In the context of global sustainability mandates, corporate carbon disclosure has emerged as a critical mechanism for aligning business strategy with environmental responsibility. The Carbon Disclosure Project (CDP) hosts the world's largest longitudinal dataset of climate-related survey responses, combining structured indicators with open-ended narratives, but the heterogeneity and free-form nature of these disclosures present significant analytical challenges for benchmarking, compliance monitoring, and investment screening. This paper proposes a novel decision-support framework that leverages large language models (LLMs) to assess corporate climate disclosure quality at scale. It develops a master rubric that harmonizes narrative scoring across 11 years of CDP data (2010-2020), enabling cross-sector and cross-country benchmarking. By integrating rubric-guided scoring with percentile-based normalization, our method identifies temporal trends, strategic alignment patterns, and inconsistencies in disclosure across industries and regions. Results reveal that sectors such as technology and countries like Germany consistently demonstrate higher rubric alignment, while others exhibit volatility or superficial engagement, offering insights that inform key decision-making processes for investors, regulators, and corporate environmental, social, and governance (ESG) strategists. The proposed LLM-based approach transforms unstructured disclosures into quantifiable, interpretable, comparable, and actionable intelligence, advancing the capabilities of AI-enabled decision support systems (DSSs) in the domain of climate governance.

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Generative AI is the biggest and hottest trend in AI (Artificial Intelligence) at the start of 2023. While generative AI has been around for several years, the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities. Every business wants to get on board with ChatGPT, to implement it, operationalize it, and capitalize on it. It is important to realize that the usual "hype cycle" rules prevail in such cases as this. First, don't do something just because everyone else is doing it – there needs to be a valid business reason for your organization to be doing it, at the very least because you will need to explain it objectively to your stakeholders (employees, investors, clients).

AI strategy defines a roadmap for integrating AI into business to enhance operational efficiency. Artificial intelligence can be used to make efficient business products and services. It can optimize business processes by automating repetitive tasks. But to actualize the AI potential, an organization needs a strategic plan to determine its AI maturity, list the challenges and track its progress. AI market size was about $330 billion in 2021, and it would be approximately $1400 billion in 2029, growing at a CAGR of 20.1 %.

AI (Artificial Intelligence) is rapidly advancing, and it's going to change business forever. AI can give organizations a competitive edge in the marketplace by automating tasks and making better decisions. You've got to know about the implications of AI for business strategy, just like with any new technology. As well as the ethical and legal considerations organizations need to consider, this article will explore how AI could impact business operations and decision-making. Aside from that, it's about how companies can get an edge in the market by implementing AI and developing a strategy for it.

The board dinner event, "The Board's role in strategy, governing ecosystems and artificial intelligence", held in Stockholm, Sweden in mid November 2019, was co-organized by two board research projects: SISU Boards led by Professor Mats Magnusson of the Royal Institute of Technology (KTH) and 4Boards.ai The event, as well as both research projects, are co-sponsored by Vinnova – the Swedish Innovation Agency. The event was divided into two sets of activities: 1) speaker presentations and 2) roundtable discussions. Speakers included two globally leading experts, Anastassia Lauterbach and Sebastian Herzog, as well as Researchers Chairman Liselotte Engstam and Professor Mats Magnusson. Below is a summary of the speakers' presentations as well as some insights gained through roundtable discussions with event participants.

To survive and remain competitive in today's economic and business environment, organizations must lead large-scale changes. Constant organizational change is the new normal in an ever-changing political, social, and economical atmosphere. Artificial intelligence is one of the few innovations that have the potential to assist businesses to transcend significant commercial chasms. AI has immense potential to rocket businesses above the established order and adapts to the new dynamics in business operations. The year 2016 marked the beginning of the business world's recognition of AI potential.

Artificial intelligence (AI) has the potential to transform every business – in the same way (and possibly more) as the internet has utterly transformed the way we do business. From smarter products and services to better business decisions and optimised (or even automated) business processes, AI has the power to change almost everything. Those businesses that don't capitalise on the transformative power of AI risk being left behind. That's why you need an AI strategy for your business. One question people often ask me is, 'Do I still need a separate AI strategy if I've already got a data strategy'? In my view, yes, you should have both.

In Part 1 of the blog series on building a value-driven data strategy, I discussed the challenges associated with framing the data strategy process as a deliverable. A Data Strategy, like a Business Strategy, should ebb and flow depending upon what is "valuable" to the organization given the current business environment. Instead of thinking of your data strategy as a one-time deliverable, think of your data strategy as a continuous journey that evolves as the business environment evolves. We must move from data strategy as a "big bang" deliverable toward an agile, continuously-learning, value-driven data strategy journey. We can leverage the "economies of learning" to power our agile, living data strategy journey.

The power of AI is now within reach of all companies, opening up a new world of strategy innovation and enabling companies to leave the constraints of legacy architecture behind forever. Three new related high-potential strategies include: Forever Beta, Minimum Viable Idea (MVI), and Co-lab. This article explains each in detail, with examples of companies that are currently using them. Though their specific strategies are distinct, the companies share three important characteristics. First, their technology, business strategy, and execution are so closely intertwined as to be nearly indistinguishable. Second, humans — not machines — are in the driver’s seat. Third, these companies understand that all companies, no matter their industry, are now technology companies. But technology-driven business strategies require farseeing leaders. Those who are able to see opportunities at the new radically human nexus of people and technology will pre-empt disruption and seize the future.