I share daily insights to help you thrive in the age of AI. Follow me to grow your career to new heights | Former @Accenture @TCS | Entrepreneur | DM for inquiries

I am a Data & Cloud Transformation Consultant with experience working across enterprise platforms, data initiatives, and AI-enabled transformation programs. My background spans consulting, business analysis, and large-scale delivery efforts, where I have supported organizations navigating complex change at the intersection of business strategy and technology execution.

Over the past few years, I have been closely involved in AI and digital transformation discussions, helping leaders think through how emerging technologies align with business goals, data readiness, and organizational realities. This experience reinforced my belief that successful and responsible AI adoption depends on strong data foundations, reliable cloud platforms, and disciplined execution.

My core strengths include structured analysis, data and system validation, requirements clarification, and cross-functional collaboration. I work closely with business stakeholders, architects, and engineering teams to translate strategic intent into actionable migration and transformation plans, including data mapping, validation criteria, integration readiness, and cutover preparation.

I am currently focused on Data & Cloud Migration and Transformation roles (contract or full-time), where enterprise experience, analytical rigor, and collaboration are critical to success. I continue to build my capabilities in AI-aware transformation, with a long-term interest in advisory roles that are grounded in real-world data, cloud, and delivery experience.

Naveeta Sehgal

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

⚡ 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! 

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The AI world is about to undergo a radical transformation - the landscape will look completely different by 2026.

Nvidia Just Declared: The Future Is SLMs, Not Bigger LLMs.

𝗛𝗲𝗿𝗲'𝘀 𝗪𝗵𝘆 𝗧𝗵𝗮𝘁 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴.

As per their groundbreaking 2025 research, hybrid SLM systems have just achieved:
- 𝟭𝟬-𝟯𝟬𝘅 𝗰𝗵𝗲𝗮𝗽𝗲𝗿 operations
- 𝟱𝟬𝘅 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗶𝗻 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝘁𝗮𝘀𝗸𝘀
- Lower inference 𝘁𝗶𝗺𝗲𝘀 for on-device use
- 𝗠𝘂𝗰𝗵 𝗹𝗲𝘀𝘀 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀 for structured outputs

But wait—it gets crazier.

Even OpenAI (yes, the "bigger is better" company) just proved this with GPT-4b Micro.

They achieved a 50x breakthrough in longevity science using a FRACTION of the LLM parameters.

Results? New proteins that outperform previous benchmarks with faster cell reprogramming. Game-changing stuff.

Why SLMs are Revolutionary:
✅ Run on your laptop - no cloud dependency
✅ Fine-tune overnight - not months
✅ Privacy-first - your data never leaves
✅ Real-time responses - user experience transformed
✅ Enterprise-ready - passes compliance that LLMs can't

The Winning Architecture (Copy This):
- 60-70% of tasks: SLMs handle daily agent work
- 20-30% of tasks: LLMs for complex/creative work
- 100% control: Modular systems that scale without burning cash

This Changes EVERYTHING:
💰 ECONOMICS: SLMs are 10–30x cheaper to run & need less energy
🎯 CUSTOMIZATION: Train on YOUR data for your specific use case
⚡ SPEED: Agent workflows become almost real-time (finally!)
🔒 COMPLIANCE: Meets privacy/security requirements

While most companies burn cash building with LLMs, innovators like OpenAI are using specialized SLMs for their biggest breakthroughs.

THE BOTTOM LINE:
Stop waiting for bigger models. Start building SMARTER ones.
The future isn't about who can afford the most computing.
It's about who can architect the most intelligent systems.

Join our next Agentic Inner Circle: Beyond RPA - The New Coded Automation!
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What do you think? 

Check comments for the link to the Nvidia paper on SLMs.

P.S. The companies making this transition now will be writing the playbooks everyone else follows in 2026.

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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!

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

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𝗧𝗵𝗲 $𝟲𝟬𝟬𝗕 𝗔𝗜 𝗺𝗮𝗿𝗸𝗲𝘁 𝗵𝗮𝘀 𝟱 𝗱𝗶𝘀𝘁𝗶𝗻𝗰𝘁 𝘁𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝗜.

However, most people confuse ML, AI Agents & Agentic AI.

They're not the same.

