Dr. Manish Mehta

AI Enterprise Vision

Over 20 years experience guiding successful large-scale deployments of AI technologies including autonomous systems, robots, and virtual agents

Areas of expertise:

- Autonomous systems - Led technology strategy for commercial mower company
- Intelligent agents - Pioneered virtual agents transforming customer service at multinational
  corporations
- Robotics - Oversaw robot deployments in manufacturing facilities
- Voice assistants - Built AI-powered assistants for construction workers
- Healthcare robots - Advised Tech startup developing robotic companions to assist seniors with medication
- AI Strategy and Cultural transformation

I offer organizations:

- Identifying clear AI objectives aligned to business goals
- Assembling cross-functional teams combining AI, business, and domain expertise
- Guiding pragmatic AI implementations focused on improving efficiency, safety, and customer
   experience
- Advising on ethical, responsible AI adoption strategies
- Leveraging experience deploying AI solutions across industries from healthcare to manufacturing

Let's connect to make your organization's AI vision an operational reality!

Chief AI Strategist: Driving Pragmatic AI Implementations

Creator AI Studio

With over 20 years as an AI innovation pioneer, I create imaginative AI experiences by taking early stage technologies from concept to implementation.

My expertise includes:

a. Conversational AI - Created interactive museum exhibits and theme park characters
b. Robotics - Developed robotic assistants and experiences like Turtle Talk AI system
c. Intuitive Creation Tools - Enabled non-programmers to build AI behaviors through easy interfaces
d. Immersive Experience Design - Architected AI gaming blending conversation and immersion

I offer:

- Innovation Strategy: Guided projects from research to requirements to design like Pirates of the
  Caribbean ride reimagining
- Team Assembly: Brought together cross-functional talent to develop experiences like robot 
   companions for seniors
- User-Focused Development: Conducted ongoing testing and feedback with users during creation of 
   museum exhibits
- Entrepreneurial Leadership: Scaled innovations from prototype to full deployment such as the AI 
   gaming experience

My passion is pioneering experiences that fascinate and inspire audiences by leveraging AI and emerging tech.

If you seek a leader who thrives on the frontier turning early stage technologies into imaginative implementations, let's make the extraordinary happen together!

Chief AI Creator: Transforming Emerging Tech into Engaging AI Experiences

Stanley Black & Decker, Inc.

Employee Number 1 at the CTO office and Help Build the company’s first-ever CTO Office. Help Lead Innovation Ecosystem to $1.2B/yr revenue. 3 Years FastCo and AII “Most Innovative” Company

- Developing multi-year research, technology and commercialization strategies across emerging areas 
   like AI, IoT, Industry 4.0, robotics and autonomous systems.

- Building partnerships between business units (Security, Healthcare, Industrial, Consumer) and functions
   (Sales, Marketing, IT, R&D).

- Managing cross-functional teams of 50-150 people on strategic projects.

- Conducting M&A technical due diligence, competitive intelligence, quantitative and qualitative studies.

- Establishing an external ecosystem network with VC firms, startups, and commercial companies across 
  the Bay Area and beyond, leading to successful partnerships and investments with 15+ organizations
  including Plug n Play, Baidu Ventures, Sapphire Ventures, and more.
  
- Driving well-received technology strategy initiatives on AI, ML, IoT, and AI conversational tech for the
  CEO.

Technical Director of Innovation, Office of the CTO

AI+ Accelerator TechCode - US

AI+ accelerator at Techcode consists of startups that are focused on the application of artificial intelligence technologies: voice recognition, machine learning, natural language processing, computer vision and gesture control to all the different industry verticals, e.g. robotics, virtual reality/augmented reality, smart home, IoT, medical device and wearables.

Startup Mentor

Accenture Technology Labs

- Supported development of visionary "liquid workforce" transformation in 2012, enabling Accenture
   crowdsourcing initiative. Platform catalyzed 15K+ hours and mobilized 100K people.

