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Digital transition and various AI models assisted in reversing existing software tools in modern corporate management structures, forcing business community to adapt to new AIs. Perspectives are towards basic corporate architecture’s changes from “being around software” towards “organising software around” in managing business’ facilities. Hence, enterprises have to focus on control, capability and operative choices, rather than on securing private evaluations and retaining “corporate ownership”.
Background
The AI models’ opportunities are becoming more than just extending the existing business management systems; they are going to represent “reinvented” structures (for some entities) in the digital and/or agentic world, and forced to adapt to systematic digital analysis of data-volumes. For others, it would be a complete replacement, in resetting business SaaS; e.g. presently evolving digital business in “intelligence solutions” can in real terms add value (being faster, cheaper and more reliable), while relying on existing SaaS for daily execution. However, while businesses pay AI providers for access to powerful models, they may also be sharing proprietary knowledge that helps those models improve over time.
More in: https://economictimes.indiatimes.com/tech/technology/microsoft-ceo-satya-nadella-warns-of-reverse-information-paradox-facing-businesses-in-ai-age/articleshow/132355538.cms?from=mdr&utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst
The “reversal paradox”
Microsoft CEO Satya Nadella warned that businesses using artificial intelligence face a “reverse information paradox,” where companies pay for intelligence twice: once with money and again by surrendering their proprietary knowledge and institutional workflows.
The following facets apply to the new SaaS facilities:
= Nobel Prize-winning economist Kenneth Arrow described the “original paradox” in the following way: “a seller risks giving away knowledge just to prove its value to a buyer”.
= The AI’s “reversal” is based on the fact that AI models “flips this dynamic”; i.e. the buyer (the enterprise) leaks or hands over critical knowledge just to make the AI model useful.
= Intelligence exhaust: every prompt, tool interaction, correction and evaluation given to an AI model distills into institutional know-how that quietly migrates to the model provider.
= Growing asymmetry: while AI providers learn deeply about their enterprise customers through usage data, businesses gain little to no visibility into what the providers learn in return. The “information asymmetry” grows over time because AI providers learn more about their customers, while customers have little visibility into what the providers learn.
= A company should be able to use an AI model without giving up the knowledge that makes it unique; hence, Nadella notes in the India Times – “it is the reverse information paradox we need to confront”.
Source and citations from: https://economictimes.indiatimes.com/tech/technology/microsoft-ceo-satya-nadella-warns-of-reverse-information-paradox-facing-businesses-in-ai-age/articleshow/132355538.cms?from=mdr&utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst
Additionally: https://cio.economictimes.indiatimes.com/news/corporate-news/microsoft-ceo-satya-nadella-warns-of-reverse-information-paradox-facing-businesses-in-ai-age/132357918
Recommended solutions
= Proprietary learning environments: companies must build independent learning systems stored on their own cloud infrastructure so internal knowledge would “stay internal”.
= Orchestration layers: adopting a neutral orchestration layer lets businesses swap multiple foundation models in and out based on price, performance and security.
= Data ownership boundaries: enterprises must establish strict trust boundaries to retain ownership of their organizational memory and fine-tune open-weight models they actually control. Some already available tools can be seen in, e.g. Microsoft Copilot (as a new OS for business) and GitHub.
More in: https://economictimes.indiatimes.com/tech/technology/microsoft-ceo-satya-nadella-warns-of-reverse-information-paradox-facing-businesses-in-ai-age/articleshow/132355538.cms?from=mdr
Software reverse engineering
Software reverse engineering (SRE) is the process of analyzing software to understand its design, architecture, and functionality. Such analysis is often performed without access to the original source code or documentation.
SRE involves deconstructing compiled binaries, executables and/or complete systems and reconstructing higher‑level representations that reveal internal mechanisms such as algorithms, data structures and data flows.
Reverse engineering works backward from a finished binary to infer architecture, logic, algorithms or data formats, while software development works forward, creating software through design documents, requirements and source code.
More on types of reverse engineering in: https://www.apriorit.com/glossary/software-reverse-engineering
For example, business benefits using reverse engineering (RE) in software development include:
1. Understanding legacy systems. Many businesses rely on old software systems that lack proper documentation. One of the main benefits of reverse engineering is helping them understand how these systems work and making them easier to maintain or update.