Here's the AI Capability Spectrum that everyone needs to understand:

Remember: A-E-C-M-O (Analyst → Expert → Creative → Manager → Organization)

Type 1: Machine Learning (The Analyst)
→ Turns raw data into insights
→ Forecasts patterns, classifies outcomes
→ Use case: Predictive maintenance, demand forecasting

Type 2: Deep Learning (The Expert)
→ Models complex patterns at scale
→ Powers vision, NLP, and speech systems
→ Use case: Computer Vision, Quality inspection

Type 3: Generative AI (The Creative Assistant)
→ Creates content, code, and designs at scale
→ Summarizes, translates, and generates synthetic data
→ Use case: Report generation, code assistance, training data

Type 4: AI Agents (The Manager)
→ Automates multi-step workflows end-to-end
→ Uses tools, maintains context, adapts in real-time
→ Use case: IT ticket resolution, Create simple applications

Type 5: Agentic AI (The Organization)
→ Coordinates multiple autonomous agents
→ Self-corrects, ensures compliance and safety
→ Use case: Supply chain orchestration, complex automation

The truth? It's not about which AI is "better."

It's about matching the capability to your use case.

Ask yourself:

- Do you need insights or actions?
- Single task or multi-step workflow?
- Human oversight or full autonomy?

Your answer determines which AI you need.

Join our next Agentic Inner Circle: Beyond RPA - The New Coded Automation!
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What's your biggest challenge in choosing the right AI approach?

(Full visual guide in the image below 👇)

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𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲'𝘀 𝗿𝘂𝘀𝗵𝗶𝗻𝗴 𝘁𝗼 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗥𝗣𝗔 𝘄𝗶𝘁𝗵 𝗔𝗜.

They're making a costly mistake.

Here's what the AI vendors won't tell you:

That "boring" RPA you're abandoning? It's still your best ROI for 70% of processes.

I mapped every automation technology against the real need. 

The resulting quadrant is how I am mapping out the landscape now. 👇


The 2025 Automation Quadrant reveals:


🟢 BUILD HERE FIRST (Green Zone) 

→ High-volume + Low complexity 

→ RPA, DPA, Intelligent Automation 

→ Why? Proven. Reliable. Cost-effective. 

→ Don't burn AI tokens when you don't need to.


🟡 PILOT STRATEGICALLY (Yellow Zone) 

→ Copilots & RAG Chatbots where you have context. 

→ Computer Use tools (Claude, Operator) for one-off tasks

→ Agentic Process Automation (APA) for cognitive automation

→ Test, learn, scale what works.


🔴 PREPARE NOW (Red Zone) 

→ Agentic AI is in its early days, and the ecosystem is falling in place

→ Reasoning is still WIP. and so complex planning fails

→ Expensive failures waiting to happen if rushed

→ Prepare, don't rush.


How to use this quadrant?

1️⃣ Build confidently in the green zone

2️⃣ Pilot yellow zones (strategic expansion)

3️⃣ Prepare for red zone breakthroughs (but stay patient)


Many companies are getting it wrong. They're piloting flashy AI while their automation foundation crumbles.

What's your take? Are you building in the green zone first or chasing the bleeding edge?

(Quadrant attached - where does your org sit?)

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𝟵𝟱% 𝗼𝗳 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘀𝗶𝗻𝗸 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲𝘆 𝘀𝗵𝗶𝗽.

Why?

Nobody's building the foundation that makes them work.

Result? Most AI projects never escape the pilot phase.

After 10 years in automation, here's why I see AI projects fail:

It's never the technology.

It's the 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝘄𝗼𝗿𝗸 that organizations skip:

→ They automate chaos instead of fixing the broken processes first
→ They buy new platforms while their existing tools collect dust
→ They feed AI systems garbage data from multiple sources
→ They deploy without security, governance, or audit trails
→ They celebrate POC wins, then watch them die

Companies think AI is a sprint. 🏃‍♂️
Reality? It's a marathon with hurdles.

The boring work IS where the magic happens.

✅ Here’s the “boring” checklist that actually makes AI scale 

- Process discovery + mapping
- Data governance frameworks (ownership, definitions, lineage)
- Data sourcing/extraction + cleaning/normalization
- Security protocols + access controls
- Legal compliance + ethical guidelines/bias testing
- Model selection, evaluation, training, tuning
- Deployment pipelines + monitoring infrastructure
- Change management (training, adoption, comms)
- KPIs + cost tracking/optimization + incident response
- Documentation + knowledge transfer + cross-team collaboration

These aren't obstacles to AI success. 𝗧𝗵𝗲𝘆 𝗔𝗥𝗘 𝘀𝘂𝗰𝗰𝗲𝘀𝘀.

