By Guest contributor |
Generative AI is no longer experimental – it’s embedded in workflows, customer experiences and decision-making processes. Rather than being a niche capability, it’s now a foundational layer of enterprise transformation. Microsoft’s own internal transformation, as documented in its 2025 AI Decision Brief, illustrates this shift. Over 4,000 Microsoft Copilot champions are driving adoption across the enterprise, supported by gamified learning programmes like Camp Copilot.
AI is now a strategic necessity. Service providers are embedding AI into core offerings and using predictive analytics to shift from reactive to proactive operations. Enterprises are moving beyond proofs of concept to full-scale deployments, with AI driving measurable gains in efficiency, personalisation and risk mitigation.
In the financial sector, agentic AI is already transforming operations. Banks and insurers are using these systems for hyper-personalised services, advanced risk modelling and intelligent automation. For example, Coretek assisted our customer in developing a prototype for a virtual chief financial officer solution designed to process financial account details and generate actionable insights in a natural language format. This integration improved customer engagement and accelerated decision-making, leading to increased reliability, scalability, enhanced security and cost reduction.
Meanwhile, in the defence industrial base, our team worked on an AI solution to provide financial governance within Microsoft Azure, implement data security governance for AI and assist in deploying the first production AI workload that included HR Policy retrieval-augmented generation. This resulted in our customers gaining their first AI production case and security approval for further AI deployments.
The next frontier is agentic AI, where systems not only respond to prompts but act autonomously, learn from outcomes and orchestrate complex workflows. These agents are evolving through four levels: reactive, proactive, adaptive and fully agentic systems. Microsoft’s experimental platform, Magentic-One, exemplifies this shift. It integrates with enterprise systems to automate workflows, provide real-time assistance and continuously improve through self-learning. Internally, Microsoft is preparing for this future by deploying agent builders in SharePoint and exploring agents that can act on behalf of employees.
As AI agents become more of the primary interface for information retrieval, traditional search engine optimisation is rapidly losing relevance. Users are no longer typing keywords into search engines – they’re asking Copilot, ChatGPT or enterprise agents to find, summarise and act on information. This shift demands a new approach to content strategy. Organisations must optimise AI accessibility, not just human readability. That means structuring content semantically for machine parsing, using metadata and schema markup to enhance discoverability, and ensuring content is context-rich and action-oriented.
The implications are profound. AI agents will favour content that is authoritative, well-structured and aligned with user intent, not necessarily what ranks highest on online search engines like Google. As a result, marketing, documentation and knowledge management teams must rethink how they create and distribute information.
Microsoft is uniquely positioned to lead this transformation. Through its Copilot ecosystem, Azure AI services and agentic frameworks like Agent Builder and Magentic-One, Microsoft is enabling organisations to build and deploy custom AI agents tailored to business needs, govern enterprise data to ensure safe and effective AI use, and accelerate adoption through change management and training.
As AI continues to evolve beyond the realms of generative and agentic AI, a new wave of specialised AI paradigms is emerging with unique capabilities and applications. AI comes in many forms: predictive, discriminative, casual, symbolic, embodied and neurosymbolic. While causal and neurosymbolic AI offer exciting possibilities for deeper reasoning and explainability, they are still in early stages of development. Embodied and agentic AI promise autonomy but face challenges in safety, cost and complexity. Ethical AI is essential but functions more as a framework than a standalone technology. Among these, predictive AI stands out as the most likely candidate to achieve widespread, mainstream adoption in the near future. In contrast, predictive AI is already here and it’s working. Its combination of maturity, utility and accessibility makes it the most likely to achieve full-scale adoption first.
Predictive AI is poised to lead the next wave of AI adoption because it strikes the perfect balance between technological maturity, practical utility and ease of implementation. Unlike more experimental or infrastructure-heavy AI types, predictive models are already delivering tangible value across industries, from forecasting customer behaviour in retail to anticipating equipment failures in manufacturing. The ability to integrate seamlessly into existing business systems makes predictive AI models highly accessible, while the abundance of available data enhances their accuracy and scalability. Moreover, predictive AI aligns well with regulatory expectations for transparency and accountability, making it a safer and more compliant choice for organisations navigating the evolving landscape of responsible AI. As a result, it offers a low-risk, high-reward path to AI-driven transformation, positioning it as the most likely to achieve widespread, mainstream adoption in the near term.
We are at a pivotal moment. AI is no longer just a tool; it’s becoming a collaborator. As agentic systems mature, they will redefine how we work, search and make decisions. Organisations that embrace this shift – by preparing their content, processes and people – will gain a decisive advantage. Microsoft’s roadmap offers a blueprint: empower employees, govern data and build agents that act with intelligence and autonomy. The future of work isn’t just AI-assisted, it’s AI-augmented and increasingly AI-driven.
Brian Barnes is chief product officer at Coretek
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