Florian Decludt

Demand Generation Manager @ Clutch

Growth Marketer

Click FOLLOW for #AI #Automation #Future Trends #Strategies #Leadership| Author & Podcast host | GPT, AI & RPA | Your favorite AI-led Automation newsletter & community ⬇️

Nandan Mullakara is a globally recognized leader in AI‑led automation and Agentic AI. He is co‑author of the best‑selling books *Agentic Artificial Intelligence* and *Robotic Process Automation Projects*, both widely cited in academia and industry. He is profiled in Onalytica's Who's Who in Automation.

As the founder of Bot Nirvana, Nandan advises enterprises on Agentic AI and Intelligent Automation—from strategy and operating models to measurable outcomes. He also hosts the Bot Nirvana AI & Automation Podcast, ranked among the top shows in its category for pragmatic, practitioner‑first conversations.

Previously, Nandan led Robotic Process Automation (RPA) practice and Application Managed Services (AMS) projects at Fujitsu Americas. He has driven AMS initiatives for global companies such as Honeywell, BCBS, Canon, and Embraer.

His insights and articles have been featured in esteemed outlets such as Forbes, Solutions Review, Packt, and more.

Nandan Mullakara

𝗜 𝗮𝗻𝗮𝗹𝘆𝘇𝗲𝗱 𝟭𝟬𝟬𝘀 𝗼𝗳 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲, 𝗢𝗽𝗲𝗻𝗔𝗜 & 𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲.

Here are the top ideas for your org right now:
(Top 100 in image, resource links in comments)

𝗦𝗔𝗟𝗘𝗦 & 𝗠𝗔𝗥𝗞𝗘𝗧𝗜𝗡𝗚
🔹 AI SDR: Automates outbound, follow-ups, and meeting booking.
🔹 Lead Scoring Agent: Scores leads using firmographic and behavioral data.
🔹 Market Intelligence Agent: Synthesizes external data for trends and opportunities.
🔹 Personalization Agent: Serves one-to-one offers using real-time behavior signals.

𝗖𝗨𝗦𝗧𝗢𝗠𝗘𝗥 𝗢𝗡𝗕𝗢𝗔𝗥𝗗𝗜𝗡𝗚
🔹 Eligibility & KYC Agent: Checks eligibility rules and performs KYC autonomously.
🔹 Welcome Journey Orchestrator: Triggers welcome emails and nudges automatically.

𝗖𝗨𝗦𝗧𝗢𝗠𝗘𝗥 𝗦𝗘𝗥𝗩𝗜𝗖𝗘
🔹 Multichannel Service Agent: Handles chat, email, and messaging end-to-end.
🔹 Sentiment & Escalation Agent: Detects dissatisfaction and routes to humans instantly.
🔹 Call Center QA Agent: Evaluates 100% of calls, not just 2-5%.
🔹 Autonomous Service Agent: Resolves routine cases without human intervention.

𝗖𝗢𝗠𝗣𝗟𝗜𝗔𝗡𝗖𝗘 & 𝗟𝗘𝗚𝗔𝗟
🔹 Fraud Detection Agent: Detects anomalous transactions at machine speed.
🔹 Legislative Analysis Agent: Analyzes regulations and summarizes business impact.
🔹 Contract Redlining Assistant: Proposes edits and highlights contractual risk.
🔹 Compliance Reporting Agent: Compiles data and drafts regulatory reports.

𝗙𝗜𝗡𝗔𝗡𝗖𝗘
🔹 Revenue Cycle Agent: Automates billing submissions, denials, and follow-ups.
🔹 Close Automation Agent: Reconciles and validates ledgers at month-end.
🔹 Loan Underwriting Agent: Evaluates applicants and recommends credit decisions.
🔹 Pricing Analyst Agent: Recommends dynamic prices based on demand signals.

𝗛𝗨𝗠𝗔𝗡 𝗥𝗘𝗦𝗢𝗨𝗥𝗖𝗘𝗦
🔹 HR Knowledge Assistant: Answers policy and benefits questions instantly.
🔹 Employee Onboarding Coach: Guides new hires through tasks and learning content.
🔹 Talent Matching Agent: Matches employees to internal projects and open roles.

𝗜𝗡𝗙𝗢𝗥𝗠𝗔𝗧𝗜𝗢𝗡 𝗧𝗘𝗖𝗛𝗡𝗢𝗟𝗢𝗚𝗬
🔹 Network Operations Agent: Detects and remediates outages from telemetry.
🔹 Architecture Review Agent: Analyzes system diagrams for risks and dependencies.
🔹 Reporting & Audit Agent: Prepares IT compliance and audit evidence packs.

𝗦𝗨𝗣𝗣𝗟𝗬 𝗖𝗛𝗔𝗜𝗡
🔹 Predictive Maintenance Agent: Predicts failures and schedules maintenance proactively.
🔹 Logistics Optimization Agent: Optimizes routes, loads, and carrier selection.
🔹 Supply Chain Planner Copilot: Simulates disruption scenarios and highlights risks.

