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Weatherford Teams Up with AIQ for AI-Powered Systems

Weatherford International plc said it has signed a strategic memorandum of understanding (MoU) with Abu Dhabi-based artificial intelligence firm AIQ. The collaboration aims to integrate Weatherford’s software and hardware solutions with AIQ’s AI-driven systems, Weatherford said in a news release. The combination will enable operators to “optimize their production workflows, reduce downtime, and significantly enhance […]

Weatherford International plc said it has signed a strategic memorandum of understanding (MoU) with Abu Dhabi-based artificial intelligence firm AIQ.

The collaboration aims to integrate Weatherford’s software and hardware solutions with AIQ’s AI-driven systems, Weatherford said in a news release. The combination will enable operators to “optimize their production workflows, reduce downtime, and significantly enhance operational efficiency across global oil and gas facilities,” the company added.

Weatherford President and CEO Girish Saligram said, “We are excited to partner with AIQ to bring innovative, AI-driven solutions to the oil and gas industry. This strategic partnership allows us to deliver cutting-edge technologies that empower our customers to maximize their operational efficiency, enhance automation, and reduce costs. By combining our strengths, we are leading the way in helping operators modernize their workflows and achieve greater success in today’s rapidly evolving energy landscape”.

Magzhan Kenesbai, Acting Managing Director of AIQ, said, “This partnership marks another step in AIQ’s mission to build partnerships that accelerate the deployment of impactful AI systems across the energy value chain. By integrating our advanced AI-driven tools with Weatherford’s energy-specific technology, we are driving greater efficiencies to the industry through the development of scalable, automated applications. Together, we are set to empower operators to optimize their workflows, reduce downtime, and achieve unparalleled operational excellence”.

Further, Weatherford’s Universal Normalizer will work with AIQ’s capabilities to harmonize multi-asset data, combining operational and financial analysis into a unified, API-supported data model. The combination will “drive smarter decision-making and streamline operations across facilities,” the company said.

New CFO Named

Meanwhile, Weatherford appointed Anuj Dhruv as Chief Financial Officer of the company.

Saligram said, “I am pleased to welcome Anuj to Weatherford. With fresh perspective and proven expertise, Anuj will enhance our leadership team and help position Weatherford to lead confidently through the next phase of our journey. His experience across multiple industries and leadership roles in finance will help shape Weatherford’s focus on delivering high returns for our shareholders. I would like to thank Arun Mitra for his contributions during his time with Weatherford and wish him the best for the future”.

According to a separate statement, Dhruv brings more than two decades of diverse experience in global finance, strategy, and transformation roles across the technology, energy, and chemicals industries. Most recently, he served as vice president of finance and strategy for the Global Olefins and Polyolefins segment at LyondellBasell, where he was responsible for driving performance, investment strategies, and transformation initiatives across a $29 billion revenue segment. Dhruv’s background includes strategic leadership at Schlumberger and Microsoft, with a track record of optimizing financial performance, leading complex transactions, and building high-performing teams, the company said.

Weatherford describes itself as a company delivering innovative energy services that integrate proven technologies with advanced digitalization to create sustainable. According to the company, it conducts business in approximately 75 countries and has approximately 19,000 team members representing more than 110 nationalities and 330 operating locations.

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Weatherford Teams Up with AIQ for AI-Powered Systems

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SLB Logs Lower Profit for Q1

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Offshore energy security is national security

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Woodside Posts $3.32B Revenue for Q1

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Deep Data Center: Neoclouds as the ‘Picks and Shovels’ of the AI Gold Rush

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Soluna Computing: Innovating Renewable Computing for Sustainable Data Centers

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Quiet Genius at the Neutral Line: How Onics Filters Are Reshaping the Future of Data Center Power Efficiency

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Colorado Eyes the AI Data Center Boom with Bold Incentive Push

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Wonder Valley and the Great AI Pivot: Kevin O’Leary’s Bold Data Center Play

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John Deere unveils more autonomous farm machines to address skill labor shortage

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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