Two paths forward:
𝗣𝗮𝘁𝗵 𝟭: Chase shiny AI tools → Skip foundation → Join the organizations that fail
𝗣𝗮𝘁𝗵 𝟮: Build Tech Foundations → Establish governance → Move slower to scale 

Which path is your organization on?

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That feeling when you realize your 𝗺𝘂𝗹𝘁𝗶-𝗯𝗶𝗹𝗹𝗶𝗼𝗻$ 𝗥𝗣𝗔 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗰𝗮𝗻 𝗯𝗲 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝗱 by an AI agent writing code.

I just watched Claude Code generate a complete n8n workflow with triggers, branches, error handling, all of it.
No visual builder. No drag-and-drop. Just pure JSON configuration written by an AI agent.

And that's when it hit me:
𝗪𝗵𝘆 𝗮𝗿𝗲 𝘄𝗲 𝘀𝘁𝗶𝗹𝗹 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗳𝗿𝗼𝗻𝘁𝗲𝗻𝗱𝘀 𝘄𝗵𝗲𝗻 𝗔𝗜 𝗰𝗮𝗻 𝘄𝗿𝗶𝘁𝗲 𝘁𝗵𝗲 𝗯𝗮𝗰𝗸𝗲𝗻𝗱 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆?

It's like using a PC when you have a supercomputer.

Here's the architecture that's coming (and it's not that complex):
1. 𝗥𝗣𝗔 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿: Lets Claude Code "look up" Activity properties and required arguments on demand.
2. 𝗖𝗹𝗮𝘂𝗱𝗲 𝗥𝗣𝗔 𝗦𝗸𝗶𝗹𝗹𝘀: Teaches patterns for RPA node configs, expressions, validation, error handling, etc.
3. 𝗗𝗶𝗿𝗲𝗰𝘁 𝗫𝗔𝗠𝗟 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Claude Code outputs ready .xaml workflows that import directly into your RPA platform.

The result?
We move from manual visual workflows to agentic coded automation.
From that transition, you can convert all your RPA to pure code like Python. 

And the advantages are staggering:
→ 𝗗𝗶𝗿𝗲𝗰𝘁 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻: No UI layer needed. Agents write code, configs, API integrations. The workflow IS the output.
→ 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗜𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝗲: Convert visual workflows to pure code. Own your automation. No vendor lock-in. No license games.
→ 𝟭𝟬𝘅 𝗙𝗮𝘀𝘁𝗲𝗿 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: What took days now takes hours. Google teams recreated year-long systems in days with coding agents.
→ 𝗦𝗲𝗹𝗳-𝗛𝗲𝗮𝗹𝗶𝗻𝗴 𝗯𝘆 𝗗𝗲𝗳𝗮𝘂𝗹𝘁: Agents detect failures, search documentation via MCP, patch code, redeploy. Autonomous maintenance.
→ 𝗕𝘂𝗶𝗹𝘁-𝗜𝗻 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲: Auto-generated tests, documentation, version control. The system improves itself over time.

𝗕𝘂𝘁 𝗵𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 𝗰𝗵𝗲𝗰𝗸:
Traditional RPA isn't dead. It's still the last-mile solution for legacy systems that'll never see a modern API.
But if your automation can be reduced to:
* Code
* APIs
* File operations
A coding agent beats manual workflows every single time.

𝗧𝗵𝗲 𝗽𝗮𝗿𝗮𝗱𝗶𝗴𝗺 𝗶𝘀 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴:
❌ Rigid rules → ✅ Flexible reasoning 
❌ Visual builders → ✅ Executable backends
❌ Brittle automation → ✅ Adaptive learning systems

Smart engineers don't use low-code platforms to solve hard problems. They write code.
Now, AI agents let you do the same at enterprise scale.

𝗪𝗼𝘂𝗹𝗱 𝘆𝗼𝘂 𝗹𝗶𝗸𝗲 𝘁𝗼 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘁𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗥𝗣𝗔 𝗶𝗻𝘁𝗼 𝗰𝗼𝗱𝗲𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻? DM me or message me at nandanmullakara .com

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𝗔𝗳𝘁𝗲𝗿 𝘆𝗲𝗮𝗿𝘀 𝗼𝗻 𝗯𝗼𝘁𝗵 𝘀𝗶𝗱𝗲𝘀 𝗼𝗳 𝘁𝗵𝗲 𝘀𝗮𝗹𝗲𝘀-𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗱𝗶𝘃𝗶𝗱𝗲, 𝗜'𝘃𝗲 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝘂𝗻𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲.