- Liquid workforce became a smash hit theme of Accenture Vision 2016, igniting 14K+ social media 
   mentions, 230+ client downloads, 112M reach. Amplified by 638 influencers like IBM and futurist Gerd
   Leonhard.

- Led research, strategy, and UX design to build Accenture's crowdsourcing capability across business areas. 

- Defined go-to-market strategy. Reported progress to leadership.

- Led patented design of new crowdsourcing offering curating on-demand teams. Transformed CIO recruiting
   and teams.

- Guided pilots analyzing coordination challenges with internal and external crowd workers. Led prototype
  development.

- Advised crowdtesting vendor pilots (Applause, Passbrain). Guided analysis for product insights. Managed 
   tools for new crowdtesting capability. Led pilots at top clients.

- Shaped design and guided development of a platform to enrich unstructured data via paid/free crowd work  
   and automation. Led vendor pilots.

- Drove multiple patented ideas advancing intellectual property. Filed inventions.

- Forged partnerships with vendors, R&D, and academia.

- Spearheading innovation blitz for Industry X.0 across 5 labs, reimagining products with emerging tech like AI
  and robotics.

- Pioneering human-robot innovation teams to conceptualize, design, manufacture and service products. 

-  Created behavior design strategies to motivate user participation for Fortune 100 clients. Recognized by  
   client with top award.

The Human-Centered AI Innovator - Unlocking Human Needs through UX and AI

American Cancer Society

Providing advice, leadership and direction to American Cancer Society to further the Society’s progress by leveraging the skills and talents of volunteers to add value to and complete strategic initiatives and projects.  The role involves advising the society to increase its appeal and competitive advantage in the volunteer marketplace, save the Society time and money on project initiatives, and offer opportunities to further develop staff and volunteers.

The goal of the program is to redefine volunteer opportunities to include Skills-Based Volunteering (SBV) to increase the society's market relevance and competitive advantage. The SBV program supports both SBV and episodic volunteers and meets the outcomes of the Board accepted recommendation. The purpose of the Society’s Skills-Based Volunteer(SBV) Advisory Team is to provide advice, consultation, leadership and direction to the Society’s strategy on Skills-Based Volunteerism.  The team is charged with developing a long term, enterprise-wide rollout for Skills-Based Volunteers.

Strategic Advisor

Georgia Institute of Technology

- Pioneered trailblazing augmented reality app - deployed and delighted hundreds of diverse users including actors, virtual world members, and non-programmers.

- Architected award-winning UX design thrilling 150+ visitors with innovative augmented reality gaming experience at UCI Beall Center. Transported players into resolving virtual character conflicts.

- Spearheaded software architecture and pioneering Wizard UIs controlling one-of-a-kind experience. Conducted rigorous qualitative and quantitative evaluation.

- Pioneered first Web 2.0 app empowering non-programmers to create, modify and share game character AI behaviors. Developed advanced AI dynamically enhancing user-generated AI.

- Deployed live and piloted by hundreds of user groups: actors from School of Theatre at Illinois State University, non-programmers from virtual world Second Life and Non-Programmers.

- Led extensive user research and crafted detailed personas. Spearheaded iterative prototyping of breakthrough end-user authoring tool and AI approach.

- Rigorously and holistically evaluated pioneering experience through qualitative and quantitative UX research methods. Dove deep into extensive data gathering and analysis.

Next-Gen AI Experience Designer

Disney Research

Transforming Turtle Talk Character Experiences

- Pioneering innovative system enabling multiple operators to engage AI characters, slashing costs.
- Uncovering guest needs and motivations through insightful operator interviews and observations at Disney Land.
- Rigorously evaluating developed system through extensive usability studies with operators and guests.

Reimagining Pirates of the Caribbean Guest Experience

- Energizing Pirates experience through vigorous ideation and prototypes.
- Accelerating cutting-edge Pirates research, assembling university and industry teams.