𝗩𝗘𝗡𝗗𝗢𝗥𝗦 & 𝗣𝗔𝗥𝗧𝗡𝗘𝗥𝗦
🔹 Vendor Onboarding Assistant: Collects documents and configures terms automatically.
🔹 Partner Contract Monitor: Tracks expirations, obligations, and SLAs in real time.

Join our new session on Agentic AI for more: https://lnkd.in/gdEnM6fc

----

🎯 Follow for Agentic

⚡ 𝗧𝗵𝗲 $𝟱.𝟱 𝗧𝗥𝗜𝗟𝗟𝗜𝗢𝗡 𝗧𝗮𝗹𝗲𝗻𝘁 𝗪𝗮𝗿 𝗝𝘂𝘀𝘁 𝗦𝘁𝗮𝗿𝘁𝗲𝗱

94% of CEOs say AI skills are priority #1. 
But here's the problem: 90% of enterprises can't find the talent.

Here are a few current & emerging AI roles they are looking for:

Management Roles
🔹 Chief AI Officer: Leads AI strategy and oversight.
🔹 VP, Head of AI: Leads AI portfolio, scaling teams and capabilities globally.
🔹 Chief Data and AI Officer: Oversees data, analytics, and AI as strategic assets.
🔹 Head of Applied AI: Drives high-impact AI use cases into production environments.
🔹 Director of AI: Manages AI roadmap, delivery teams, and stakeholder expectations.
🔹 Responsible AI Lead: Champions responsible AI practices & accountable deployment.
🔹 Director of AI Transformation and Strategy: Orchestrates AI-driven change across BUs
🔹 AI Strategy Manager: Identifies opportunities, builds business cases, prioritizes AI initiatives.
🔹 AI Product Manager: Manages the AI product development lifecycle end to end.

Tech Roles
🔹 AI Architect: Designs scalable AI system architecture.
🔹 Model Manager: Oversees the AI model lifecycle.
🔹 Model Validator: Verifies AI model accuracy and reliability.
🔹 AI Developer: Builds and implements Gen AI & Agentic systems.
🔹 Model/ML Engineer: Develops and deploys machine learning models.
🔹 AI Application Developer: Integrates AI into business applications.
🔹 AI App Dev Platform Architect: Designs AI development platform infrastructure.
🔹 AI Cybersecurity Analyst: Mitigates AI-specific cybersecurity risks.
🔹 AI Redteam Engineer: Tests AI systems for weaknesses.
🔹 Prompt Engineer: Optimizes prompts for AI models.
🔹 Data Scientist: Analyzes data for AI insights.
🔹 Data Engineer: Manages data pipelines for AI.

Business Roles (Mostly Emerging)
🔹 AI Customer Success Manager: Predicts churn, drives adoption using AI-based customer insights.
🔹 AI Accounting Analyst: Applies AI to streamline accounting, forecasting, and reconciliations.
🔹 AI-Powered Auditor: Uses AI to detect anomalies, fraud, and control weaknesses.
🔹 AI Financial Reporting Specialist: Automates financial reporting, analysis, and narrative insights.
🔹 AI Compliance Analyst: Monitors regulations, flags risks with AI-driven surveillance tools.
🔹 AI Payroll Specialist: Uses AI to forecast, validate, and optimize payroll operations.
🔹 AI HR Data Analyst: Analyzes workforce data using AI for HR decision support.

This is a list of roles based on Gartner & other reports, and my general observations. 

𝗪𝗵𝗮𝘁 𝗻𝗲𝘄 𝗔𝗜 𝗿𝗼𝗹𝗲𝘀 𝗮𝗿𝗲 𝘆𝗼𝘂 𝘀𝗲𝗲𝗶𝗻𝗴 𝗲𝗺𝗲𝗿𝗴𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻?

Drop them below and let's build a comprehensive list! 

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

POV: Your team watching AI Engineers get hired at 2x their salary.

The RPA Architect who built your automation platform?
They could be your AI Architect tomorrow.

The only difference:
→ One learned agentic AI frameworks
→ One built agents with Google ADK
→ Now making $100k+ more

Your automation team already orchestrates workflows, handles exceptions, and integrates systems.

That's the foundation of agentic AI.
They don't need replacement. 

They need 20 - 40% upskilling + strategic positioning.

Stop hiring over your own talent.

What are you doing? 

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

𝗨𝗻𝗽𝗼𝗽𝘂𝗹𝗮𝗿 𝗼𝗽𝗶𝗻𝗶𝗼𝗻: 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗺𝗼𝘀𝘁 𝗔𝗽𝗽𝘀 𝗮𝗻𝗱 𝘁𝗼𝗼𝗹𝘀 𝘄𝗶𝘁𝗵𝗶𝗻 𝘁𝗵𝗶𝘀 𝗱𝗲𝗰𝗮𝗱𝗲.