As an engineering manager, "Not possible" was our usual response.
As a pre-sales leader, I watched brilliant solutions die because of that.

Here's what I learned from being part of pre-sales:
→ Every "no" from engineering kills a potential partnership
→ Sales isn't about lying - it's about exploring the art of possible
→ Clients buy the vision, then they usually work with you to build it

The magic happens when engineering and sales collaborate instead of competing.

✅ The best companies find the middle ground
✅ Sales needs to understand technical constraints
✅ Engineering needs to embrace the art of the possible

Bill Gates proved this decades ago - sell the vision, build the solution.

Now I help bridge both worlds. It's challenging but necessary.

What's your experience? Have you seen this dynamic play out?

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𝗧𝗵𝗲𝗿𝗲 𝘄𝗮𝘀 𝗮 𝗿𝗲𝗮𝘀𝗼𝗻 𝗥𝗣𝗔 𝗵𝗮𝗿𝗱𝗹𝘆 𝘀𝗰𝗮𝗹𝗲𝗱 - 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗶𝘁𝘀𝗲𝗹𝗳 𝘄𝗮𝘀 𝗻𝗼𝘁 𝗮𝗺𝗲𝗻𝗮𝗯𝗹𝗲.

Here's the uncomfortable truth about RPA automation that nobody talks about:

❌ "Autonomous operations" remained a fantasy
❌ A change in a UI field broke the entire workflow
❌ "Citizen developers" became "citizen firefighters"
❌ ROI projections ignored the high maintenance costs
❌ Enterprise-wide automation remained a PowerPoint fantasy

Agentic AI-led automation will change all that.

Here's why this time is different:

🧠 REASONING over rule-following
🔄 ADAPTATION over rigid programming
🎯 AUTONOMY over constant monitoring
🤝 COLLABORATION over isolated task execution

The difference?
RPA mimics human actions.
Agentic AI understands human intent.

One requires constant babysitting.
The other grows smarter over time.

We're not just upgrading our bots.
We're fundamentally changing what automation means.

The companies that realize this first will dominate the next decade.
What's your take? Are you building for the Agentic future?

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𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 𝗷𝘂𝘀𝘁 𝗱𝗲𝘀𝘁𝗿𝗼𝘆𝗲𝗱 𝘁𝗵𝗲 𝗥𝗣𝗔 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝗶𝗻 𝟵 𝗺𝗶𝗹𝗹𝗶𝘀𝗲𝗰𝗼𝗻𝗱𝘀.
Last year, I was amazed when AI completed this same challenge in 14ms using "computer use."

But here's what shocked me now...
The WAY Claude code worked was completely different.

Last year's approach w/ "Computer Use":
→ Perfect prompt = working solution 
→ Hit or miss (mostly miss)
→ Lots of prompt finagling
→ 14ms completion time

Claude Code's approach:
→ Zero prompting required
→ I just gave it the RPA Challenge URL
→ It made a plan and asked for approval
→ Tested, iterated, improved - all by itself
→ Final time after a few tries and some help: 9ms

It felt like working with an intelligent junior programmer, not a prompt-dependent tool.

Here's how it went: 
- First attempt: 6 seconds (disappointing).
- After iterations:  200ms (still not there).
- Gave it the "Computer Use" script: went to 11ms.
- Two more attempts later: 9ms. ✅

Short Video: https://lnkd.in/gB3-GAPy

I've now built complete Next.js apps, monitoring tools, and full applications with Claude Code.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻:

The same way developers are building apps with AI agents, we can now build coded automations.

You can also build low-code applications in Claude code. E.g. Make N8N automations in Claude code and import.  

Just describe your intent → Review the plan → Work with AI to complete it.

With MCP access (more powerful than just APIs), Claude Code can handle most web automations through tools like Playwright.

The reality check:

- Legacy systems (mainframes, SAP)? Still need RPA/low-code
- API & Web automations? Move to coded automation with guardrails
- Traditional RPA? Will move towards "legacy automation" (still substantial)

𝗦𝗼, 𝗰𝗮𝗻 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘂𝘀𝗲𝗿𝘀 𝗷𝘂𝘀𝘁 𝘁𝗲𝗹𝗹 𝗔𝗜 𝘁𝗼 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝘁𝗵𝗶𝗻𝗴𝘀?

No (at least now). Without proper knowledge, you get AI slop, and that's dangerous.