Theme Park Experience Innovator - Infusing UX with AI

University of Southern Denmark

- Pioneered Museum Exhibit Advancing State-of-the-Art in Conversational AI - Enabled first-of-its-kind 
speech interaction between children and AI-controlled Hans Christian Andersen.

- Led insights-driven Wizard of Oz studies, observations, and interviews to deeply understand young participants. Crafted detailed user personas and storyboards bringing exhibit vision to life.

- Spearheaded iterative wireframing, mockups, and development of breathtaking final exhibit. Published top-tier peer-reviewed papers.

- Conducted rigorous qualitative and quantitative evaluation uncovering actionable insights to shape future roadmap. Translated findings into pioneering recommendations.

- Developed cutting-edge advances in speech recognition, NLU, dialog management, and gestural coordination—unlocking seamless speech-based interaction.

- Exhibit captivated audiences and received extensive press for pushing scientific and engineering boundaries. Addressed key challenges to inspire future innovation

Pioneer of First-of-its-Kind AI Experiences

Steria

Software programmar

Reimagined Disney Attractions with AI | Pioneered Conversational AI Museum Exhibits | Self-Driving Car Degree | Corporate AI & Robotics Deployment & Strategies | Speaking Human, AI & Business over two decades

I speak human, business & AI and build AI solutions that nail all 3; learned and practiced over 20 years at the school of hard-knocks pioneering first-of-its-kind AI experiences including:

- Next-gen AI powered system for Turtle Talk with Crush
- Reimagining Pirates of the Caribbean ride with AI based storytelling
- AI powered speaker for construction workers
- Cobots and mobile robots deployed at manufacturing plants
- Medication compliance robot companion at senior living facilities
- Conversational AI powered museum exhibit at Hans Christian Andersen Museum
- AI powered immersive conversation gaming experience at UCI Beall Center Musuem
- Self-driving car development at Udacity
- Autonomous mower technology strategy
- Conversational agents for customer service 
- AI strategy and cultural transformation at large multinationals
- AI powered technology enabling non-programmers to create, modify and share game character AI 
  behaviors


Industries Covered: Manufacturing, Security, Healthcare, Entertainment, Creator Economy

Technologies Covered: Self-Driving Cars, Robotics, Human-Centered AI, Conversational AI, Crowdsourcing, Industry4.0

Work presented at White House and featured in USA Today, MSNBC, Washington Post, Computer Technology, InformationWeek, Bloomberg, Venture Beat and Business Wire

Partnering with me, you gain:

•      A human-centered designer - translating user insights into exceptional experiences
•	A business strategist - providing diligence to steer initiatives, quantify ROI, and minimize risks
•	An AI/ML expert - architecting, deploying and optimizing systems from prototype to production
•	20+ years experience spearheading innovations from concept to commercialization
•	Investments analysis quantifying technical feasibility and commercial viability
•	A technology pioneer - adopting emerging capabilities like robotics and autonomy
•	Cross-functional leadership uniting diverse teams to deliver complex initiatives
•	Trend tracking identifying high-potential focus areas to capitalize on

I would enjoy grabbing a virtual coffee to explore how we can work together to bring AI innovations to life - the future is bright! Let's connect.

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

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🎯 Follow for Agentic

🎯 𝗧𝗵𝗲 𝗚𝗲𝗻 𝗔𝗜 𝗽𝗮𝗿𝘁𝘆 𝗶𝘀 𝗼𝘃𝗲𝗿. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗔𝗜 𝘄𝗼𝗿𝗸 𝗶𝘀 𝗷𝘂𝘀𝘁 𝗯𝗲𝗴𝗶𝗻𝗻𝗶𝗻𝗴.

The new Gartner Hype Cycle reveals what we suspected: 
Gen AI is sliding into the trough of disillusionment.

But here's what's rising to the top:
- AI-Ready Data
- AI TRISM
- Sovereign AI
- ModelOps
- AI Agents

Translation: Customers now want "AI-ready infrastructure," “AI orchestration,” and “AI agents,” not just another chatbot.

