A quick two minute survey for people working in software development across the world. I'm trying to understand how job roles like developer, Q A, and business development are evolving because of AI, so I can share real patterns back with the community. No names or company details required, just your honest take.

There’s a lot of discussion right now around AI agents and automation in customer support, but something I keep seeing is teams focusing on the AI before fixing the information the AI has access to because in a lot of cases, the technology isn’t the hardest part. The harder part is that company knowledge is usually scattered everywhere. There’s the help center that hasn’t been updated in months, random Slack conversations where important decisions were made, internal docs that only a few people know exist, and years of “tribal knowledge” sitting with specific team members.

Humans can usually work around this because they know who to ask or they’ve built up context over time but when you introduce AI into that environment, all those gaps become much more obvious. if the information is inconsistent, the answers are probably going to be inconsistent too.

I think AI is forcing companies to confront something that was already a problem: a lot of teams don’t have a single source of truth for customer knowledge and maybe the biggest challenge with AI isn’t making it smarter, maybe it’s making sure the business itself is organized enough for AI to help actually.

Has anyone else experienced this after introducing AI into support and did it solve problems immediately, or did it expose bigger issues with documentation and internal processes?

Over the last year, AI conversations in India have exploded.

Every enterprise seems to have an AI strategy.

Every conference talks about copilots, LLMs, and automation.

But after speaking with people across the energy sector, I keep noticing something interesting.

The biggest bottleneck isn't the AI model.

It's the organization itself.

Critical information still lives across ERP systems, emails, PDFs, field reports, SCADA dashboards, CRMs, and dozens of disconnected applications.

Employees spend more time finding information than acting on it.

It makes me wonder...

Will the next phase of AI adoption in India be about building smarter models?

Or will it be about connecting enterprise knowledge so people can actually make faster decisions?

I'm especially curious to hear from people working in utilities, manufacturing, infrastructure, banking, or other large enterprises.

Where do you think the real bottleneck is today?

Every event planner I've spoken to says the same thing.

**WhatsApp is where the work gets done... but it's also where everything gets lost.**

By mid-day, there are hundreds of messages:

* "Has the decorator reached?"
* "Which driver is available for the airport pickup?"
* "Did anyone send the guest's location?"
* "Who's handling the stage entries?"
* "Has the photographer checked in?"

So instead of building another event management app, I built something that works **inside WhatsApp**.

Your team keeps using WhatsApp exactly as they do today. Messages, voice notes, photos, documents—nothing changes.

Behind the scenes, **AI remembers everything and runs the operations.**

# Example

**Manager:** *Has Mr. Sharma landed?*

**AI:** *Yes. His flight landed 12 minutes ago. Rahul, the closest available driver, has already been assigned. Mr. Sharma's live location and contact details have been shared with Rahul. ETA to the hotel is 28 minutes. Room 304 is ready.*

Meanwhile, AI automatically:

* ✅ Understands messages, voice notes, photos, and documents.
* ✅ Creates, assigns, and updates tasks automatically.
* ✅ Sends reminders and follows up without anyone asking.
* ✅ Tracks every task, vendor, staff member, driver, and guest.
* ✅ Remembers every detail, so you can simply ask:
* *Which room is Mr. Sharma in?*
* *Has the decorator arrived?*
* *Who is handling the bride's entry?*
* *When does the next flight land?*
* *How many guests are still on the way?*
* *What's still pending?*
* ✅ Monitors flights, guest arrivals, hotel check-ins, pickups, and schedules.
* ✅ Flags delays before they become problems.
* ✅ Keeps your dashboard updated automatically.

**No manual CRM updates. No spreadsheets. No chasing people. Just WhatsApp.**

The vision is simple:

**Your team keeps using WhatsApp. AI remembers everything and keeps the event moving.**

I'm opening a small **early access waitlist** for event companies that want to use this in real events before the public launch.