We still need dedicated developers who understand:
→ Proper specifications
→ Architecture patterns
→ Security guardrails
→ Best practices

𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: If you're in automation, start exploring Claude Code NOW.
Join our next Agentic Inner Circle: Beyond RPA - The New Coded Automation!
https://lnkd.in/g_WTssYx

Learn to build with guardrails. Master the specifications. Position yourself for the coded automation era.

The tools will improve. Security will strengthen. But early adopters will lead.

𝗔𝗿𝗲 𝘆𝗼𝘂 𝗿𝗲𝗮𝗱𝘆 𝗳𝗼𝗿 AI 𝗰𝗼𝗱𝗲𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻?

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𝗢𝗻𝗹𝘆 𝟭𝟯% 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹𝗹𝘆 𝘀𝗰𝗮𝗹𝗲 𝗔𝗜 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻.

I've watched this pattern for over a decade:

→ Company gets excited about "AI transformation"
→ Deploys RPA bots on broken processes
→ Pilot shows good “promise"
→ Scale fails spectacularly
→ Blame the technology

Here's what nobody wants to admit:

You cannot build a skyscraper on quicksand.
Automation applied to an inefficient process will magnify the inefficiency.

But everyone's trying to skip straight to Stage 4.

The companies reaching 10x results?

They follow a completely different playbook: 
THE AI AUTOMATION FUNNEL (The tried & tested framework)

🔴 Stage 1: ELIMINATE
Stop the bleeding first.
Cut every redundant, non-value task.
Goal: Clear the path.

🟠 Stage 2: STANDARDIZE  
Turn tribal knowledge into repeatable SOPs.
Document and train your people.
Goal: Build the foundation.

🟡 Stage 3: OPTIMIZE
Apply Lean methodologies.
Remove bottlenecks before automating.
Goal: Maximize efficiency.

🟢 Stage 4: AUTOMATE
NOW deploy your RPA and AI agents.
Let technology handle the robot work.
Goal: Scale without headcount.

🔵 Stage 5: INNOVATE
Turn AI into a competitive advantage.
Create new revenue streams.
Goal: Market domination.

I've seen Fortune 500 companies fail at Stage 4 because they skipped Stages 1-3.

The companies in that top 5-10%?

They don't skip stages.
They don't chase shiny objects.
They don't mistake pilots for progress.

Your competitors are probably stuck at Pilots pretending they're at Stage 4.

That's your opportunity.

Stop chasing the latest AI models or tools.
Start building the foundation that makes AI actually work.

The next decade belongs to companies that execute.
Not companies with the shiny tools.

What stage are you really at?

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𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀 𝗮𝗿𝗲 𝗚𝗼𝗼𝗴𝗹𝗶𝗻𝗴 "𝗵𝗼𝘄 𝘁𝗼 𝗱𝗲𝗹𝗲𝘁𝗲 𝗖𝗼𝗽𝗶𝗹𝗼𝘁" 𝗮𝘁 𝗿𝗲𝗰𝗼𝗿𝗱 𝗿𝗮𝘁𝗲𝘀.

And honestly? I don't blame them.

Because we are making the same mistake with AI that we made with RPA in 2016.

We're prioritizing:
❌ Licenses over learning
❌ Rollout speed over readiness
❌ Technology over transformation

I've seen this movie before.
Same script. Different buzzword.

The companies still using their RPA successfully 8 years later?
They didn't win because of the technology.
They won because they drove adoption.  

Here is the 5Es FRAMEWORK that I like & use:

→ 𝗘𝗱𝘂𝗰𝗮𝘁𝗲 with real use cases (not flashy demos)
→ 𝗘𝗺𝗽𝗼𝘄𝗲𝗿 through structured enablement
→ 𝗘𝗻𝗮𝗯𝗹𝗲 safe experimentation spaces
→ 𝗘𝗻𝗴𝗮𝗴𝗲 concerns openly & early
→ 𝗘𝘅𝗲𝗰𝘂𝘁𝗲 with clear KPIs/metrics

The 3-step playbook that I recommend:

1. START WITH WHY
↳ Connect to business AND personal outcomes
↳ Show Organizational and personal benefits
↳ Show the path to becoming irreplaceable

2. ENABLE THE HOW
↳ Hands-on workshops > PowerPoint training
↳ Real problems > Hypothetical scenarios
↳ Peer champions > Executive mandates

3. SUPPORT THE WHAT
↳ Detailed roadmaps with quick wins
↳ Feedback loops that really close
↳ Celebrate adopters publicly

The data doesn't lie:
Companies with structured adoption programs see 4-6x higher utilization rates.
Your expensive Copilot or any AI licenses aren't the real asset.