Drawing on this, here are 5 pillars every AI-mature enterprise must master:
1. AI Agents: Autonomous, goal-oriented systems that learn and adapt
2. AI-Ready Data: Governed, cataloged, real-time streams that fuel decisions
3. AI Orchestration: ModelOps, workflow automation, and intelligent resource allocation
4. AI TRISM: AI Trust, Risk, and Security Management - focus on reliability and accountability
5. Sovereign AI: Localized models that meet regulatory, data residency, and privacy requirements

Why this shift matters:
→ Infrastructure outlasts hype
→ Orchestrated agents > isolated AI apps
→ Governance beats speed-to-market always

What this means for your AI strategy:
Instead of asking "How do we use our internal ChatGPT or CoPilots?"
Start asking "How do we orchestrate intelligent agents?"

What do you think? Are you ready to ditch the Gen AI craze and architect dependable AI value? 

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

Repost if this helped you see the shift ♻️

𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐢𝐬 𝐠𝐨𝐢𝐧𝐠 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐬𝐡𝐢𝐟𝐭 𝐢𝐧 𝟔𝟎 𝐲𝐞𝐚𝐫𝐬.

For decades, we optimized how people wrote code.
Now we are moving to agents building & optimizing systems autonomously.

The old software playbook looked the same:

- Engineers write every line manually
- Weeks-to-months development cycles
- Cross-team bottlenecks kill momentum
- Most good ideas never get built (too expensive)

AI agents are quietly rewriting that playbook:

- Engineers orchestrate AI agents
- Hours-to-days shipping cycles
- Parallel multi-agent execution
- Previously unviable projects become possible

The software lifecycle is shifting from human execution to 𝐚𝐠𝐞𝐧𝐭 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧.
That changes everything.

Master these 7 skills to stay above the change:
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 and risk evaluation
𝟱. Verification Design - Automated quality gates
𝟲. Toolchain Architecture - AI-operable infrastructure
𝟳. Intelligence Orchestration - Human-AI symbiosis

Your uncomfortable choice:

Keep operating like before, while others ship 97% faster.
Or add these skills before the gap becomes permanent.

The gap is widening every week.

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🎯 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? 

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🎯 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?

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

Repost if this helped you see the shift ♻️

𝗔𝗿𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗔𝗻𝗼𝘁𝗵𝗲𝗿 "𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻"? 🤔

First came the LLMs (ChatGPT, Claude, Gemini)
Then came "AI RPA" ( (Computer Use, OpenAI Operator)
Then we saw LLM  using API to execute (Anthropic MCP).
Now, we also see the integration of these Agents (Google A2A)

So, yes they are coming for Digital Automation.

But AI Agents are different...

🧠 Here's why:

✨ They "Plan" - break down tasks into logical steps

✨ They coordinate multiple systems simultaneously

✨ They learn from feedback and adapt responses

✨ They understand natural language instructions

✨ They intelligently invoke tools & act

AI Agents aren't just automation tools...

They're intelligent partners that grow and adapt with you.

What do you think?

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🎯 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?

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

Repost if this helped you see the shift ♻️

𝟴𝟵% 𝗼𝗳 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗻𝗲𝘃𝗲𝗿 𝗿𝗲𝗮𝗰𝗵 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻. (𝗗𝗲𝗹𝗼𝗶𝘁𝘁𝗲)

Yet 86% of organizations are actively planning to deploy them.

The gap isn't the technology. It's how they're built.

Here's the framework that changes that 👇

1) 𝗣𝘂𝗿𝗽𝗼𝘀𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝗰𝗼𝗱𝗲
Define exactly what problem the agent solves, who it serves, what success looks like, and what constraints apply (cost, latency, compliance).

