Artificial Intelligence (AI) projects often don't meet expectations, run over budget, take too long to show results, and fail to deliver a clear return on investment.
Having implemented AI/ML solutions since 2018, we have adapted our methodology for "classical" AI/ML solutions to developing autonomous AI agents. This methodology addresses these issues by prioritizing quick, measurable results while also creating a foundation that supports long-term scalability.
The agents we will be describing below are designed to handle repetitive tasks and complex workflows with minimal human oversight or intervention (human-in-the-loop).
The agents can use a mix of Generative AI (GenAI) and "classical" Machine Learning Algorithms - such as those used for credit scoring or predictive maintenance. Often, multiple agents are deployed and orchestrated to tackle workflows with no predefined rules, especially those which might need to interact with customer data across systems and perform tasks on behalf of users.
Venturing into AI and AI agent building can raise plenty of questions and concerns. Artificial intelligence is still very much a blue ocean, people are exploring it, but many don’t yet know where they’re headed. New AI agents, models, and tools appear every day, often making bold claims that far exceed what they can realistically deliver.
When first approaching artificial intelligence, it’s easy to think it should be implemented across the board to boost workflow and efficiency. But in practice, adding AI in certain areas can turn out to be counterproductive or even redundant. That’s why, before diving into adoption, it’s important to consult with experts who can guide you through the process using a well-defined AI adoption framework.
Not every organization is ready to implement artificial intelligence right away. Before considering deployment, you need to assess your company’s technological readiness, ensuring you have the right infrastructure and a sufficient volume of quality data, all protected by strong security protocols. Without this foundation, your AI agent may not perform as expected.
We developed an AI Adoption Framework based on years of previous experience working on "classical" ML / Data Science solutions development. It is a fixed time, fixed scope framework with the following deliverables:
Although artificial intelligence (specifically Generative AI, powered by large language models) is widely seen as a groundbreaking technology, it can also be expensive. That’s why a clear evaluation of your use cases, capabilities, and a step-by-step plan is critical before getting started.
To help our clients navigate all of this, we’ve developed an AI Adoption Framework that addresses exactly these concerns. It allows us, and the organizations we work with, to:
What is the result in using this iterative approach? It accelerates time-to-value and creates a foundation for your digital transformation process and development of AI agents.
We often encounter two types of scenarios when speaking with clients. Some are ready to dive into a full-scale solution, complete with all the features they’ve planned. Others are earlier in their AI journey, or exploring AI for a new use case, and prefer to first validate the return on investment before committing to a broader implementation.
In both cases, we follow a phased, agile approach. This allows us to continuously adjust the project scope, timeline, and deliverables to align with each client’s evolving needs.
Once the AI Adoption Framework phase is complete, we move into the next stage of implementation, which branches into two distinct tracks:
This track is designed to deliver fast, tangible outcomes using Microsoft’s AI tools. It’s ideal for organizations that want to demonstrate the value of AI early on and build momentum for broader adoption.
When choosing the Quick Wins track, the goal is to validate AI’s viability and impact by focusing on areas where it can immediately deliver business value. That starts with identifying key processes and systems where AI can drive efficiency and cost savings.
The next step is to define a clear AI roadmap that outlines the specific use cases to prioritize in order to realize these benefits.
By the end of this track, you should have a fully functional AI agent, an initial version tailored to analyze data within your selected use cases.
Below is an example of typical deliverables from a Quick Wins implementation. Keep in mind, however, that your setup may look different depending on your specific goals and requirements.
Some of the deliverables and assumptions include:
Technical Assumptions:
Business Capabilities:
To bring autonomous AI agents to life, we rely on a robust set of Microsoft technologies—each playing a specific role across data storage, analysis, automation, and deployment. Below is a breakdown of the key tools we use and the tasks they support within an artificial intelligence implementation project.
This track takes a structured, strategic approach to AI adoption. It’s the natural next step after developing a successful proof of concept, moving toward a fully realized AI platform with multiple working autonomous AI agents work as part of a coordinated multi-agent system that can be deployed across the entire organization.
The goal of the Full AI Platform track is to ensure that AI solutions are scalable, well-integrated, and aligned with your business objectives. At the same time, it lays the groundwork for long-term innovation powered by artificial intelligence.
Early in this track, we set up two critical components:
This track is designed for enterprise-wide AI implementation, offering a long-term path toward AI maturity.
By the end of this phase, your organization will benefit from capabilities such as:
Similarly to what we showed at the Quick Wins track, below are examples of typical deliverables and technical assumptions included in the Full AI Platform track.
Deliverables include:
Technical Assumptions:
Business Capabilities:
Implementing a full-scale AI platform with multiple autonomous AI agents, organized as part of a coordinated multi-agent system, requires a sophisticated and well-integrated technology stack.
Below is a breakdown of the core Microsoft technologies we use during Full AI Platform implementations, along with the key tasks they support across orchestration, automation, analytics, governance, and optimization.
Before moving forward with any of the tracks described above, as mentioned earlier, it’s important to start with an assessment of your organization’s data and artificial intelligence maturity. There are several stages of “data-first capabilities” that every company needs to progress through before reaching fully AI-aligned, end-to-end business operations and complete data maturity.