Your people adopting what works for them -that's the asset.

What's working (or not working) in your AI rollouts?

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The world changed last week.
You just didn't notice.

A Google Principal Engineer casually mentioned that Claude Code recreated a YEAR-LONG orchestration system in one hour.

Let that sink in. From months → 1 hour.

The gap between "idea" and "working software" just collapsed.

We're not talking about code snippets or boilerplate.
We're talking about entire codebases w/ multiple agents running in parallel.

The development paradigm has shifted beneath our feet.
And most developers are still operating like it's 2023.

The new development paradigm calls for new skills:

1. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗙𝗿𝗮𝗺𝗶𝗻𝗴 — Defining intent and constraints with precision
2. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 — Designing durable AI guidance systems
3. 𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝗹𝗲𝗴𝗮𝘁𝗶𝗼𝗻 — Coordinating parallel AI workflows
4. 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 — Evaluating output for risk and quality
5. 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝘀𝗶𝗴𝗻 — Building quality gates at scale
6. 𝗧𝗼𝗼𝗹𝗰𝗵𝗮𝗶𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 — Creating AI-operable systems
7. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 — Shaping human-AI collaboration

With Claude Opus, Claude Code, and the new generation of AI coding tools, this isn't coming.

It's here. 

The question isn't whether to adapt.

It's whether you'll be the irreplaceable w/ AI coding skills or ready to drown.

Which side of that line are you on?

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Hot or Not? 

Some people prefer the GPUs and CPUs right now. 

How about you? 

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𝗧𝗵𝗲 𝗖𝗶𝗿𝗰𝗹𝗲 𝗼𝗳 𝗟𝗶𝗳𝗲 𝘄𝗶𝘁𝗵 𝗔𝗜: 𝗗𝗲𝘀𝗶𝗴𝗻𝗲𝗿𝘀 𝗮𝗿𝗲 𝗿𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗮𝗿𝗲 𝗿𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗿𝘀.

Wait... Who replaces whom?

Having led digital automation initiatives, I can tell you what AI actually replaces:
The gap between idea and execution.

Let me show you what this looks like in practice w/ an AI Automation example:

I've seen organizations let business teams build RPA bots in low-code. (Citizen development)
The code wasn't perfect. The build surely wasn't enterprise-grade. 

But you know what? IT understood what the business needed.

IT then built robust solutions on top of the “rough” bots. 
Result: faster deployment, happier stakeholders

This is what I call "𝗧𝗵𝗲 𝗛𝗮𝗻𝗱𝗼𝗳𝗳 𝗧𝗮𝘅" - and AI is eliminating it at scale.

𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 (Months): Business → Requirements Doc → Designer → Mockups → Developer → Code → QA → Back to Business 
(Context lost at every step with handovers)

𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 (Weeks): Business/Designer builds working prototype → Developer refines → QA → Production 
(Preserved context, better alignment)

So, we're not headed toward: 
❌ Designers replacing developers 
❌ Engineers replacing designers 
❌ AI replacing humans

We're headed toward something different: 
✅ Designers who can validate their ideas in code 
✅ Engineers who can fine-tune the designs 
✅ Teams that ship faster w/ less handoffs 

AI isn't about which roles will get eliminated.
It's about who's training their teams to expand beyond their silos.

But here's the dangerous part: AI slop.

When everyone can generate code/designs without understanding:
→ We get 10x more output with 1/10th the quality
→ Technical debt compounds faster than ever
→ Junior talent never learns foundational skills

The difference between AI augmentation and AI slop?

𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴. 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀. 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀.

When teams expand their boundaries with proper foundations, magic happens:
- Faster iteration & build cycles
- Fewer miscommunications
- Better end products

The real AI transformation isn't about fewer people. 
It's about fewer silos.

What do you think? Where are you seeing the “handoff tax” slow down your teams?

Image: programmerhumor .io

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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? 

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Claude Code just hit $1B ARR. Faster than ChatGPT.

But everyone's is missing this HUGE milestone & shift.
Claude Code isn't just another AI tool going viral.
It's a glimpse into what I'm calling the "AI OS."

And it changes work for anyone who uses a computer.

𝗛𝗘𝗥𝗘'𝗦 𝗧𝗢𝗗𝗔𝗬'𝗦 𝗘𝗡𝗧𝗘𝗥𝗣𝗥𝗜𝗦𝗘 𝗥𝗘𝗔𝗟𝗜𝗧𝗬:
→ Too many apps (200+ on average) 
→ Too many handoffs 
→ Too much swivel chair automation 
→ RPA and integrations filling gaps that shouldn't exist

We called this digital transformation.
It was really just managed chaos.

