) phase, using only about 6,400 math-focused problems, further refined the model’s reasoning capabilities. The model has been released under a permissive MIT license—enabling its use for broad commercial and enterprise applications, and fine-tuning or distillation, without restriction—and is compatible with widely used inference frameworks including Hugging Face Transformers, vLLM, llama.cpp, and Ollama. Microsoft provides detailed recommendations on inference parameters and system prompt formatting to help developers get the most from the model. The model’s development reflects Microsoft’s growing emphasis on training smaller models capable of rivaling much larger systems in performance. Despite its relatively modest size, Phi-4-reasoning-plus outperforms larger open-weight models such as DeepSeek-R1-Distill-70B on a number of demanding benchmarks. On the AIME 2025 math exam, for instance, it delivers a higher average accuracy at passing all 30 questions on the first try (a feat known as “pass@1”) than the 70B parameter distillation model, and approaches the performance of DeepSeek-R1 itself, which is far larger at 671B parameters. To achieve this, Microsoft employed a data-centric training strategy. During the supervised fine-tuning stage, the model was trained using a curated blend of synthetic chain-of-thought reasoning traces and filtered high-quality prompts. A key innovation in the training approach was the use of structured reasoning outputs marked with special <think> and </think> tokens. These guide the model to separate its intermediate reasoning steps from the final answer, promoting both transparency and coherence in long-form problem solving. Following fine-tuning, Microsoft used outcome-based reinforcement learning—specifically, the Group Relative Policy Optimization (GRPO) algorithm—to improve the model’s output accuracy and efficiency. The RL reward function was crafted to balance correctness with conciseness, penalize repetition, and enforce formatting consistency. This led to longer but more thoughtful responses, particularly on questions where the model initially lacked confidence. Phi-4-reasoning-plus is intended for use in applications that benefit from high-quality reasoning under memory or latency constraints. It supports a context length of 32,000 tokens by default and has demonstrated stable performance in experiments with inputs up to 64,000 tokens. It is best used in a chat-like setting and performs optimally with a system prompt that explicitly instructs it to reason through problems step-by-step before presenting a solution. Microsoft positions the model as a research tool and a component for generative AI systems rather than a drop-in solution for all downstream tasks. Developers are advised to carefully evaluate performance, safety, and fairness before deploying the model in high-stakes or regulated environments. Phi-4-reasoning-plus has undergone extensive safety evaluation, including red-teaming by Microsoft’s AI Red Team and benchmarking with tools like Toxigen to assess its responses across sensitive content categories. According to Microsoft, this release demonstrates that with carefully curated data and training techniques, small models can deliver strong reasoning performance—and democratic, open access to boot. Here’s a revised version of the enterprise implications section in a more technical, news-style tone, aligning with a business-technology publication: The release of Microsoft’s Phi-4-reasoning-plus may present meaningful opportunities for enterprise technical stakeholders managing AI model development, orchestration, or data infrastructure. For AI engineers and model lifecycle managers, the model’s 14B parameter size coupled with competitive benchmark performance introduces a viable option for high-performance reasoning without the infrastructure demands of significantly larger models. Its compatibility with frameworks such as Hugging Face Transformers, vLLM, llama.cpp, and Ollama provides deployment flexibility across different enterprise stacks, including containerized and serverless environments. Teams responsible for deploying and scaling machine learning models may find the model’s support for 32k-token contexts—expandable to 64k in testing—particularly useful in document-heavy use cases such as legal analysis, technical QA, or financial modeling. The built-in structure of separating chain-of-thought reasoning from the final answer could also simplify integration into interfaces where interpretability or auditability is required. For AI orchestration teams, Phi-4-reasoning-plus offers a model architecture that can be more easily slotted into pipelines with resource constraints. This is relevant in scenarios where real-time reasoning must occur under latency or cost limits. Its demonstrated ability to generalize to out-of-domain problems, including NP-hard tasks like 3SAT and TSP, suggests utility in algorithmic planning and decision support use cases beyond those explicitly targeted during training. Data engineering leads may also consider the model’s reasoning format—designed to reflect intermediate problem-solving steps—as a mechanism for tracking logical consistency across long sequences of structured data. The structured output format could be integrated into validation layers or logging systems to support explainability in data-rich applications. From a governance and safety standpoint, Phi-4-reasoning-plus incorporates multiple layers of post-training safety alignment and has undergone adversarial testing by Microsoft’s internal AI Red Team. For organizations subject to compliance or audit requirements, this may reduce the overhead of developing custom alignment workflows from scratch. Overall, Phi-4-reasoning-plus shows how the reasoning craze kicked off by the likes of OpenAI’s “o” series of models and DeepSeek R1 is continuing to accelerate and move downstream to smaller, more accessible, affordable, and customizable models. For technical decision-makers tasked with managing performance, scalability, cost, and risk, it offers a modular, interpretable alternative that can be evaluated and integrated on a flexible basis—whether in isolated inference endpoints, embedded tooling, or full-stack generative AI systems.

BIPROGYが社内でのAI活用事例をMicrosoft AI Tourで発表。全社的な取り組みが業務効率化を実現。

ベクストがMicrosoft AI Co-Innovation Lab KOBEの支援を受け、AIエージェント型テキストマイニング技術を開発。専門家以外でも利用可能な新たな分析手法に挑戦しています。