Javed is a senior editor at TIME, based in the London bureau. Javed is a senior editor at TIME, based in the London bureau. How do you see your clients adopting AI and grappling with the rapid changes it is bringing? CEOs have identified that AI is simple to try and hard to scale, and that's why they come to Accenture. And you can see that in the explosive growth of our advanced AI practice over the past couple of years.

--Modern cyber attacks unfold through multiple stages, requiring defenders to dynamically prioritize mitigations under uncertainty. While game-theoretic models capture attacker-defender interactions, existing approaches often rely on static assumptions and lack integration with real-time threat intelligence, limiting their adaptability. This paper presents Cy-GATE, a game-theoretic framework modeling attacker-defender interactions, using large language models (LLMs) with retrieval-augmented generation (RAG) to enhance tactic selection and patch prioritization. Applied to a two-agent scenario, CyGATE frames cyber conflicts as a partially observable stochastic game (POSG) across Cyber Kill Chain stages. Both agents use belief states to navigate uncertainty, with the attacker adapting tactics and the defender re-prioritizing patches based on evolving risks and observed adversary behavior . The framework's flexible architecture enables extension to multi-agent scenarios involving coordinated attackers, collaborative defenders, or complex enterprise environments with multiple stakeholders. The evolving cybersecurity landscape presents increasingly sophisticated threats that necessitate adaptive, proactive defense strategies. Patch management, a cornerstone of cyber defense, requires intelligent prioritization of vulnerabilities under resource constraints such as maintenance windows and operational cost [1] [2] . However, traditional scoring systems like common vulnerability scoring system (CVSS) [3] fail to capture the evolving nature of cyber threats, where attackers adapt their strategies based on defender actions. Game theory provides a structured framework for modeling attacker-defender interactions [4], with chained or multistage games particularly suited to representing complex attack progressions along the Cyber Kill Chain (CKC) [5][6][7]. These models allow defenders to reason about long-term risks and preempt cascading compromises. Despite these advancements, existing models remain constrained by fixed strategies, static payoff structures, and minimal integration of threat intelligence, failing to dynamically prioritize vulnerabilities based on evolving exploitation trends [8]. Traditional game-theoretical approaches typically use predefined rules to analyze strategies, hence are limited in dynamic cyber environments where adversaries continuously adapt, operate under uncertainty, and employ unpredictable tactics [9].

At the same time, hosted services like generative AI and tailored industry solutions can help companies quickly launch applications and grow the business. To get the most out of these services, companies are turning to cloud optimization--the process of selecting and allocating cloud resources to reduce costs while maximizing performance. But despite all the interest in the cloud, many workloads remain stranded on-premises, and many more are not optimized for efficiency and growth, greatly limiting the forward momentum. Companies are missing out on a virtuous cycle of mutually reinforcing results that comes from even more efficient use of the cloud. Organizations can enhance security, make critical workloads more resilient, protect the customer experience, boost revenues, and generate cost savings.

Reinforcement Learning from Human Feedback (RLHF) has played a crucial role in the success of large models such as ChatGPT. RLHF is a reinforcement learning framework which combines human feedback to improve learning effectiveness and performance. However, obtaining preferences feedback manually is quite expensive in commercial applications. Some statistical commercial indicators are usually more valuable and always ignored in RLHF. There exists a gap between commercial target and model training. In our research, we will attempt to fill this gap with statistical business feedback instead of human feedback, using AB testing which is a well-established statistical method. Reinforcement Learning from Statistical Feedback (RLSF) based on AB testing is proposed. Statistical inference methods are used to obtain preferences for training the reward network, which fine-tunes the pre-trained model in reinforcement learning framework, achieving greater business value. Furthermore, we extend AB testing with double selections at a single time-point to ANT testing with multiple selections at different feedback time points. Moreover, we design numerical experiences to validate the effectiveness of our algorithm framework.

Although AI was recognized as strategically important before generative AI became prominent, our 2022 survey found CIOs' ambitions limited: while 94% of organizations were using AI in some way, only 14% were aiming to achieve "enterprise-wide" AI by 2025. By contrast, the power of generative AI tools to democratize AI--to spread it through every function of the enterprise, to support every employee, and to engage every customer --heralds an inflection point where AI can grow from a technology employed for particular use cases to one that truly defines the modern enterprise. As such, chief information officers and technical leaders will have to act decisively: embracing generative AI to seize its opportunities and avoid ceding competitive ground, while also making strategic decisions about data infrastructure, model ownership, workforce structure, and AI governance that will have long-term consequences for organizational success. This report explores the latest thinking of chief information officers at some of the world's largest and best-known companies, as well as experts from the public, private, and academic sectors. It presents their thoughts about AI against the backdrop of our global survey of 600 senior data and technology executives.

I recently had a conversation with a senior executive who had just landed at a new organization. He had been trying to gather new data insights but was frustrated at how long it was taking. After walking his executive team through the data hops, flows, integrations, and processing across different ingestion software, databases, and analytical platforms, they were shocked by the complexity of their current data architecture and technology stack. It was obvious that things had to change for the organization to be able to execute at speed in real time. Data is a key component when it comes to making accurate and timely recommendations and decisions in real time, particularly when organizations try to implement real-time artificial intelligence.

It's time for Fortune 1000 companies to rethink their investments in data, analytics, and AI. Of course, companies should be investing in these critical business capabilities and differentiators. What they need to take a hard look at is how they're investing, and whether these investments are leading to the kinds of gains and the levels of business value that companies are aspiring to achieve. Responses to a recently released survey of Fortune 1000 and global data and business leaders show that data, analytics, and AI efforts have stalled -- or even backslid. Since 2012, when I launched the survey to investigate organizations' investments in data initiatives, the survey has expanded into related topics such as analytics, AI and machine learning, the role of the Chief Data Officer, and data ethics.

Back in 2019, Gartner predicted that the vast majority of AI projects would continue to fail: Only 53% of projects make it from prototypes to production, and 85% of those blow up. And yet, AI adoption has only accelerated. In an IBM study, 42% organizations reported they're exploring AI, and AI adoption is growing steadily, up four points from 2021. "Very few AI products become successful in creating value for companies, even though companies invest quite a lot of manpower and resources," says Ali Riza Kuyucu, global head of data and analytics at Blue.cloud. "But driving efficiencies through artificial intelligence requires constant monitoring and improvement, or what we call continuous AI -- keeping and sustaining the business value of AI for an organization over a longer period."

IT executives must look beyond cost savings to new forms of operational excellence and seek technologies that can help them optimize resilience, scale industry-specific solutions and product delivery, and pioneer new forms of engagement, according to the 10 top strategic technology trends for 2023 unveiled at Gartner's IT Symposium/Xpo 2022. These include multiple forms of wireless, artificial intelligence, and sustainability, according to Frances Karamouzis, distinguished vice president and analyst at Gartner, and external events are making IT pros' decisions about them even more difficult. "Depending on what region of the world you are in there are lots of looming issues such as a potential recession, supply chain concerns, the war in Ukraine and that impact, as well as energy-related issues," Karamouzis said. IT executives must focus on continuing to accelerate digital transformation and consider possible use both for technologies that can be applied immediately and those that are on the horizon. With that as background, Gartner's top 10 strategic technology trends for 2023 looks like this: No single wireless technology will dominate, but enterprises will use a variety of wireless solutions to support a range of environments, from Wi-Fi in the office, services for mobile devices, low-power protocols, and even radio connectivity, Gartner stated.

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