Here's why:
Traditional Apps & Tools: Static rule-based systems
Agent Networks: Dynamic systems that think, communicate, and act autonomously

The architecture is already here:

🎯 𝗔𝗣𝗣𝗟𝗜𝗖𝗔𝗧𝗜𝗢𝗡 𝗟𝗔𝗬𝗘𝗥

What users interact with:
- Personal assistants that actually get things done
- E-commerce apps that predict what you need
- Enterprise systems that run themselves

🤖 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗟𝗔𝗬𝗘𝗥

The "brain" of the operation:
- Smart agents handling tasks automatically
- Agentic protocols enabling seamless coordination:
    - MCP for tool integration
    - A2A for agent negotiation
    - ACP for secure messaging
    - ANP for network coordination

⚙️ 𝗜𝗡𝗙𝗥𝗔𝗦𝗧𝗥𝗨𝗖𝗧𝗨𝗥𝗘 𝗟𝗔𝗬𝗘𝗥

The foundation:
- Servers, APIs, cloud services
- Data resources and storage
- The "factory floor" that powers everything

The shift is happening NOW:
- Capital One risk agents make real-time decisions for fraud and operations.
- Anthropic’s MCP has gone viral since its November 2024 launch
- AWS AgentCore infra has scaled to millions of users now

Instead of different App licenses & SaaS subscriptions, you'll have AI agents that:
- Handle your CRM automatically
- Manage your marketing campaigns
- Coordinate your team's workflow
- Make data-driven decisions 24/7

The companies building these layers now will dominate tomorrow.

Join our next Agentic Inner Circle: Beyond RPA - The New Coded Automation!

https://lnkd.in/g695QhxX

Too aggressive or inevitable? What's your take?.

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

I've been implementing document automation for Fortune 500 companies since the RPA era.

Here's the evolution I'm seeing from static OCR to Agentic OCR:

The 5-Stage Agentic OCR Framework:

1. 𝗢𝗕𝗦𝗘𝗥𝗩𝗘
→ Monitor environments and detect document arrivals automatically
→ No manual uploads or triggers needed

2. 𝗣𝗥𝗢𝗖𝗘𝗦𝗦
→ Analyze using advanced reasoning and contextual understanding
→ General-purpose AI handles ANY document format

3. 𝗗𝗘𝗖𝗜𝗗𝗘
→ Make autonomous decisions based on situational context
→ Auto-approve high-confidence extractions (>90%)

4. 𝗔𝗖𝗧
→ Take actions and adapt strategies based on real-time results
→ Push to ERPs, notify stakeholders, route exceptions

5. 𝗟𝗘𝗔𝗥𝗡
→ Improve performance through continuous feedback loops
→ Every human intervention trains the system

What makes this different from traditional OCR:
🔹 Not format-specific - handles invoices, contracts, forms, emails
🔹 Not brittle - adapts to variations without retraining
🔹 Not expensive - costs 40- 60% less than traditional AI/ML OCR solutions
🔹 Not static - continuously improves through feedback

Companies that solve extraction at scale unlock:
→ Faster decision-making
→ Better customer experiences
→ Competitive advantage through data

The future of automation isn't better scripts.
It's orchestrated teams of specialized AI agents that observe, learn, and adapt.

The architecture pattern we've proven with OCR applies to virtually every business process.

Join our next session on Agentic AI: https://lnkd.in/gkh98D4q

Ready to build your AI agent team?

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

"𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘄𝗶𝗹𝗹 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗥𝗣𝗔."

No. Here's what's actually happening:

5 shifts from Rule-Based to Goal-Oriented automation:

𝟭. From brittle to self-healing
RPA breaks when an App updates.
Agentic AI adapts to app updates and more in real-time.

𝟮. From path-oriented to goal-oriented
RPA needs every If/Then mapped.
Agentic AI gets a goal, e.g., "flag unpaid invoices," and it figures out the path. 

𝟯. From idle scripts to proactive agents
Traditional RPA waits for triggers.
Agentic loops check for events, not waiting for them.

𝟰. From linear steps to agentic loops
Invoice processing in RPA = a rigid chain of handoffs.
In Agentic AI, each step becomes its own loop:
→ OCR Agentic Loop (extract + validate + self-correct)
→ 3-Way Match Agentic Loop (resolve discrepancies autonomously)
→ Approval Agentic Loop (handle exceptions dynamically)

5. From "replace" to "hybrid."
The winning architecture in 2026:
→ Agentic AI = the Intelligence (the agentic loop that autonomously acts)
→ RPA = the Muscle (precise task flows & deal w/legacy systems)
→ BPM = the Rails (end-to-end workflows and audit trails)

But here's what most people overlook:
These agentic loops run autonomously. They perceive, reason, and act continuously.
That means they're better contained w/ dedicated hardware to run within.
In the diagram, those are Mac Minis & can be any server.  

Think of it like giving each agent its own space (sandbox).
→ The OCR agent runs inside its box
→ The 3-Way Match agent runs inside its box
→ The Approval agent runs inside its box

No agent roams free. Every loop is isolated, observable, and killable.

This is the part that the "just deploy AI agents" crowd skips.
In the enterprise, autonomy without containment isn't innovation - it's a risk.

The companies still debating "RPA vs Agentic AI" are asking the wrong question.

The right question: How do we wire them together?

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

The 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 era is ending. True 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 era has begun. And it's replacing apps! 

I've been playing around with OpenClaw (Clawdbot/Moltbot). 