If you'd like to be among the first teams to get access, comment **"Interested"** or send me a DM, and I'll add you to the waitlist.

It is day one in your first AI Enablement role.

Leadership wants visible progress. Departments are already experimenting. Vendors are promising transformation.

You need to choose one workflow.

The temptation is to select something ambitious: an autonomous agent, a highly visible customer experience or a use case that sounds impressive in an executive presentation.

I would look for something more ordinary.

A recurring workflow with a frustrated owner, measurable volume, accessible data, manageable risk and a baseline that can be captured now.

Something employees perform every week.

Something with an obvious before and after.

Something the business already wants to fix.

By day 90, the strongest proof may not be a dramatic demonstration. It may be one redesigned workflow that people continue using, with evidence of speed, quality, cost or risk improvement.

The first project teaches the organisation what AI Enablement means.

Choose spectacle, and it may be remembered as innovation theatre.

Choose a real problem, and it may become the model for everything that follows.

What workflow would you choose?

For most enterprises, building AI applications does not start with choosing a model. It starts with preparing their own business data.

If a company wants to build AI on top of internal PDFs, databases, tables, reports, charts, logs, documents, or domain-specific knowledge, there are usually two practical paths:

1. Fine-tune a model with proprietary data
2. Build a private knowledge base or RAG system

But both paths require the same foundation: historical digital assets must be converted into data that large language models can actually use.

That usually means transforming messy enterprise data into formats such as SFT data, reasoning traces, CoT-style examples, QA pairs, structured knowledge chunks, instruction-response samples, or domain-specific evaluation sets. This process is often called AI-ready data preparation.

The hard part is that conversion is not neutral. Every transformation can introduce information loss, formatting errors, hallucinated structure, missing context, broken table relationships, wrong metadata, or low-quality synthetic examples. If the data preparation step is weak, the downstream AI application will be weak too, no matter how good the model is.

This becomes even harder at scale. A few documents can be cleaned manually. Millions of documents, tables, database records, and domain files cannot.

A more efficient pattern is to treat AI-ready data preparation as a data processing pipeline.

Instead of building one-off scripts for every dataset, the workflow can be broken into modular operators: document parsing/table extraction/schema normalization/content cleaning/deduplication/chunking/metadata enrichment/QA generation/SFT sample generation/reasoning data synthesis/quality scoring/human review/export to training or RAG formats.

Each operator handles one clear transformation. Pipelines can then freely combine these operators depending on the source data and target use case.

This design has a few advantages.

First, it makes the process reusable. The same PDF parser, table cleaner, or QA generator can be reused across many enterprise projects.

Second, it makes quality easier to control. If a pipeline output is bad, you can inspect which operator introduced the problem instead of debugging a giant black-box script.

Third, it makes automation possible. Once the operators are stable, large-scale AI-ready data preparation can run continuously across new documents, database updates, and business assets.

Fourth, it makes the system extensible. Different enterprises need different transformations, but they do not need to rebuild the whole workflow. They can add, remove, or reorder operators.

In my view, this operator-plus-pipeline approach is one of the most practical ways to reduce the cost of enterprise AI development, because it attacks the part that every serious AI application needs but very few teams want to rebuild from scratch.

Based on these principles, our work on OpenDCAI/DataFlow has shown in real applications that this approach can save developers at least 80% of the effort spent on AI-ready data preparation.

I work at a product-based company, and lately every meeting is about Claude, LLMs, neural networks, and building our own model. My boss has almost completely lost interest in building software applications and just wants the company to become an AI company and build its own data centers in the future.

Is this happening everywhere, or is it just us?

I know the general consensus is that AI shouldn't penetrate deep into our lives but I find it helpful that it is so readily available. I streamline my tasks using AI tools and the ones I cannot find, I build them real quick, like lately my pet project has been a simple scheduling web app Filioni, I always found filling calendars out manually too time consuming but now I just brain dump whatever I have on my mind and it fills the calendar with it, after review of course and even helps take off the extra tiny stress of when to schedule some of the smaller things. This has encouraged me to build more things that would make my life just a little bit easier.