That era is ending.

𝗧𝗛𝗘 𝗔𝗜 𝗢𝗦 𝗠𝗢𝗗𝗘𝗟 𝗙𝗟𝗜𝗣𝗦 𝗘𝗩𝗘𝗥𝗬𝗧𝗛𝗜𝗡𝗚:

Instead of buying apps and automations from multiple vendors and managing all that complexity...

The AI OS lets you just describe what you want. No prompting required.

Instead of: Login to SAP → Extract data → Build report → Upload to SharePoint → Email stakeholders
You get: "Create Q4 financial summary from SAP and send to exec team."

Instead of: Check Salesforce → Pull customer data → Draft proposal → Update CRM → Schedule follow-up
You get: "Build proposal for ACME Corp using past wins and update Salesforce."

Instead of: Access ServiceNow → Review ticket → Check knowledge base → Update incident → Notify requestor
You get: "Resolve ticket #4521 using our standard protocols and notify the use.r"

Here's what's happening behind the scenes:
The AI traverses your ERP, CRM, HCM, and all backend systems.
It understands your intent. Plans the execution path. Orchestrates across systems. Validates the outcome.

Your enterprise apps move to the backend.
𝗧𝗵𝗲 𝗔𝗜 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘁𝗵𝗲 𝗳𝗿𝗼𝗻𝘁𝗲𝗻𝗱 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿.

But let's be honest.
This is NOT (yet) a plug-and-play “bot nirvana”.

Three hard truths:
🔸 Early Stage: AI OS with the Terminal interfaces like Claude code aren't user-friendly yet.
🔸 Token Economics: Heavy usage burns through limits. Pro plans help, but enterprise users will need strategies.
🔸 Guardrails Required: Claude Code can delete, overwrite, and make costly mistakes. Always review plans. Start with minimal permissions. Backup 

This is why 𝗜𝗧 𝗮𝗻𝗱 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗲𝘃𝗲𝗿.

Because the productivity divide ahead isn't linear.
𝗜𝘁'𝘀 𝟭𝟬𝗫 𝗳𝗼𝗿 𝗲𝗮𝗿𝗹𝘆 𝗮𝗱𝗼𝗽𝘁𝗲𝗿𝘀 𝘄𝗶𝘁𝗵 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀.
𝗜𝘁'𝘀 𝟭𝟬𝟬𝗫 𝗱𝗲𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝘃𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲.

Agentic AI is entering enterprises whether you're ready or not.

Join our next Agentic Inner Circle: Beyond RPA - The New Coded Automation!
https://lnkd.in/g695QhxX

Will your automation strategy evolve with Agentic AI? 

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𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗥𝗲𝗮𝘀𝗼𝗻 𝗪𝗵𝘆 𝟵𝟱% 𝗼𝗳 𝗚𝗲𝗻 𝗔𝗜 𝗣𝗶𝗹𝗼𝘁𝘀 𝗙𝗮𝗶𝗹 😀

Everyone talks about it. 

Nobody really knows how to do it. 

Everyone thinks everyone else is doing it... 

So everyone does pilots to claim they are doing it 😊

Stop building Agentic AI pilots. Instead, add controlled agents with APA. 

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𝟯𝟬% 𝗯𝗲𝘁𝘁𝗲𝗿 𝘄𝗶𝗻 𝗿𝗮𝘁𝗲𝘀.
That's what early AI adopters in sales are seeing.

But here's what nobody's talking about:
Most sales AI projects fail before they start.

Why? Because of challenges unique to sales:

→ Sales data lives in 10+ disconnected systems 
→ Seller days are fragmented across dozens of tasks 
→ Teams are skeptical (they've seen tools come and go) 

But the AI automation opportunity is massive across the sales life cycle: 

𝗦𝗧𝗔𝗚𝗘 𝟭: 𝗟𝗲𝗮𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗣𝗿𝗼𝘀𝗽𝗲𝗰𝘁𝗶𝗻𝗴 
AI contact enrichment • Automated research • Predictive scoring • AI BDR outreach

𝗦𝗧𝗔𝗚𝗘 𝟮: 𝗛𝗶𝗴𝗵-𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆 𝗦𝗲𝗹𝗹𝗶𝗻𝗴 
Seller copilots • Intent-based routing • Self-service • Agent optimization

