AI as a service in 2025 is built around outcomes. Intelligent agents now handle entire workflows. They check refund requests, verify orders, apply policies, update records, and send confirmations in one continuous flow.
The shift is clear: orchestration matters. These agents utilise tools like CRMs and ERPs, carry memory across sessions, adapt to feedback, and complete tasks from start to finish. In support, they resolve tickets across Zendesk. In legal ops, they summarise contracts, draft filings, and update case systems.
In healthcare, they book appointments, verify coverage, and manage follow-ups without handoffs. The AIaaS market is set to reach 20.64 billion dollars in 2025 and grow past 98 billion by 2030.
Agentic AI will contribute 7.6 billion this year, growing at nearly 46 per cent. Inside organisations, 85 per cent are already using agents.
In India, 92 % of employees now work with generative AI every day.
Businesses are moving fast with structured rollouts, integrated tools, and updated governance. Platforms like Aeldris support this shift with agentic platforms built for secure, enterprise-ready deployment. The AI that stands out now doesn’t just respond. It finishes the job.
AIaaS: how businesses adopt AI without starting from zero
AI as a service (AIaaS) lets organisations use AI through cloud platforms instead of building custom models internally. Providers like AWS, Google Cloud, Azure, and IBM Watson offer ready-made capabilities such as machine learning, natural language processing, document understanding, and fraud detection. These are available via APIs or user-friendly interfaces.
A financial company flags suspicious transactions using a pre-trained model. A legal department sorts documents by case type. A healthcare provider automates appointment handling through a conversational assistant. These tools integrate into existing systems without a large upfront investment or internal development.
AIaaS makes it easier to adopt AI. It removes infrastructure complexity, shortens implementation time, and allows organisations to focus on outcomes. It is built for speed, scale, and practical use, not for managing the technology underneath.
Types of AI as a service
AI is moving from simple automation; the companies making real progress are investing in solutions that match how they already work.
Retrieval-Augmented Generation (RAG) is one of the most used approaches. It helps AI deliver accurate responses by extracting data from trusted sources.
- Static RAG uses internal content for consistency
- Dynamic RAG pulls from live external sources
- The hybrid RAG does both, switching based on the task, and can be further enhanced by integrating knowledge graphs with RAG for deeper context and accuracy.
This setup supports legal helpdesks, customer portals, and internal tools where accuracy is critical. Learn more: Hugging Face on RAG
Agentic architecture allows different AI agents to plan, act, and verify results in sequence. These modular setups are ideal for workflows that require reasoning, validation, or escalation. CrewAI & Aeldris are examples of a platform that supports this kind of structure.
Multi-Channel Processors (MCPs) let systems receive input through voice, chat, or forms. They support flexibility in how users interact with AI, especially in customer-facing or multilingual environments.
Agent-to-Agent (A2A) communication allows agents to pass tasks to each other. This keeps workflows going without resets, especially in long or multi-step processes.
Voice and text assistants are the layer most users see. These assistants do more than chat; they book appointments, file requests, summarise documents, and complete domain-specific tasks using the logic and data behind the scenes.
Model training and fine-tuning come into play when prebuilt models do not meet the mark. Customising models like LLaMA or Mistral ensures they align with business data, languages, or workflows.
Business consulting for AI integration helps organisations choose the right use cases, map AI to real decision points, and measure impact across speed, accuracy, or cost.
They are working systems already in use, already solving real business problems. The shift is underway. And this is what it looks like.
Why prediction is no longer enough
Most current AI services are reactive. They classify, complete, and summarise while working well for static, one-shot tasks.
But what about:
- Filing an insurance claim that requires multiple checks and steps?
- Drafting a legal response that draws from three different databases?
- Guiding a user through a health decision based on changing symptoms?
In these scenarios, prediction, only systems break down. They lack memory, can’t adapt, and don’t understand what the task is.
To solve this, AIaaS is evolving into agentic infrastructure: systems that plan, use tools, track progress, and adjust when the unexpected happens.
What reasoning agents do differently
Reasoning agents are designed to complete tasks with structure, logic, and memory. Instead of giving a one-time answer, they move through a process step by step, using context and available tools.
Tracking progress
Agents keep track of what’s done and what comes next. This structured reasoning uses internal memory or "scratchpads," like in GPT-4o on ChatGPT and Claude, allowing them to handle complex, multi-turn tasks more reliably.
Evaluating how well RAG systems perform is becoming critical; frameworks like Ragas help measure contextual accuracy, relevance, and completeness.
Using tools mid-task
They don’t rely only on what they’ve seen. Through APIs, databases, and live search, agents can act while working. ChatGPT’s function calling and Perplexity’s web access are strong examples of this in action.
Making decisions step by step
Reasoning agents don’t rush to an answer. They pause, check, and respond based on what’s already known. This is key in areas like onboarding, finance approvals, and legal reviews, where each step depends on the last.
Adapting to change
If something shifts mid-process, agents can adjust. Platforms like Gemini and Claude support longer interactions that allow reasoning across changing inputs.
Reasoning agents work through tasks, with memory, tools, and logic. In AIaaS, this is not a bonus feature. It’s what makes systems reliable, usable, and ready for real business impact.
Why this shift matters beyond the backend
In 2024, a Capgemini survey of 12,000 consumers found that 58% use GenAI tools like ChatGPT, Gemini, and Perplexity for product and service recommendations, up from just 25% the year before.
During the same year’s holiday season, Adobe Analytics reported a 1,300% increase in AI-powered search referrals to U.S. retail sites.
These users are leading indicators of where digital behaviour is heading. They tend to be younger, higher-income, and more engaged, and their journeys now often begin inside a conversation with an LLM:
“What’s the best coffee machine under $200?”
