Day 12 : AI Can Help the BA Find Business Solutions Inside the Company

 

Day 12 — AI Can Help the BA Find Business Solutions Inside the Company

Sometimes the customer requirement exposes an internal company problem.

For example:

Customer onboarding takes 20 days.

Investigation shows:

Manual partner configuration
Manual mapping requests
Multiple approvals
Manual testing
Manual deployment
Manual validation

The customer requirement has revealed an internal operational problem.

The BA can propose:

Partner Onboarding Automation

Potential capabilities:

Partner registration
→ Configuration
→ Mapping
→ Validation
→ Testing
→ Approval
→ Deployment
→ Monitoring

AI can help analyze the existing process and identify automation opportunities.

So the BA becomes a bridge between:

Customer Problems + Company Problems + Technology Solutions

hashtag#BusinessAnalyst hashtag#AI hashtag#Automation hashtag#ProcessImprovement hashtag#B2BIntegration 

CHAT GPT 6 ASTRA - Introduction - continuation

The Day Your Laptop Learned to Drive Remember when AI was just a smart pen pal in a chat window? You typed a prompt, it wrote a paragraph, and you still had to copy, paste, reformat, and click "submit" yourself. GPT-6 Astra just crossed the line from a chat partner to an operator. Here is what stepping into the Astra era actually looks like for a normal Tuesday morning over the next few years: Step 1: The End of "Copy-Paste" Busywork You don’t ask Astra to write an expense report or fill out a tax form anymore. You just say, "Sort my receipts and file this." Astra opens the browser, logs into the portal, maps the fields, cross-checks the data, and asks you only one clarifying question before hitting send . Step 2: From Prompt-to-Code to Prompt-to-Artifact You don't get handed a block of Python code to debug. You say, "Build a tracking dashboard for my small business," and Astra spins up the live web app, tests the login flow on a virtual screen, fixes its own broken buttons, and hands you a working URL . Step 3: The Quiet Shift in Human Time When an AI can handle 40-minute multi-step browser workflows in a fraction of the time , the value of "knowing where to click" plummets. What becomes expensive isn't execution—it’s taste, judgment, and knowing what question to ask in the first place. We aren't just getting a faster chatbot. We are moving from a world where we work with software to a world where we supervise it. What’s the very first tedious computer chore you’d hand over to Astra today if you could? hashtag#AI hashtag#CHATGPT hashtag#GPT6 hashtag#LLM hashtag#futureAI hashtag#Intelligence

Automate Resume Screening with TAOne.AI | Fast & Smart Talent Filtering Process 500+ Resumes in Hour

Hi Friends Good Day Hope you are doing good. I have created this presentation for Idea, which aims to address a significant challenge in talent acquisition: efficiently processing a large volume of resumes within tight deadlines. To develop this solution, I have utilized various AI/ML LLM models, including Cohere Embed, reinforcement learning (based on trial and error), and the Random Forest (decision trees) ML model, to analyze data and effectively process a greater number of resumes in a shorter time frame, ensuring the selection of more qualified and skilled candidates. For coding the algorithms in Python, I am leveraging Chat GPT and Google Bard LLMs. To articulate the Idea, the problem, the solution, and the marketing opportunities, I have employed the Lean Canvas Model, which provides a clear explanation of each section. I encourage you to watch the video and share any questions you may have, as your feedback will be invaluable in enhancing my solution. https://lnkd.in/dDwZWzfm Thanks