We always wanted machines that could think. What we got instead were machines that listen. Not perfectly, not like us, but close enough to change everything.
The strange part is how quickly it stopped feeling strange. One year you're typing into a search bar, trying to guess the right keywords. Next, you're in a live conversation with something that doesn’t sleep, doesn’t blink, and doesn’t forget. And it's not just ChatGPT or Claude or Gemini. It's your documents, your calendar, your inbox, your codebase, your CRM. The conversation is happening across all of it. Quiet, constant, and invisible.
People call it agentic AI. It sounds like branding. What it really means is that the machine doesn’t wait for instructions. It takes a prompt and moves. Refund the item. Notify the customer. Update the CRM. Adjust the inventory. Schedule the follow-up. No buttons. No workflow triggers. Just finished tasks, one after another, like falling dominoes. And when something breaks, the AI adjusts. Not always perfectly, but often enough to feel real.
This isn't automation. It's delegation. That’s the shift. And once you see it, it’s hard to unsee.
This is the new AIaaS. Less about models and more about momentum. Built not just to reply, but to carry work from start to finish. Let's read more about it.
AIaaS: how businesses adopt AI without starting from zero
AIaaS lets organisations use artificial intelligence without building everything from the ground up. Instead of training custom models or managing infrastructure, teams can access ready-made tools from platforms like AWS, Google Cloud, Azure, and IBM Watson. These tools offer machine learning, language processing, document analysis, and fraud detection through APIs or simple interfaces.
This approach is already changing how different sectors work. A finance team can flag suspicious transactions using a pre-trained model. Legal departments can sort documents by case type automatically. Healthcare providers can manage appointments using a conversational assistant. These services fit into existing systems without requiring major development or investment.
The real benefit is simplicity. There’s no need to set up infrastructure, wait through long implementation cycles, or build in-house AI expertise. AIaaS is designed to get results quickly, letting teams focus on outcomes instead of the technology behind them.
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.
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. Aeldris is an example 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 AGASensures 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
A lot of the focus in AI used to be on speed. How fast a model could respond, how cheap it was to run, and how well it could be tuned to answer a question. And sure, that still matters. But what matters more now is what AI actually does. Not just replying, but doing. Slowly, we’re moving toward systems that take initiative.
These agents remember what happened earlier. They plan the next steps. They connect to APIs, trigger CRM workflows, and adapt when something changes. And this isn’t theory anymore. It’s already showing up in production. Platforms like Aeldris are helping companies bring agents into finance approvals, legal work, and internal operations.
Governments in Singapore and the UAE are building agent-driven systems for healthcare and public services. South Korea is putting national investment behind reasoning-based AI programs. Amazon and Shopify are using agents to run support, logistics, and storefronts. Stripe uses agents to power customer service. IKEA is using them behind the scenes to make operations smoother.
A year ago, AIaaS was about responding faster and cutting costs. Now it is about giving systems memory, reasoning, feedback, and the ability to actually carry things out.
The global shift toward cloud and AIaaS increased fast after 2020, as covered in QED42’s piece on post-COVID cloud adoption. That shift laid the groundwork for everything we're seeing now.
In my view, this is one of the most important changes in how we interact with technology. We are not just building smarter tools. We are starting to build systems that work alongside us. That raises new questions about trust and accountability. But it also opens up real opportunities to rethink how work gets done and who does it.
We’re seeing agents that specialise in legal, finance, and business tasks. Agents that work together, passing tasks between them. Agents that reflect a brand’s voice and decision-making style. And a new kind of digital presence, where being inside the model could matter as much as ranking in Google search.
The big question isn’t just what the model can say.
It’s what the agent can figure out and actually do.
And that, to me, is where things start to get interesting
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.