2) 𝗦𝘆𝘀𝘁𝗲𝗺 𝗽𝗿𝗼𝗺𝗽𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
This is the agent's brain wiring. Clear goals, defined role, explicit boundaries, safety guardrails. 𝗚𝗼𝗼𝗱 𝗽𝗿𝗼𝗺𝗽𝘁 𝗱𝗲𝘀𝗶𝗴𝗻 𝗿𝗲𝗱𝘂𝗰𝗲𝘀 𝗵𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝗻 𝗯𝗶𝗴𝗴𝗲𝗿 𝗺𝗼𝗱𝗲𝗹s.

3) 𝗠𝗼𝗱𝗲𝗹 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮𝗯𝗼𝘂𝘁 𝗳𝗶𝘁, 𝗻𝗼𝘁 𝗵𝘆𝗽𝗲
Context window, cost vs latency tradeoffs. The best model is the one that's right-sized for your use case, not the one that wins benchmarks.

4) 𝗠𝗲𝗺𝗼𝗿𝘆 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗺𝗼𝘀𝘁 𝗯𝘂𝗶𝗹𝗱𝘀 𝗳𝗮𝗹𝗹 𝗮𝗽𝗮𝗿𝘁
Conversational, working memory, vector databases, structured storage. Without memory, agents reset every session. User experience is broken.

5) 𝗧𝗼𝗼𝗹𝘀 𝘁𝘂𝗿𝗻 𝗟𝗟𝗠𝘀 𝗶𝗻𝘁𝗼 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗲𝗿𝘀
APIs, MCP servers, custom functions, and other agents as tools. Without tool integrations, you have a chatbot, not an agent.

6) 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗹𝗮𝘆𝗲𝗿
Workflow routing, triggers, message queues, agent-to-agent collaboration, and error handling. Production agents are systems, not scripts.

7) 𝗨𝗫 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲𝘀 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗺𝗼𝗱𝗲𝗹 𝗾𝘂𝗮𝗹𝗶𝘁𝘆
Meet users where they are. Chat, web app, API, Slack. A brilliant agent nobody uses is still a failure.

8) 𝗘𝘃𝗮𝗹𝘀 𝗶𝘀 𝗻𝗼𝗻-𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲
Ensure Outcome Quality, Reasoning Quality, Data Groundedness, Operational Efficiency, and Feedback (User Trust). Skip evals, ship a demo. Nail evals, build a system.

Here's the broader pattern worth internalizing:

𝗘𝘃𝗲𝗿𝘆 𝗳𝗮𝗶𝗹𝗲𝗱 𝗥𝗣𝗔/𝗜𝗔 𝗿𝗼𝗹𝗹𝗼𝘂𝘁 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 𝗱𝗲𝗰𝗮𝗱𝗲 𝗵𝗮𝗱 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗿𝗼𝗼𝘁 𝗰𝗮𝘂𝘀𝗲: 𝗽𝗲𝗼𝗽𝗹𝗲 𝗿𝘂𝘀𝗵𝗲𝗱 𝘁𝗼 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲𝘆 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗲𝗱 𝗮 𝘀𝘆𝘀𝘁𝗲𝗺.

AI agents are heading down the same road. The difference between a pilot and a production deployment is rarely the model. It's the architecture around it.

Build the system first. The intelligence follows.

♻️ Repost if this saves someone from shipping a demo into production.

What layer do you see teams skip most often? 👇 

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𝟲 𝗣𝗿𝗼𝘃𝗲𝗻 𝗔𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝘁𝗼 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝗬𝗼𝘂𝗿 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻.


🚀 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁’𝘀 𝗔𝗜 𝗠𝗮𝘁𝘂𝗿𝗶𝘁𝘆 𝗠𝗼𝗱𝗲𝗹

How does AI adoption evolve? This model maps the journey and shifting human roles:

- Assisted Intelligence: AI delivers insights; humans call the shots.
- Augmented Intelligence: AI boosts human creativity and decision-making.
- Autonomous Intelligence: AI takes the wheel and decides independently.
- Hybrid Stage: AI and humans collaborate, blending strengths.
- Scalability: AI matures to handle complex, organization-wide tasks.