“Plan a weekend trip that’s quiet and close to nature.”
These agents aren’t just model endpoints. They’re becoming decision-making interfaces, and that means your AIaaS strategy now shapes both how internal systems operate and how customers interact with your brand from the very first question.
The rise of SOM: what LLMs think about your brand
To track this new dynamic, marketers and researchers have coined a new metric: Share of Model (SOM).
SOM measures how often and how favourably LLMs recommend your brand, based on real user prompts.
Unlike traditional metrics like Share of Voice (SOV) or Share of Search (SOS), SOM is a model-facing. It reflects what LLMs reason through when they try to solve a user's task.
Jellyfish’s Share of Model (SOM) platform tested prompts across ChatGPT, Gemini, Perplexity, and others, revealing striking differences in how LLMs surface brands.
- Ariel (laundry care) had a 24% SOM on Llama, but less than 1% on Gemini.
- Chanteclair appeared prominently on Perplexity but was absent from Meta’s LLM.
- Lincoln showed strong human brand recall, but did not surface in most LLM responses, likely because its messaging emphasises aspirational qualities, while LLMs prioritise functional relevance and task resolution.
These differences highlight a key shift: LLMs don’t simply index popularity; they select what helps resolve a prompt. That means brands must think beyond attention and start optimising for AI reasoning paths.
LLMs don’t index. They decide. And if your brand doesn't help them resolve a task, you won't be recommended.
LLMs as agents: where AIaaS and brand discovery meet
Here’s the connection: the LLMs behind ChatGPT, Gemini, and Perplexity are fully realised AIaaS platforms.
They work through a few key parts:
- How they understand and respond
- How do they use tools?
- How do they remember things?
- How they learn and improve
These systems are built to reason across context, trigger external actions, and respond to evolving intent. They function as thinking agents, capable of handling multi-step workflows and decision paths.
This highlights what AIaaS now supports: not just predictions, but agents that complete real tasks. Whether they’re guiding a customer to a product or powering internal operations, these agents now sit at the centre of how businesses interact, respond, and deliver outcomes.
Benefits and challenges of AIaas
Instead of building everything from scratch, companies access powerful models and agents through cloud platforms. They connect via APIs, pay as they go, and get instant access to the latest improvements without managing the infrastructure.
The benefits are clear:
- Speed: What once took months to build can now be tested and launched in weeks.
- Flexibility: AI tools can be added to workflows for search, support, automation, or content generation. Real-world implementations like QED42’s machine learning layer on Slack show how AIaaS enhances team communication and internal workflows.
- Cost savings: No need for large internal AI teams or heavy infrastructure.
- Scalability: Services like OpenAI, Google Cloud AI, AWS AI Services, and Azure AI offer powerful, production-ready capabilities.
- Advanced features: Many platforms now support tool use, memory, and step-by-step reasoning, making it possible to build agents that do more than answer; they complete tasks with context.
But AIaaS also comes with real challenges:
- Customisation limits: Out-of-the-box tools often fall short in domain-specific tasks.
- Integration issues: Legacy systems, inconsistent data, and unique workflows still require engineering effort.
- Privacy and compliance: With sensitive data flowing through third-party services, concerns around GDPR and local data laws are rising. See OECD’s AI risk report.
- Vendor lock-in: Once a company builds around a provider, switching can be difficult and expensive.
- Security and oversight: As agents take on more responsibility, the need for transparency, explainability, and monitoring grows. Read more from NIST’s AI risk framework.
AIaaS has opened the door for faster, smarter development. But scaling it responsibly means going beyond easy wins.
Conclusion
AI that predicts still matters. It helps generate responses in the background. But the real value now comes from agents. These systems are built to complete tasks, not just reply.
This shift is already happening.
Enterprise-focused platforms like Aeldris are enabling businesses to adopt reasoning agents across finance, legal, and operations. Governments in
Governments in Singapore and the UAE are building agent-led platforms for healthcare, public services, and digital systems. South Korea is investing in national AI programs focused on reasoning and applied agents.
In the private sector, Amazon and Shopify are using agents for support, fulfilment, and storefronts. Stripe has GPT-powered support agents. IKEA is using agents to improve service and operations.
These agents do more than respond. They remember past steps, plan next actions, use tools, and adjust when things change.
A year ago, AIaaS was focused on response speed, cost, and prompt tuning. Now it is shaped around memory, tool use, reasoning paths, and feedback systems. The software is starting to behave more like a support team than a search box.
And this is just the beginning.
The global shift toward cloud and AIaaS increased fast after 2020, as explored in this piece on cloud adoption post-COVID, laying the foundation for today’s agentic systems.
We are starting to see:
- Purpose-built agents in legal, finance, and operations
- Agents that pass tasks to each other
- Custom agents are designed to match the brand tone and decision style
- A new kind of visibility, where Share of Model may become as important as SEO
The real question is no longer what the model can say.
It is what the agent can figure out and make happen.
Frequently asked questions
What is AI as a Service (AIaaS)?
AIaaS is a way for businesses to use advanced AI tools without building everything themselves. It offers features like chatbots, language understanding, and image analysis through cloud platforms. You can access these tools through APIs and start using them quickly.
How is AI as a Service (AIaaS) different from SaaS?
SaaS gives you complete software, like email or project management tools. AIaaS gives you specific AI abilities that you can add to your systems. It focuses on things like understanding text, making predictions, or automating tasks.
What are some of the best AI as a Service (AIaaS) platforms?
The most popular platforms in 2025 are OpenAI, Google Cloud Vertex AI, Microsoft Azure AI, AWS Bedrock, and Anthropic. They offer powerful models, easy-to-use tools, and the flexibility to build smart features into your existing workflows.