And I can see how it can replace 80% of the apps.

First, here is the 𝗜𝗻𝗰𝗼𝗻𝘃𝗲𝗻𝗶𝗲𝗻𝘁 𝗧𝗿𝘂𝘁𝗵:
→ Modern SaaS apps: 70-80% are just UI layers over APIs
→ Legacy systems: Only 30-40% have usable APIs
→ Everything else: AI controls the browser directly

OpenClaw handles both. APIs where they exist, browsers where they don't, and carries out your intent.

𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝘄𝗼𝘂𝗹𝗱 𝗟𝗼𝗼𝗸 𝗟𝗶𝗸𝗲 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲:

𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲 𝗖𝗥𝗠: "Update the Johnson account with today's call notes and move to negotiation stage."  No login. No 12-click workflow. No form fields to hunt for. Done.

𝗘𝘅𝗽𝗲𝗻𝘀𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 (𝗖𝗼𝗻𝗰𝘂𝗿/𝗦𝗔𝗣):  Snap a receipt. "Expense this to the Dallas client meeting." AI extracts data, categorizes expenses, and submits a report. 

𝗘𝗥𝗣 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗦𝗔𝗣/𝗢𝗿𝗮𝗰𝗹𝗲): "Create a PO for 500 units of SKU-7834 from our preferred vendor." AI navigates SAP, validates inventory, and routes for approval.

𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴:

Your teams stop switching between dozens of applications. Instead, one conversational AI:
→ Accesses all systems via API
→ Maintains context across platforms
→ Self-hosts on your infrastructure (your data stays private)
→ AI controls browsers where APIs don't exist
→ Executes system commands, scripts, and file operations
→ Extends with custom skills for your workflows

This Isn't RPA 2.0. This Is Beyond RPA.
- You automated the clicking.
- You built bots for repetitive tasks.
- Now, AI eliminates the apps that bots were clicking through.

Here's the Architecture:
→ Self-hosted: Mac/Windows/Linux + Anthropic/OpenAI/local LLMs (your data stays private)
→ Universal integration: Teams, Slack, iMessage, WhatsApp, Telegram, Signal, and more
→ Persistent memory: Learns preferences, maintains context, evolves with your org
→ Browser control: Navigates sites, fills forms, extracts data (for systems without APIs)
→ System access (recommend sandboxed mode): File operations, shell commands, scripts 
→ Infinitely extensible: Community skills + AI-generated plugins for custom workflows

My Take:
Within 3 years, measuring success by apps and automations will be outdated.
The winning organizations will ask: "How many intelligent agents do we orchestrate?"

OpenClaw is open-source proof that this future is already here.
The app era didn't die. It just got absorbed by something smarter.

What's your strategy when apps become workflows + conversation?

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️OpenClaw handles both. APIs where they exist, browsers where they don't.

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 (𝗔𝗣𝗔) 𝗶𝘀 𝗻𝗼𝘁 𝗥𝗣𝗔 𝗼𝗿 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 

But it just might outshine AI agents in Enterprises. 

🤔 Why?

- AI agents currently struggle with complex tasks.
- Because LLMs' reasoning is still maturing.
- So enterprise use is currently limited.

🔄 Enter Agentic Process Automation:

- Controlled AI agents in workflows.
- Operate within strict guardrails.
- Has agency in a defined context.

🔧 What Happens to RPA?

- Still best for structured tasks
- Ideal for high-volume, repetitive tasks.
- Lower running costs as no token/AI model costs.

🚀 Market is evolving fast, tools & tech are changing.

It's not which tool wins. Use the right tool for the right job.

What do you think?

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

🚀 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀 𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝗳𝗶𝗻𝗮𝗻𝗰𝗲 — 𝗮𝗿𝗲 𝘄𝗲 𝗿𝗲𝗮𝗱𝘆?
IBM's report uncovers the urgent need for change in finance.
(also applicable to other businesses)

🔍 𝗟𝗲𝗴𝗮𝗰𝘆 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝘀 𝗰𝗮𝗻'𝘁 𝗸𝗲𝗲𝗽 𝘂𝗽:
Real-time governance is crucial.

🧠 𝗖𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗿𝗶𝘀𝗸 𝗶𝗻 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀:
Misalignment causes drift and deception.

📚 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝗮 𝗱𝗼𝘂𝗯𝗹𝗲-𝗲𝗱𝗴𝗲𝗱 𝘀𝘄𝗼𝗿𝗱:
Enforce strict reset and audit policies.

🔍 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗶𝘀 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹:
Prevent bias and data misuse.

📊 𝗔𝗴𝗲𝗻𝘁 𝗿𝗲𝗴𝗶𝘀𝘁𝗿𝗶𝗲𝘀 𝗼𝘃𝗲𝗿 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀:
Track every agent like a microservice.

🔧 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗯𝘆 𝗱𝗲𝘀𝗶𝗴𝗻:
It's imperative for the success of AI deployment.

Are we leading AI design or reacting to its outcomes?
𝗗𝗲𝗹𝘃𝗲 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗿𝗲𝗽𝗼𝗿𝘁 𝗮𝗻𝗱 𝘀𝗵𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀!