🤝 𝗣𝘄𝗖’𝘀 𝗔𝗜 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗦𝗽𝗲𝗰𝘁𝗿𝘂𝗺

A roadmap for human-AI teamwork across six stages:

- AI as Advisor: Offers insights and suggestions for humans to act on.
- AI as Assistant: Streamlines tasks to save time and effort.
- AI as Co-Creator: Partners with humans on creative or analytical work.
- AI as Executor: Tackles tasks with little human oversight.
- AI as Self-Learner: Evolves by learning from experience.


🌍 𝗗𝗲𝗹𝗼𝗶𝘁𝘁𝗲’𝘀 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸

It’s all about balance—automation meets augmentation:

- Automate: AI handles repetitive, predictable tasks solo.
- Augment: AI serves up insights to sharpen human decisions.
- Amplify: AI supercharges productivity and scales human impact.
- Collaborate: Humans and AI team up for better outcomes.
- Adapt: AI adjusts to new challenges alongside human input.


⚙️ 𝗚𝗮𝗿𝘁𝗻𝗲𝗿’𝘀 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸

From human-led to AI-driven, here’s how work transforms:

- Manual Work: Humans run the show, no AI involved.
- Assisted Work: AI lends a hand to ease the load.
- Semi-Autonomous Work: AI leads, humans step in when needed.
- Fully Autonomous Work: AI operates solo, no supervision required.
- Optimization: AI refines processes over time for efficiency.


🧠 𝗧𝗵𝗲 “𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗟𝗼𝗼𝗽” 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹 (𝗠𝗜𝗧)

Humans stay central, especially where ethics and judgment matter:

- AI Automation: AI knocks out routine tasks alone.
- Human-in-the-Loop: Humans review or decide on AI outputs.
- Human Override: Humans can veto AI in critical moments.
- Ethical Guardrails: Humans ensure AI aligns with values.
- Continuous Feedback: Humans guide AI to improve accuracy.


🌈 𝗛𝗕𝗥’𝘀 𝗛𝘂𝗺𝗮𝗻-𝗔𝗜 𝗧𝗲𝗮𝗺𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹

AI enhances—not replaces—human work:

- AI as a Tool: Deliver data to fuel smarter human choices.
- AI as a Collaborator: Shares the workload for better results.
- AI as a Manager: Handle tasks like scheduling or tracking.
- Human Leadership: Humans set goals AI works toward.
- Synergy: AI and humans amplify each other’s strengths.


Which framework resonates with your organization’s AI journey? 


Image Credit: Timothy T Tiryaki On Linkedin

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𝗧𝗵𝗲 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗳 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗺𝗶𝗻𝗱-𝗯𝗹𝗼𝘄𝗶𝗻𝗴... 🤖

From rule-based robots to intelligent agents that plan & act.

Here's how we got here 👇

5 years ago:
🤖 RPA was king
📊 Basic process automation
💼 Simple, rule-based tasks
🔄 Repetitive work only

3 years ago:
🧠 Intelligent Automation emerged
🎯 Pattern recognition w/ML
📈 Advanced OCR, analytics
🔍 Complex process handling

Today: AI Agents are taking over...
🚀 Autonomous decision making
🎮 Self-learning capabilities
🔮 Predictive intelligence
🤝 Augmenting roles

The future isn't about automating tasks.
It's about creating digital teammates that think, learn, and evolve.

What do you think?

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⏳ 𝗧𝗵𝗲 𝗔𝗜 𝗧𝗮𝗹𝗲𝗻𝘁 𝗥𝗮𝗰𝗲 𝗜𝘀 𝗢𝗻—𝗗𝗼𝗻’𝘁 𝗚𝗲𝘁 𝗟𝗲𝗳𝘁 𝗕𝗲𝗵𝗶𝗻𝗱!

67% of Mature AI Organizations Are Creating New AI Roles.

87% already have dedicated AI teams, according to a Gartner survey.