2. Improved software security. Reversing allows security specialists to examine binaries and internal structures to uncover design flaws, vulnerabilities, and potential attack vectors, strengthening the organization’s overall security posture.
3. Enhanced interoperability. RE facilitates integration with third‑party products when documentation is unavailable, support has ended, or undocumented errors disrupt compatibility.
4. Restored product documentation. Over time, software documentation becomes outdated or lost. Reverse engineers can help reconstruct this documentation, fill in the gaps, and even create new documentation from scratch.
5. Accelerated software development. Reverse engineering enables development teams to analyze existing binaries or legacy implementations, identify useful patterns or architectural decisions, and use these insights to guide or speed up new development tasks.
Source and citation from: https://www.apriorit.com/glossary/software-reverse-engineering#benefits
Businesses use artificial intelligence models primarily to automate customer service, forecast inventory, streamline internal workflows and analyze large datasets to cut costs and save time.
For example, the “corporate usage”; in the EU-27, over 55 percent of large companies use AI tools, compared to about 30 percent of medium-sized firms and 17 percent of SMEs.
Source: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Category:Digital_economy_and_society
Then, the “executive usage”: about 88 percent of business executives report active AI usage in their organizations, with 72 percent specifically leveraging generative AI technologies.
Among top ways businesses use AI are the following:
= Customer support: AI chatbots and virtual assistants handle high-volume routine inquiries, resolve issues quickly and reduce repeat contact rates.
= Inventory and supply chain: predictive models forecast product demand, optimize stock placement and plan delivery routes.
= Marketing and sales: AI drafts personalized sales outreach, analyzes customer relationship management (CRM) data and generates marketing copy.
= Finance and underwriting: Al models assist the financial data process at high speed, score credit risk and detect fraudulent transactions.
= Administration and human resources, HR: AI tools transcribe meetings, summarize long email threads and automate routine shift scheduling.
More in: https://www.certified-nets.com/how-businesses-use-ai/
Note. Some reservations: the moment employees started entering regulated data into the corporate AI systems (whether through Microsoft Copilot or external AI tools), the organization assumes risks; not because AI is inherently unsafe, but because policies and controls may not yet reflect how AI is being used. Organizations need clear standards for reviewing, approving and documenting AI-assisted work product. Artificial Intelligence is not just a productivity tool: AI is an operational capability that must be governed like any other enterprise system.
Top 10 leading global companies driving and utilizing AI
= NVIDIA: it dominates the AI hardware and chip market with its GPU architecture and CUDA software ecosystem, powering the training and inference of nearly all major AI models.
= Microsoft: it integrates generative AI across its enterprise and consumer ecosystem through Microsoft 365 Copilot, Azure AI, and a deep strategic partnership with OpenAI.
= Google (Alphabet): it a pioneer in machine learning that uses AI across search, YouTube recommendations, Waymo autonomous vehicles and the Gemini model family.
= OpenAI: the creator of the landmark ChatGPT conversational platform and the GPT foundation model family, setting global benchmarks in generative AI.
= Amazon (AWS): it uses AI heavily in e-commerce personalization and logistics while providing the cloud computing backbone (Amazon Bedrock and SageMaker) for enterprise AI adoption.
= Meta platforms represent and drive open-source AI development with its Llama model family while deploying machine learning to optimize social media ad targeting and content curation.
= Anthropic is the developer of the safety-focused Claude AI model family, widely deployed across enterprise, research and financial institutions.
= IBM: it focuses on enterprise-grade and responsible AI governance through its watsonx’s platform, helping corporations integrate custom models securely.
= Tesla: it uses real-world physical and neural AI for full self-driving vehicle systems and humanoid robotics (e.g. Optimus).
= Apple: it implements on-device neural processing, facial recognition (FaceID), Siri’s voice assistance and Apple Intelligence featuring across its hardware lineup.
Source and citations from: https://www.fool.com/investing/stock-market/market-sectors/information-technology/ai-stocks/companies-that-use-ai/