AI Thought Leadership Archives

Given my 12.5 years in the industry at this point – where I started my journey from Dynamics CRM 2013 online (and having dealt with some D365 CRM 4.0 on-prem along the way too) – I’ve come a long way from changing XML for SiteMaps to make sure the Calendar is set right for new orgs – features in D365 CRM got added and many features kept getting deprecated [Although the later is less than the former].

The only help I get and also which I offered was treacherous hours and hours spent in making sure the. ‘customer experience’ is top notch as much as the quality of my solutioning.
Now, sitting in later half of 2026 and putting even the Solution Architecting years behind me – I look back in time and gauge what has changed really?

Well, fundamentally things remain the same –

Set up new environments
Implement C# plugins and JS for custom logic
Configure roles, System Settings
Configure behind the scenes for applications like Project Operations, Field Service, Customer Service etc.
Migrate and deploy solutions between environments

…and so on.

But, the medium of doing all the above has changed. That definitely has!

With advent of AI tools – Copilot, Claude, ChatGPT, disparate configuration pieces are more connected to each other and to the Developer / Solution Architect sitting in front of the screen on a random Tuesday midnight.

So from a bird’s eye view of things – here’s what is more cohesive, AI-powered and in control which I plan to write further in this series and connect it all together as we sunset 2026 in these relatively newer Agentic development years.

Metadata, Configuration: Dataverse MCP on your AI tools like Claude.
CI/CD: Power Platform Pipelines, Azure DevOps
C# Plugins and Code: Claude Code, GitHub Copilot
End User Guides / Documentation: Claude Design, Figma, Copilot in Office Apps

So in the new few weeks and month, as AI continues to ever-evolve, I’ll share with my audience a framework of bringing together all the applications together with AI to deliver the same high-quality CE applications in an agentic-way.

And it doesn’t stop there – probably years after, there would be something else to replace it. But till then, this areas continue to evolve and take shape bringing from my experience and feedback from the teams and customers I work with in this remaining decade.

Hope this was helpful!

Here are some Power Automate posts you want to check out –

Thank you

So in 2026 as I write this, AI is intrusive to the point that everyone in Management want it because it is believe to work 10x and reduce costs or because everyone is implementing so must we.

And the predicament doesn’t stop there – it continues expand to a point where plans are chalked out to run a department entirely. And organizers scramble to solve the problem directly by introducing AI as a form of “automation” rather than “augmentation”. I pondered on this a lot and this is what I think of it as it stands today.

Although most organizations are way ahead with working AI solutions already and are monitoring for production performance and tuning. But this blog is focussed on someone who’s just arrived to AI conversations and have burnt their fingers in some POCs maybe. Here are my two cents –

Foundation of AI for Business Applications

Here’s how you start – and it’s not building your first agent directly. But are the systems hygienic for your first AI agent to not struggle? Here’s what I mean by that –

Clean your data – Make sure missing Sales or Operations data isn’t cluttering your system, giving unclean and unreliable reporting. Does management completely trust the reports you run today? Get the data right.
Connect your systems – Integrate systems together for not everything – but enough to make sure you don’t duplicate, miss data or rely on manual labor prone to making mistakes. If integrated, make sure integrations work well.

Now extrapolate this at the Org level and you already are starting on a high-trust metadata and data at your org for any AI agents to make sense on.

How to introduce it?

Now that data is clean and systems are connected –

Start small – introduce AI in micro-tasks of your workflows. Use it to generate what your team does obviously. The routine, mundane updates. Repeating tasks which can draw patterns.
With those patterns – form the knowledge base of the AI. Improve AI to feed off of the knowledge base itself. It could be a structured prompt engine or a library that the model has to traverse, process the documents and make sense.
Don’t use it for decision making – use it yourself so you can make a decision – just quicker.

Why do we feel it is not what it is meant to be?

I started off by thinking in a straight line –

I want to get to point B from point A – so, let me implement it and give it to the tech team to do it with some instructions.
Well, it does work – most times. But I’m still not happy with it. And here is the reason –
1. No matter how perfect the answer is.
2. Your judgement in that moment will override always — a confirmation bias or a change in situation suitable at that point in time will make more sense.
3. You tend to take your decision overriding what AI has to offer for you (or rather recommend).
And you dismiss it saying – “It doesn’t work”.

Well, it does work – but it’s giving you an opinion so you could extend yours. It’s not meant to give you your answer, yet. And this is true for most cases if not all.

It’ll be interesting to see how this blog either ages like milk or like wine in coming years. 😊

With this, I kick-start my AI Thought Leadership as a new section and running commentary as I work to get our customers and partners ready and thriving on AI in coming years.

Hope this was helpful!

Here are some Power Automate posts you want to check out –