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

Agentic AI: The next big wave in finance is here!

𝟰-𝟴 𝗺𝗼𝗻𝘁𝗵𝘀 → 𝟮 𝘄𝗲𝗲𝗸𝘀. 𝟵𝟬%+ 𝘁𝗶𝗺𝗲 𝗿𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻.

That's the new software development timeline.

And it is making traditional development obsolete.


Here's what happened (Anthropic report):

An enterprise team scoped a complex system integration.
Conservative estimate: 4-8 months.

They used Claude Code instead.

Delivered in 2 weeks! 

Let that number sink in.


Many of your competitors are already operating at that speed.

Here’s how: 

The old playbook:

✗ Engineers write every line manually
✗ Weeks to onboard to new codebases
✗ Weeks-to-months development cycles
✗ Cross-team bottlenecks kill momentum
✗ Tech debt grows faster than you can pay it down
✗ Most good ideas never get built (too expensive)

The new playbook:

✓ Engineers orchestrate AI agents
✓ Hours to achieve codebase fluency
✓ Hours-to-days shipping cycles
✓ Parallel multi-agent execution
✓ AI hunts down and eliminates tech debt
✓ Previously unviable projects become possible

The critical insight everyone gets wrong:

People think this is about AI "replacing" developers.

Wrong.

Research proves it: 
60% of dev work now uses AI, but only 0-20% is "fully delegated."

This is about new skill sets, not automation.

The developer role didn't die.

It's evolved.

From code writer → Intelligence orchestrator.

Master these 7 skills or become irrelevant:

𝟭. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗙𝗿𝗮𝗺𝗶𝗻𝗴: Surgical precision in defining intent
𝟮. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Designing durable AI systems
𝟯. 𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝗹𝗲𝗴𝗮𝘁𝗶𝗼𝗻: Coordinating parallel workflows
𝟰. 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁: Quality & risk evaluation
𝟱. 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝘀𝗶𝗴𝗻: Well designed quality gates
𝟲. 𝗧𝗼𝗼𝗹𝗰𝗵𝗮𝗶𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: AI-operable infrastructure
𝟳. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Human-AI symbiosis

Your uncomfortable choice:

Keep operating like before while competitors ship 97% faster.

Or retrain your team on these skills before the gap becomes permanent.

How is your team evolving?

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

𝗜'𝘃𝗲 𝘀𝗲𝗲𝗻 𝘁𝗵𝗶𝘀 𝗺𝗼𝘃𝗶𝗲 𝗯𝗲𝗳𝗼𝗿𝗲. 𝗔𝗻𝗱 𝗜 𝗸𝗻𝗼𝘄 𝗵𝗼𝘄 𝗶𝘁 𝗲𝗻𝗱𝘀.

Everyone's saying traditional coding is dying. AI coding is the future.

But here's what decades in tech have taught me:  
I abandoned mainframes for Java. "Dead technology," they said.
Then I pivoted to AI. "Future-proof," they promised.  

Today? My mainframe colleagues are earning premium rates for "obsolete" skills.

𝗬𝗲𝘀, 𝗜'𝘃𝗲 𝘀𝗲𝗲𝗻 𝘁𝗵𝗶𝘀 𝗺𝗼𝘃𝗶𝗲 𝗯𝗲𝗳𝗼𝗿𝗲:
→ COBOL developers now command premium rates
→ Mainframe specialists are irreplaceable
→ Legacy ERP experts are unicorns

Now, we're witnessing the emergence of two parallel tracks:
→ Traditional coding
→ AI-assisted coding

The assumption: AI will consume everything.
The reality: Technology evolves counterintuitively.

Here's my prediction: In 3 years, developers who deeply understand code architecture, debugging, and system design will be worth 3-5x those who only know how to "vibe code".

𝗧𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗹𝗲𝗮𝗱𝗲𝗿𝘀:
Are you building a team that can maintain what AI creates? Or are you optimizing for short-term velocity at the cost of long-term stability?

Image: programminghumor dot io

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

𝗖𝗼𝗽𝗶𝗹𝗼𝘁𝘀 𝗮𝗿𝗲 𝗳𝗮𝗶𝗹𝗶𝗻𝗴.(1.8% conversion after heavy marketing)
Satya's response? Testing Clawdbot, the viral AI agent.

But Clawdbot (now Openclaw) isn't the solution.
(It's already a security nightmare.)

The real answer?

Better foundations and smarter adoption.

After 10 years in automation, I've watched the same pattern repeat:

→ Exciting launches 
→ Great potential, Cool Pilots  
→ No adoption, Doesn’t scale ❌

𝗜𝘁'𝘀 𝗻𝗲𝘃𝗲𝗿 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆.

It's the foundation work organizations skip:

❌ Building chaos instead of fixing broken processes
❌ Buying new platforms while existing tools collect dust
❌ Feeding AI systems garbage data from multiple sources
❌ Deploying without security, governance, or guardrails
❌ Celebrating POC wins, then watching them die in silence

𝗕𝘂𝘁 𝗧𝗲𝗰𝗵 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 ≠ 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻

Having AI isn't enough.