A lack of skills and staff is holding many back. Here are a few current & emerging AI roles: 

🔹 Chief AI Officer: Leads AI strategy and oversight.

🔹 Head of AI: Directs AI initiatives and teams.

🔹 AI Architect: Designs scalable AI system architecture.

🔹 AI Product Manager: Manages the AI product development lifecycle.

🔹 AI Risk & Governance Specialist: Ensures AI compliance and risk management.

🔹 AI Ethicist: Addresses ethical implications of AI.

🔹 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 Cybersecurity Researcher: Researches AI system vulnerabilities.

🔹 AI Redteam Engineer: Tests AI systems for weaknesses.

🔹 Prompt Engineer: Optimizes prompts for AI models.

🔹 Knowledge Engineer: Translate expertise into a knowledge-based system

🔹 Decision Engineer: Designs AI-driven decision systems.

🔹 AI Process Automation Engineer: Automates processes using AI.

🔹 Data Scientist: Analyzes data for AI insights.

🔹 Data Engineer: Manages data pipelines for AI.

🔹 Analytics Engineer: Builds AI-driven analytics solutions.

🔹 D&A and AI UX Designer: Designs user experiences for AI apps.

This is a quick list based on the Gartner report and my general observations. 

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

What are the new roles you are seeing emerge? 

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89% of HR leaders expect AI to significantly reshape roles in 2026.

I reviewed LinkedIn's fastest-growing skills and mapped them to the roles. 

Here are the key roles and skills by business layer:

𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗔𝗜 𝗥𝗼𝗹𝗲𝘀
→ AI Strategy Advisor: roadmapping, value-case prioritization, ROI modeling
→ AI Transformation Lead: change management, AI-driven process redesign
→ AI Governance Lead: responsible for AI, ethical AI, and compliance frameworks
→ AI Fluency Lead: AI literacy for executives, AI-ready culture building

𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗔𝗜 𝗥𝗼𝗹𝗲𝘀
→ AI Engineer: ML fundamentals, model training, deployment, monitoring
→ Generative AI / LLM Engineer: prompt engineering, RAG, vector DBs, API integration
→ MLOps / LLMOps Engineer: model versioning, CI/CD for ML, observability, scaling
→ Workflow Automation Engineer: process automation, RPA + AI, no-code/low-code platforms

𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝗙𝗮𝗰𝗶𝗻𝗴 𝗔𝗜 𝗥𝗼𝗹𝗲𝘀
→ AI-Enabled Marketing Strategist: audience segmentation, content personalization, campaign optimization
→ AI-Driven Growth Marketer: AI-powered experimentation, funnel optimization, growth analytics
→ AI-Enabled Sales Ops Lead: lead scoring, CRM automation, AI-driven forecasting
→ AI-Driven GTM Strategist: channel strategy, pricing, and packaging with AI insights

Three things stand out from this mapping:

𝗙𝗶𝗿𝘀𝘁: AI isn't just reshaping the tech layer. It's rebuilding strategy, ops, sales, and marketing from the inside out. 

𝗦𝗲𝗰𝗼𝗻𝗱: The hiring lens has flipped. "Title + degree" is no longer the filter. Skills + AI fluency is. That's a structural shift, not a trend.

𝗧𝗵𝗶𝗿𝗱: Most organizations are patching one layer and calling it an AI strategy. The teams pulling ahead are building capability across all three simultaneously.

The net shift is from credential-driven work to skill-driven work.

If you're not intentionally building AI fluency at the strategic, technical, and business levels, you're not hedging your career or your team. You're just waiting.

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

Which of these roles are you building toward or hiring for right now?

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𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸

1️⃣ Eliminate
Remove unnecessary steps, approvals, and handoffs.

2️⃣ Redesign
Clearly define the outcomes instead of the sequence of tasks. 

3️⃣ Implement agents
Define guardrails, access & constraints. Then let agents achieve the outcomes. 

𝗥𝗣𝗔 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗮𝗹𝗹 𝘀𝘁𝗲𝗽𝘀. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗲𝘀 𝘀𝘁𝗲𝗽𝘀.