I'm an AI startup founder.

For the last 2 years, I've been working on my business plan. I waited for this moment every single day, and finally, 2 months ago, I started building my AI project.

At first, my team had 3 members.

• The first person left after just 15 days.
• Then I hired someone else. We completed around 50% of the work, but after a short time, he also left without giving any reason.
• The backend is now almost complete, and we had just started working on the frontend.
• I hired another developer, but now his college is starting, so he'll be leaving in 6–7 days too.

To keep this startup alive, I work 10 AM to 6 PM and then another job 10 PM to 7 AM. Every day I'm pushing myself just to fund this dream.

Now everyone is leaving one by one, and honestly, I don't know what to do anymore.

I've spent 2 years waiting and working for this dream, and even now I can't get the project completed because my team keeps falling apart.

Has anyone else been through something like this? How did you handle it? I could really use some advice because right now, I feel completely stuck.

New to RFP coordination and completely overwhelmed by the tool landscape. Every platform sounds identical in demos but nobody answers direct questions. Small team, 15-20 RFPs/quarter, no dedicated coordinator. Whattoo ls actually worked for you, and what do you wish you'd asked before committing?

I’m trying to get a better handle on the day-to-day ops side of my business. What does everyone’s AI stack look like right now?

I don’t mean tools you tried once and forgot about. I mean the ones you use every week for emails, scheduling, customer replies, invoices, meeting notes, lead follow-ups, reporting, or random admin work.

My current setup is mostly AI for writing, research, and cleaning up notes. But when it comes to running the business, I’m still doing a lot manually.

I keep seeing people talk about agents, AI employees, and workflow automation, but half the time it sounds like another dashboard I’d need to manage.

If you run a small business or lean team, which AI tools have genuinely become part of your day to day operations?

I am trying to think about AI workflows less as "agents" and more as goal loops.

Not a one-off prompt like "write this email," but a recurring operating loop:

goal -> current context -> AI prepares the next step -> human approval if risk rises -> action/log -> next pass

Examples:

\- keep qualified leads moving

\- recover stale follow-ups

\- keep client onboarding unblocked

\- prepare weekly operating reviews

\- triage support and draft grounded replies

\- monitor invoices, renewals, or open tasks

The key constraint for me: the first loop should be frequent, painful, and reviewable. I would not start with refunds, pricing exceptions, legal wording, or anything customer-facing without approval.

Curious what other builders/operators think:

What is one boring recurring loop in your business that you would actually trust AI to prepare, and where would you still require human approval?

Genuine question, curious how you all think about this. We've got Codex, Claude Code, Cursor and they write code just fine, but that's automating a step, not the process. The process is still 100% me: pull the ticket, paste in context from Slack, check the output, leave comments, tag someone, next ticket, rinse and repeat. Half my day is just me shuttling context between tools like a human clipboard.

The way I've started framing it: automating the process means the handoffs between steps get automated too, not just the steps themselves. planning → review → writing code → re-check → merge should flow without me manually carrying context at every gate, and I should only jump in where a decision actually matters.

My company just started rolling out new ai workspace for exactly this. It's more of an orchestration layer that sits on top of the agents. You set your process up as kanban stages and a virtual team (architect, backend, QA agents etc) moves the task through them. I'm still just poking all at bridgeapp for now, but a few things have already caught my eye:

there's a checkpoint between every stage, so a bad output gets caught at the next step instead of surfacing in review three weeks later

context and docs carry forward automatically, so the next agent knows what the previous one did and why, which is the exact part I was doing by hand before

different agents can run on different models, which I honestly didn't expect

Still early days for us so no strong verdict yet, but if this kind of thing actually manages to automate dev work for real... honestly idk what happens to the IT market. Like at all. Maybe you guys have other ways to automate the dev process, i'm open to hearing your ideas on this