Since the turn of 2025, AI has been thrown around a lot in conversations – both individual and also at an organizational level. Major technology providers have started their own suite of tools to build AI agents.  While these tools are good enough for simpler AI use cases like fetching data from systems and presenting to us, but complex use cases like predicting patterns, collating data from multiple systems and driving insights from connected systems – that’s where AI implementations need to be looked at like projects which needs architecting and implementing with organization’s vision of AI.  Let’s look at how we can make sure that AI implementations give us over 95% accuracy and not just answers every time which we assume might be correct.  Is AI enough by itself?  Common perception that AI Agents are deployed on top of applications which can be used to interact with the underlying systems to do what users are supposed to get done from AI.  This perception stems from our use of AI tools like ChatGPT/Claude/Gemini as they interact with the Internet to get your queries answered. Since this is a tool available independently, there’s not technical setup and it is ready to go.  Speaking of being Copilot being enough on itself, it depends on where the data is sourced from – and what the intent of the Agent is. If your Custom Copilot / AI Agent is meant to only look at some SharePoint files, some websites and within 1 system in your M365 gated access, you should be able to patch to knowledge sources and be good enough to let AI Agent give you the information in the format you need.  Challenge occurs where you expecting the AI Agents to make sense of the data which is stored differently in different systems with different naming conventions – that’s when AI agents will fall through because it cannot understand when you are pointing to an “Account” in CRM, but the same is stored as a “Customer” in Business Central.  And this is where something like a Unity Catalog comes into picture. The term itself describes that the data comes together in a catalog for common access and AI agents to source from.  Let’s look at how we can imagine this unity catalog to be in the next section.  Unity Catalog  Unity Catalog can be thought of as an implementation strategy and collection of connected systems over which AI Agents can be based upon.  Here’s how I summarize this process –   Above diagram is a summary for how AI implementations will scale within organizations and have different variations of the same.  To encapsulate, while independent AI agents can be implemented for personal use within the organization, given the appropriate privileges, for AI to make sense of and enable trusted decision making, AI implementations need to have data readiness in place with clarity.  Hopefully, this topic summarizes the direction in which organizations can think of AI implementation, more than just building agents.  We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com.

CloudFronts is hosting an event focused on data-driven growth on May 22nd, 2025, at MPR2, Microsoft Schweiz, The Circle 02, Flughafen Zürich 8058 Zürich, from 8:00 AM to 11:00 AM.   Having clean, reliable data is a must have in our changing technology & geo-political landscape. Organizations struggle with many things in this journey from organizational policies, legacy systems, getting people ready, change management, data quality issues and many such things. It is tough to get started and it can be daunting, but it must be done and done with urgency.   Join Reto Hossmann (Head Global ICT at BUCHI) and Anil Shah (a veteran Microsoft partner with a deep focus on business applications and connecting systems using Azure) in this discussion on our journey together since the last 4 years enabling BUCHI’s data journey . We will talk about our learnings, our mistakes and our recommendations. Come with your questions (we will do our best to answer!), meet others and understand their challenges and their own journey (sometimes it helps to know we are in the same boat!) and get insights from our experience.  Register Here, let’s have some fun together and get our enterprise and our data ready for growth.   About CloudFronts   CloudFronts is a global Microsoft Solutions Partner for Business Applications, Data & AI, helping teams and organizations worldwide solve their complex business challenges with Microsoft Cloud, AI, and Azure Integration Services. We have a global presence with offices in U.S, Singapore & India.  Since its inception in 2012, CloudFronts has successfully served over 200+ small and medium-sized clients all over the world, such as North America, Europe, Australia, MENA, Maldives & India, with diverse experiences in sectors ranging from Professional Services, Financial Services, Manufacturing, Retail, Logistics/SCM, and Non-profits.   “Discover How We’ve Enabled Businesses Like Yours – Explore Our Client Testimonials!” 

At the turn of 2025, AI has been infused in everything we do, both in our personal lives and at work. As AI becomes more mainstream in business, from mastering prompt engineering to having a solid data catalog, it’s become crucial for organizations to ensure their applications are fully AI-ready. This article will focus on how you can drive AI and Insights from your Sales to Billing cycle at your organization to establish a dependable AI strategy.  PO and BC – Data Catalog: The word ‘Data Catalog’ gives an impression of all the data being available through connected systems through a single source – this source is AI Agents.  Organizations which run their projects with Project Operations have Sales cycle, Project & Resource Management, Invoicing in Business Central with all the transaction data from Project Operations – they for a data catalog when connected.  AI Agents need a data catalog to have consolidated form a system which it can leverage to give reliable insights and take correct actions which you define in your business. For example – AI will give incorrect responses or not act accordingly if the two systems are not connected, and AI Agents are not able to make sense of it. This will throw off the AI Strategy planned for your organization.  A well-organized Data Catalog increases AI Agents’ reliability and accuracy, enabling correct results and supporting AI use-cases in your organization.  PO-BC Integration Module: There is an absence of an integration solution between Project Operations and Business Central which caters to connect Customers/Projects from Project Operations to transaction data in Business Central.  Now, the AI Strategy will align only when the systems are seamlessly connected end-to-end. And due to this disconnect, the Project Management as well as the Business Central data doesn’t align and is human-dependent for its reliability which is counter-intuitive. AI won’t give errors, it’ll give incorrect answers! And hence, the integration needs to be in place for data integrity to drive AI Agents off of this reliable data.  To fill this gap of Project Operations and Business Central not being connected out-of-the-box, CloudFronts has released PO-BC Integration Module to bridge this gap and connect Project Operations to Business Central. This solution connects Project Operations data end-to-end to Business Central and back so that the AI agents can take reliable actions against this data – either automatically or when asked.  Conclusion : For a reliable AI strategy, integrate systems like Project Operations and Business Central. Achieve this using CloudFronts’ PO+BC Integration solution. This ensures AI agents can use the data to improve cash flow for growth.  This will help optimize operations efficiencies so that both, resources and cashflow can be freed up to focus on growth at the organization.  We hope you found this article useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com