𝗦𝗧𝗔𝗚𝗘 𝟯: 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 & 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 
Real-time coaching • Next-best-action • Call summarization 

𝗦𝗧𝗔𝗚𝗘 𝟰: 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝗻𝗴 
Automated proposals • CRM cleanup • AI configuration • Smart routing

𝗦𝗧𝗔𝗚𝗘 𝟱: 𝗖𝗹𝗼𝘀𝗶𝗻𝗴 
Pipeline intelligence • Forecasting • Manager assistant • Territory optimization

𝗦𝗧𝗔𝗚𝗘 𝟲: 𝗣𝗼𝘀𝘁-𝗦𝗮𝗹𝗲𝘀 
Digital sales rooms • Adaptive learning • Partner enablement

20+ use cases. Some critical insights into the AI playbook.

The companies seeing 30%+ improvements are doing 5 things differently:

1️⃣ Map your end-to-end selling journey 
2️⃣ Identify high-impact, high-feasibility use cases 
3️⃣ Design new processes (not automate old ones) 
4️⃣ Solve for data quality and context 
5️⃣ Win hearts and minds of sellers

The companies capturing value from sales AI aren't starting with technology.
They're starting with strategy.

Where are you in your sales AI journey?

Stats & Use case source: BCG

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗻𝗼𝗯𝗼𝗱𝘆 𝘁𝗲𝗹𝗹𝘀 𝘆𝗼𝘂 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜 𝗥𝗢𝗜:

AI's ROI problem is not about enough intelligence.
It’s the gap between intelligence and action.

I've watched this play out for years:
ML/Gen AI models → Great insights → Beautiful dashboards → Zero execution

The ROI died in that final gap.
Between recommendation and action. 
Between insight and impact.

So we 𝗯𝘂𝗶𝗹𝘁 𝗮 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝘁𝗼 𝗺𝗮𝗽 𝘁𝗵𝗶𝘀.
The AI Impact Grid helps you understand the key AI capabilities now.
And, more importantly, gives you a roadmap to move them toward real impact.

Two dimensions define the landscape: 
→ Authority (Advisory to Empowered) 
→ Action (Passive to Active)

Here are the 𝗳𝗼𝘂𝗿 𝘁𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝗜 in this landscape:
- 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰 𝗔𝗜: Predictive & prescriptive models that tell you what's happening and what could happen. 
- 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜: Creates code, content, images, plans - powerful outputs. 
- 𝗔𝘀𝘀𝗶𝘀𝘁𝗶𝘃𝗲 𝗔𝗜: Copilots and RAG chatbots that actively engage with you. 
- 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜: Active execution WITH empowered authority to act & where ROI actually lives.

Here's 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗮𝗿𝗿𝗼𝘄𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗴𝗿𝗶𝗱 𝗿𝗲𝘃𝗲𝗮𝗹:
There are multiple paths to getting to active and authoritative autonomous AI.
Not just one way. Multiple journeys based on where you are and what you've built.

𝗧𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 𝗰𝗵𝗲𝗰𝗸:
Understandably, people don't want just throw powerful AI agents into their organizations.
You can't and shouldn't jump straight to autonomous systems without building the right foundations and confidence.

𝗧𝗵𝗲 𝗽𝗮𝘁𝗵𝘀 𝗜'𝗺 𝘀𝗲𝗲𝗶𝗻𝗴 𝘄𝗼𝗿𝗸:

𝗧𝗵𝗲 𝗔𝘀𝘀𝗶𝘀𝘁𝗶𝘃𝗲 𝗥𝗼𝘂𝘁𝗲: Organizations building "ChatGPT for their company" - essentially RAG chatbots. They start advisory and active, then gradually increase authority as trust builds. → From answering questions to taking actions.

𝗧𝗵𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Teams who've done predictive and prescriptive ML for years. They have the data foundations and governance already. Now they're layering in Gen AI & moving toward agentic execution.  → From insights to intelligent action.

𝗧𝗵𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Developers using generative AI to create great code. That led to coding agents like Claude code. Now they're stepping into autonomous agentic systems. → From creation to automation.

Have seen these paths work, and there are more. 

𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗱𝗼𝗲𝘀:

The AI Impact Grid helps you: 
→ Map where your current AI initiatives sit 
→ Identify which path suits your situation best 
→ Understand what foundations you need for each move 
→ Sequence your journey strategically w/ right foundations

You don't have to force-fit someone else's roadmap.

The path you take should match where you are. 
The grid gives you the map. You choose the route.

What paths are you seeing?

Would love to hear your perspective. 

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