Your people must adopt it.

Here's my 𝟱𝗘 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 that transformed adoption rates:

- 𝗘𝗱𝘂𝗰𝗮𝘁𝗲: Show AI capabilities with real use cases
- 𝗘𝗺𝗽𝗼𝘄𝗲𝗿: Provide proper training and support
- 𝗘𝗻𝗮𝗯𝗹𝗲: Create space for experimentation
- 𝗘𝗻𝗴𝗮𝗴𝗲: Address concerns openly (especially job fears)
- 𝗘𝘅𝗲𝗰𝘂𝘁𝗲: Implement clear adoption strategies

𝗧𝘄𝗼 𝗽𝗮𝘁𝗵𝘀 𝗳𝗼𝗿𝘄𝗮𝗿𝗱:

𝗣𝗮𝘁𝗵 𝟭: Chase shiny AI → Skip foundation & adoption → Join the 85% that fail

𝗣𝗮𝘁𝗵 𝟮: Build foundations → Plan adoption → Move slower to scale faster

Choose wisely. What's your path? 

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

𝗔𝗜 𝗳𝗮𝗶𝗹𝘀 𝗮𝘁 𝟵𝟲% 𝗼𝗳 𝗷𝗼𝗯𝘀 (new AIS study)

Let that sink in.

A new study just shattered the narrative that AI is coming for everyone's job tomorrow. 

The reality? The best AI (Claude Opus 4.5) can ONLY fully automate 𝟯.𝟳𝟱% 𝗼𝗳 𝘁𝗮𝘀𝗸𝘀, it's supposed to "replace."

But here's what a decade in automation has taught me:
100% automation should never be the goal.

The real opportunity is in the 30-75% automatable tasks across 80% of the work. It's a spectrum, not a binary switch.

Yet we're watching Big Tech pay influencers $500K+ to sell you the AI dream while companies struggle with basic implementation.

The problem isn't the technology. It's the approach.
→ You can't agent-ify chaos and expect results
→ You can't automation-wash your broken processes
→ You can't skip the foundational work and jump to "AI will fix everything."

AI will absolutely transform work. 
It will replace SaaS tools and change software. 

But it requires us to put in the work to make it work FOR us.

The gap isn't in AI capability. 
It's in the focus - demos & hype vs doing the work. 

Stop chasing 100% automation fantasies.
Start building the foundation for Agentic augmentation.

The companies winning with AI are not chasing pipe dreams.
They're the ones who are putting in the work now.

What do you think? 

🔗 Link to Center for AI Safety study in comments

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

𝗜'𝗺 𝗵𝗲𝗮𝗿𝗶𝗻𝗴 𝘀𝘁𝗼𝗿𝗶𝗲𝘀 𝗮𝗯𝗼𝘂𝘁 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗳𝗮𝗶𝗹𝘂𝗿𝗲𝘀.

Employees are NOT using it - they don't see the value or don't know how to. 

And I know exactly why...

All fancy AI licenses are worthless because you are:

🚫 Throwing licenses at employees
🚫 Forcing top-down adoption
🚫 Assuming people will "figure it out"
🚫 Focusing only on technology

The truth?

Having AI isn't enough; effective adoption is key.

Here's what successful companies do differently (5Es):

✅ Educate: Show AI capabilities w/ use cases & benefits
✅ Empower: Provide proper training and support
✅ Enable: Create space for experimentation 
✅ Engage: Address concerns openly
✅ Execute: Implement clear adoption strategies

Here's a 3-step framework that transformed our AI/RPA Automation adoption rates 👇

Start with WHY
- Connect AI/Automation to business objectives
- Show Organizational & personal benefits
- Address replacement fears head-on

Enable through HOW
- Structured training programs
- Hands-on workshops
- Real-world use cases

Support with WHAT
- Clear implementation roadmap
- Regular feedback sessions
- Celebration of small wins

Remember: Having AI isn't enough.

Success lies in your people adopting it.


Join our next session on Agentic AI:  https://lnkd.in/gPM_qT4T

What do you think?

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝗱𝗶𝗻𝗴 𝗶𝘀 𝗱𝘆𝗶𝗻𝗴. 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲. 𝗢𝗿 𝗶𝘀 𝗶𝘁?

Everyone's saying it. But here's what decades in tech have taught me:

I abandoned mainframes for Java. "Dead technology," they said.
Then I pivoted to AI. "Future-proof," they promised.

Today? My mainframe colleagues are earning premium rates for "obsolete" skills.

Now, we're witnessing the emergence of two parallel tracks:
→ Traditional coding
→ AI-assisted coding

The assumption: AI will consume everything.
The reality: Technology evolves counterintuitively.

MY TAKE:
Technology transitions are counterintuitive.
The obsolete becomes scarce. The scarce becomes premium.

The question isn't which will win.
It's which path aligns with YOUR long-term value.
AI coding = broader accessibility, faster iteration
Traditional coding = deep expertise, system architecture, debugging complex failures

The winners? Those who master BOTH.
What's your bet?

