University of Kwazulu-Natal

The World Of Enterprise Ai Is Turning Hybrid Seeking Alpha

Bonisiwe Shabane
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the world of enterprise ai is turning hybrid seeking alpha

Enterprises are seeking a “cloud-smart” strategy, leveraging the public cloud, private cloud, and edge computing.http://dlvr.it/TPFXj6 Δ document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Enterprise AI is shifting from passive tools to agentic systems Image: Unsplash/Zulfugar Karimov This article has been intentionally misrepresented on other websites that spread false information..chakra .wef-rrnhcm{line-height:var(--chakra-lineHeights-base);font-weight:var(--chakra-fontWeights-black);} Please read the piece yourself before sharing or commenting. Artificial intelligence (AI) has captured the imagination of boardrooms around the globe. However, as organizations rush to harness its promise, many enterprise deployments continue to stall, not for lack of ambition but because current solutions fall short of business realities.

Business leaders are finding themselves caught between highly capable consumer AI and fragmented enterprise tools that require immense customization. The result is a landscape where proof-of-concepts abound but scaled success stories remain rare. A new generation of AI for businesses is emerging; one that recognizes the nuanced needs of large organizations: data security, operational integration, regulatory compliance and above all, business context. This is not about building AI for AI’s sake, it’s about embedding intelligence where work happens. This article is part of an ongoing series about the future of artificial intelligence, drawing insights from LIFT Labs’ AI portfolio. Hybrid AI systems are helping large organizations strike that important balance.

By combining Small Language Models (SLMs), Large Language Models (LLMs), and Retrieval Augmented Generation (RAG), these systems offer an innovative approach to AI—delivering the precision, scale, and control enterprise leaders have long been seeking. And they’re already proving their value across real-world use cases. Unlike monolithic AI models, hybrid architectures blend the best of multiple AI components. SLMs handle specialized, task-specific functions. LLMs address broader, more complex prompts. And RAG connects these models to real-time, internal data sources—improving the relevance and accuracy of LLM outputs.

For enterprises navigating AI adoption, hybrid systems offer a practical roadmap for scalable, responsible innovation. These systems reduce operational costs by aligning the right tool with the right task. They accelerate deployment by streamlining development. And they foster greater trust in AI systems by delivering more accurate, explainable results. With competition intensifying, hybrid AI may become the default. These systems aren’t just solving today’s enterprise challenges—they’re shaping the foundation for tomorrow’s AI-first organizations.

The 2025 Artificial Intelligence and Business Strategy report, from MIT Sloan Management Review and Boston Consulting Group, looks at how organizations that are adopting agentic AI are gaining advantage while facing four distinct tensions. The research and analysis for this report was conducted under the direction of the authors as part of an MIT Sloan Management Review research initiative in collaboration with and sponsored by Boston Consulting Group. Executives have long relied on simple categories to frame how technology fits into organizations: Tools automate tasks, people make decisions, and strategy determines how the two work together. That framing is no longer sufficient. A new class of systems — agentic AI — complicates these boundaries. These systems can plan, act, and learn on their own.

They are not just tools to be operated or assistants waiting for instructions. Increasingly, they behave like autonomous teammates, capable of executing multistep processes and adapting as they go. Notably, 76% of respondents to our global executive survey say they view agentic AI as more like a coworker than a tool. For strategists, agentic AI’s dual nature as both a tool and coworker creates new dilemmas. A single agent might take over a routine step, support a human expert with analysis, and collaborate across workflows in ways that shift decision-making authority. This tool-coworker duality breaks down traditional management logic, which assumes that technology either substitutes or complements, automates or augments, is labor or capital, or is a tool or a worker, but not all at...

Organizations now face an unprecedented challenge: managing a single system that demands both human resource approaches and asset management techniques. The separation of technology and strategy inside most organizations exacerbates this challenge. Technology executives focus on technology issues, making pilot, vendor, or infrastructure decisions. Strategic executives focus on markets, competition, and people. But agentic AI makes that separation untenable. It simultaneously influences the design of processes, the structure of roles, the allocation of decision rights, and the culture of accountability.

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