Use the bullet points below to evaluate where your organization currently stands in its AI and data maturity journey:
The first level is the “Initiating” stage, where data collection is limited, and the overall data infrastructure still has significant room for improvement.
Here we have:
Results in an ungoverned data ecosystem
At the “Emerging” stage, your organization likely has some solid data practices and a few systems in place. However, you're still in the early stages compared to what’s required for effective AI agents adoption. At this point, investing in autonomous AI agents may turn out to be premature or even redundant
Here we have:
Leading to federated data programs (BU focus)
The “Performing” stage is where an organization has already established solid data practices, consistent data collection, and a secure data platform. At this level, some targeted adjustments, such as adopting best practices or implementing minor technical improvements, are often enough to move the organization to the next stage of maturity.
Here we have:
Results in a foundation data platform
At this point, the wheels are already in motion. A company at this stage has strong data practices and the right technology in place. What separates them from reaching the “Leader” level is mostly fine-tuning, details that can be addressed with the support of a skilled technical leader in Data and AI. This is where an organization is nearing peak maturity.
Here we have:
Leading to a Governed & self-serve data platform
The “Leader” stage represents peak data and AI maturity within an organization. Everything is well-defined, well-managed, and aligned. Companies at this level don’t need to worry about whether they’re ready for artificial intelligence and autonomous AI agents, they already have the data, infrastructure, and expertise needed to develop and implement advanced technology solutions with confidence.
Here we have:
Resulting a transversal data platform
Reaching the Leader stage in data and AI maturity signals the presence of a world-class ecosystem, one where insights and decision-making are embedded across the entire organization.
Meaning the organization has:
Change intertwined into data processes.
Our AI platform development is deeply rooted in Microsoft’s ecosystem, an advantage for organizations already invested in Microsoft technologies. While every AI platform implementation requires a variety of tools, several core Microsoft technologies stand out:
OneLake
A unified data lake that centralizes structured and unstructured data, creating a single source of truth across the organization.
Microsoft Fabric
An integrated analytics platform that prepares and governs data for AI use. Fabric ensures that data is high-quality, complete, and accessible across teams.
Azure AI Foundry
A code-first platform for building custom AI tools, solutions and copilots. It enables enterprises to create AI applications tailored to their business needs.
Semantic Kernel
An orchestration framework for integrating and coordinating AI models, services, and plugins. Semantic Kernel enables developers to create dynamic, multi-agent AI workflows combining traditional logic and natural language processing.
Azure OpenAI Service
Offers access to advanced models like the GPT and o-series through Azure, supporting a broad range of generative complex tasks like summarization, content generation, and intelligent querying thanks to large language models.
Azure Machine Learning
A complete platform for managing machine learning lifecycle operations, from model training and deployment to monitoring and MLOps.
These tools by themselves solve only parts of your Data and AI journey, however when used together by a team that has the experience and expertise you will end up having a full-fetched AI platform that’s tailored for your company.
Maybe you’ve got so far reading this article and you’re not sure if this is the right approach for your specific situation. We already applied the same expertise, framework and mindset to several large companies, here’s a breakdown of our case studies.
Developed IoT-powered predictive maintenance and quality models using sensor data and neural networks to detect production anomalies. The Azure-integrated solution improved uptime, reduced product returns, and enhanced quality control across 11 manufacturing sites.
Implemented a logistic regression model with advanced text mining and feature engineering to detect wrong classifications in invoice processing. The solution significantly reduced manual errors by analyzing both structured fields and free-text descriptions, boosting overall system efficiency.
Built a neural network–based system to detect product placement across social media in real time. The solution identifies brands, objects, and context, enabling automated marketing reports, influencer tracking, and competitor analysis for global marketing teams.
Developed a web-based app using non-linear optimization to reduce raw material usage and emissions in concrete production. The Azure-hosted solution enables CRH to calculate cost, CO₂ impact, and performance in real time, supporting sustainable manufacturing across global plants.
Built a regression-based forecasting model using Gradient Boosting and time-series analysis to predict hourly sales, returns, and restocking efficiency. The AI system improved inventory planning and operational effectiveness across stores using Python, R, and SQL-based analytics.
Delivered a machine learning solution that processes CCTV footage in milliseconds to detect queues, count customers, and analyze shelf space in retail stores. The system helps LPP validate in-store behavior patterns and boosts demand forecasting with ground truth data.
Developed an internal Copilot solution that uses Azure OpenAI to match relevant case studies to active sales conversations. The AI tool improves response time and personalization, empowering the sales team to deliver stronger, evidence-backed proposals faster.
Built a secure, GPT-powered legal research assistant using Azure OpenAI, Semantic Kernel, and AI Search. Enabled Dutch legal professionals to query across laws and case files with source attribution and AI-powered Q&A; addressing major limitations of public LLMs while validating the client’s product-market fit.
Looking to start your AI journey, or you have already started?
When you work with us, you’ll benefit from our AI Framework, which includes:
Learn more on our AI Framework services page here: https://www.itmagination.com/services/data-ai/ai-framework
If you’re concerned about time-to-value or the overall engagement timeline, we’ve got you covered. Here’s what to expect:
Let’s kickstart your AI implementation, book a call with our team of experts and discover how we can help your organization harness the power of advanced data and AI technologies.
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