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

🤖 𝗧𝗵𝗲 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗳𝗮𝗺𝗶𝗹𝘆 𝗴𝗼𝘁 𝗮 𝗩𝗘𝗥𝗬 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗺𝗲𝗺𝗯𝗲𝗿...

Meet the family:
C: The respected grandfather. Built everything.
C#: The overachiever. C's ambitious daughter.
Java: The reliable corporate employee. Does the job.
PHP: The web's workhorse. Gets things done quietly.

Then there's RPA.

RPA showed up to the family dinner with:
• No code required
• Drag-and-drop stuff  
• "I'll just record what humans do."

How did we get here?

Simple. Business moved faster than IT departments.

People needed automation NOW.
Not after 6 months of development.
Not after learning C or C#.

NOW.

So RPA said, "I'll be the automation that business users can use."

The result? 

RPA is the family rebel who:
✅ Enables non-technical people 
✅ Delivers results in days, not months  
✅ Doesn't care about "proper" programming
✅ Just gets most of the UI automation stuff done

Sometimes the best solution isn't the most elegant one.

It's the one that works for real people, solving real problems.

What do you think? Is RPA or Low-code the future?

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

𝗨𝗻𝗽𝗼𝗽𝘂𝗹𝗮𝗿 𝗼𝗽𝗶𝗻𝗶𝗼𝗻: 𝗬𝗼𝘂𝗿 "𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁" 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁.

Here's what I mean:

Traditional automation waits for events. 
A form gets submitted → workflow triggers → rules execute → process completes.

That's not intelligence. That's orchestrated obedience.

Real intelligence? It doesn't wait for triggers. It actively pursues objectives.

I've seen this evolution across implementations:
- 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗣𝗔/𝗗𝗣𝗔: Event-Based: "When X happens, do Y." 
- 𝗔𝗴𝗲𝗻𝘁𝗶𝗰: "Achieve outcome Z, continuously adapt."

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻:

Take KYC (Know Your Customer) in financial services. (See attached from BCG)

An 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 workflow:

→ 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 𝗔𝗴𝗲𝗻𝘁: Cross-checks company data against trusted sources in real-time 
→ 𝗗𝗼𝗰 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁: Auto-extracts and organizes data from unstructured documents 
→ 𝗦𝗰𝗿𝗲𝗲𝗻𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁: Runs sanctions/PEP checks, escalating only genuine high-risk matches 
→ 𝗟𝗟𝗠 𝗞𝗬𝗖 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁: Compiles regulatory-ready files with analysis and synthesis 
→ 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁: Completes account creation in minutes with embedded quality checks 
→ 𝗣𝗲𝗿𝗽𝗲𝘁𝘂𝗮𝗹 𝗞𝗬𝗖 𝗔𝗴𝗲𝗻𝘁: Continuously monitors ownership changes and emerging risks 

The difference shows up in every layer:
𝗣𝗲𝗿𝗰𝗲𝗽𝘁𝗶𝗼𝗻 – Not just reading inputs, but fusing multi-source signals into live context 
𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 – Not just IF/THEN rules, but reasoning with context and AI models 
𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 – Not just executing steps, but optimizing against goals and guardrails 
𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 – Not just running workflows, but orchestrating dynamic tool chains 
𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 – Not just logging, but learning and improving autonomously

And here's the part nobody talks about: Governance has to flip completely.
𝗢𝗹𝗱 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: "Did it run the right steps?" 
𝗡𝗲𝘄 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: "Did it achieve the right goal while staying compliant?"

The organizations leading this shift see automation as an autonomous operating system - not a task executor, but a goal achiever.

Is your architecture ready for agents that are autonomous, not just bots that respond to events?

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

💰 𝗜'𝘃𝗲 𝗯𝗲𝗲𝗻 𝘀𝗲𝗹𝗹𝗶𝗻𝗴 𝗺𝘆 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝘀𝘁𝗼𝗰𝗸𝘀, 𝗮𝗻𝗱 𝗵𝗲𝗿𝗲'𝘀 𝘄𝗵𝘆 𝗜 𝗰𝗮𝗻'𝘁 𝘀𝘁𝗼𝗽...

As someone deep into automation and AI, I've been testing AI models and agents intensively. 

The more I test, the more convinced I become:

𝗧𝗵𝗲 𝗯𝗹𝗼𝗼𝗱𝗯𝗮𝘁𝗵 𝗶𝗻 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝘀𝘁𝗼𝗰𝗸𝘀 𝗵𝗮𝘀 𝗷𝘂𝘀𝘁 𝗯𝗲𝗴𝘂𝗻.

📉 𝗧𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗱𝗼𝗻'𝘁 𝗹𝗶𝗲: Yesterday, US software stocks fell 7%. Legal software got crushed. JPMorgan's software index is down 18% this year.

And Salesforce is a case in point: 

🔴 𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲: 𝗧𝗵𝗲 𝗢𝗿𝗶𝗴𝗶𝗻𝗮𝗹 𝗦𝗮𝗮𝗦 𝗖𝗼𝗺𝗽𝗮𝗻𝘆'𝘀 𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝗖𝗿𝗶𝘀𝗶𝘀

Salesforce literally invented the SaaS category. At Dreamforce, they went all-in on AI agents. Big announcements. Big promises. Customer and market response?

A nosedive.

Why? Because they're trying to dress up traditional software with Agentic AI.

Meanwhile, Anthropic released Cowork that targets all areas of businesses. The legal tech took a big beating as the cowork plugin can now do contract review, compliance checks, legal briefings, and more.
 
There are some sales plugins and more features, like CRM, coming from Anthropic and/or other providers. 

✅ Here's what's different with Anthropic's approach:

- Improves with every model release
- Native agentic architecture (not bolted on)
- Configurable to your organization's playbook
- Works across your workflows, not just one silo

🚨 The brutal lesson: You can't dress up legacy/application-era software for the agentic era.
𝗬𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝗲 𝗮𝗴𝗲𝗻𝘁𝗶𝗰-𝗳𝗶𝗿𝘀𝘁.

💡 What most people are missing:
We're witnessing the transition from Applications to what I call the 𝗔𝗜 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺 Era.

Not better SaaS apps. A unified intelligence layer that:

- Understands your intent
- Orchestrates APIs & browser action
- Collaborates with humans and other bots
- Eliminates context-switching and breaks down silos

Here's the brutal irony companies like Salesforce are learning right now:

They built their "Agentforce" on "model + wrapper + workflow,"  assuming the AI companies would stay neutral and keep selling them AI Inference.

Now 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗶𝘀 𝘀𝗵𝗶𝗽𝗽𝗶𝗻𝗴 𝗮 𝗯𝗲𝘁𝘁𝗲𝗿 𝘃𝗲𝗿𝘀𝗶𝗼𝗻 𝗼𝗳 "𝗺𝗼𝗱𝗲𝗹 + 𝘄𝗿𝗮𝗽𝗽𝗲𝗿 + 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄" with context and neatly packaged as plugins. The SaaS companies are getting cut out.

𝗠𝘆 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻: 80% of today's SaaS unicorns won't exist in their current form.

What do you think?

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

𝗧𝗵𝗲 𝗜𝗻𝘁𝗲𝗿𝗻𝗲𝘁 𝗳𝗲𝗲𝗱𝘀 𝗔𝗜. 𝗔𝗜 𝗳𝗲𝗲𝗱𝘀 𝘁𝗵𝗲 𝗜𝗻𝘁𝗲𝗿𝗻𝗲𝘁. 𝗦𝗲𝗲 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺? 🔄

Model collapse is imminent:
→ Bad data goes in
→ Amplified bad data comes out
→ That o/p becomes training data for the next 
→ The cycle continues, getting worse each time

The solution? Start w/data responsibility
Your AI is only as good as what you feed it.

Remember: 
The companies winning at AI aren't collecting the most data. 
They're collecting the RIGHT data.

Join our next session on Agentic AI: https://lnkd.in/gNwxX58X

What do you think? 

Image credit: Ralph Aboujaoude Diaz 

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

𝗜'𝘃𝗲 𝘀𝘁𝗼𝗽𝗽𝗲𝗱 𝗯𝘂𝘆𝗶𝗻𝗴 𝗦𝗮𝗮𝗦 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀.

In the last 30 days, I built 5+ custom applications using Claude Code:

- nandanmullakara dot com website (Next.js + React)
- Server monitoring system with daily alerts
- Content creation suite (LinkedIn & YT)
- Video Editor to edit from the transcript
- Personal LifeOS planning system
(Some of the code is public on my Github - check comments for link)

I haven't coded in 15+ years. I'm not a programmer.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜𝗧 𝗮𝗻𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗻𝗲𝗲𝗱 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱:

The economics of custom software just fundamentally changed.

Building enterprise applications used to require:
→ Infrastructure teams
→ Development resources
→ Months of timeline
→ Six-figure budgets
→ Ongoing maintenance overhead

Now it requires:
→ Clear requirements & specs
→ Agentic coding solutions (Claude Code, Cursor, etc.)
→ Validation & testing frameworks
→ Observability & monitoring setup
→ Security & access controls
→ Days to weeks timeline
→ Token costs (less headcount costs)

𝗧𝗵𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻?

Every SaaS tool you've adopted was a compromise.
You molded your processes to fit their limitations. You paid recurring fees for features you didn't need. 
You integrated systems that were never designed to work together.

That compromise era is ending.

𝗧𝗵𝗲 𝘁𝗵𝗿𝗲𝗲 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀:

Internet → Connected everything
Cloud → Eliminated infrastructure barriers
AI agents → Eliminating development barriers

We're at the intersection of all three.
The question isn't whether to build custom applications.

It's what you're building this quarter. See screenshots of some I built👇🏽

What custom applications would solve your biggest pain points today?

----

🎯 Follow for Agentic AI, Gen AI & RPA trends: https://lnkd.in/gFwv7QiX

Repost if this helped you see the shift ♻️

taplio

The Battle

Florian Decludt

Nandan Mullakara

Followers

Posts

Engagements

Likes

Performance ScoreNandan Mullakara's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Florian Decludt

Nandan Mullakara

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Nandan Mullakara's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreNandan Mullakara's LinkedIn Performance