The deeper shift: processes were designed for human coordination.
Multiple approvers. Serial handoffs. Explicit instructions at every turn.

Agents don't need that scaffolding.
They need goals, guardrails, and access to systems.

Don’t add intelligence to broken workflows
Redesign your work itself for the Agentic world. 

What do you think?

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𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟲 𝗶𝘀𝗻'𝘁 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗰𝗼𝗱𝗲 𝗮𝗻𝘆𝗺𝗼𝗿𝗲.

I just got a call asking for engineers who can "solve problems" w/Claude Code.  

Not Python. Not JavaScript. Not even AI/ML - Agent orchestration.  

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝗹𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁:  

🚀 Full System Access: Reads, writes, and organizes files directly on your machine. Process 10,000 files in minutes.  

⚡ Agent Swarms: Delegate as if you have 5 junior employees running in parallel. Meeting transcript → PRD → Jira tickets → dashboard. 

🔌 MCP Protocol: One interface for 200+ tools. Connect GitHub, Jira, PostgreSQL, etc. One protocol, hundreds of connections.  

💾 Skills + Project Memory: Reusable automations Claude auto-loads. CLAUDE md = Project context and preferences persist forever.  

𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀:
/clear → reset context 
/compact → save tokens
/model → switch models
/mcp → check connections
/help → all commands
Use @ to reference files (@filename, @folder/).

𝗣𝗿𝗼 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄:  Ask Claude to make a plan first → Review before exec → Create checkpoints → Rewind if needed → Iterate with feedback  

We now know the key skills for AI Software development.

The 𝟳 𝘀𝗸𝗶𝗹𝗹𝘀 that now define high-value AI roles:  
1) 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗙𝗿𝗮𝗺𝗶𝗻𝗴: Define intent and constraints with precision  
2) 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Design durable AI guidance systems  
3) 𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝗹𝗲𝗴𝗮𝘁𝗶𝗼𝗻: Coordinate parallel AI workflows  
4) 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁: Evaluate output for risk and quality  
5) 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝘀𝗶𝗴𝗻: Build quality gates at scale  
6) 𝗧𝗼𝗼𝗹𝗰𝗵𝗮𝗶𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: Create AI-operable systems  
7) 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Shape human-AI collaboration  

With Claude Opus and Claude Code, this isn't coming. It's here.  

People are already being interviewed and recruited for these roles.  

Join our new session on Agentic AI: https://lnkd.in/gzWjhAzX

What are you waiting for?

Cheatsheet: aakashgupta on Substack

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𝗜 𝗷𝘂𝘀𝘁 𝘁𝘂𝗿𝗻𝗲𝗱 𝟭𝟬𝟬+ 𝗵𝗼𝘂𝗿𝘀 𝗼𝗳 𝗰𝗼𝗻𝘀𝘂𝗹𝘁𝗶𝗻𝗴 𝘄𝗼𝗿𝗸 𝗶𝗻𝘁𝗼 𝗮 𝟭𝟬-𝗺𝗶𝗻𝘂𝘁𝗲 𝗔𝗜 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 𝗳𝗶𝗻𝗱𝗲𝗿.

The problem it solves: every IT/Automation/Business leader knows AI matters, but most struggle to answer "which use case should we build first?"

That's the question I normally solve in discovery engagements. It requires understanding the business context, current processes, and where the ROI actually lives.

I took that diagnostic process and compressed it into an agentic app.

The foundation: 546 AI agent use cases across 14 business functions. These came from:
- Real IA consulting projects I've run
- Market research across industries
- Enterprise case studies

The app shows you relevant use cases and asks you 6-8 questions about your situation, then generates a custom 1-page opportunity scan showing:
- Top use cases that you can implement now w/ savings potential
- Whether each fits into Gen AI, APA, Pure agentic (or RPA/IA) 
- A quick business case with estimated value & next steps

Also gives you an AI Automation score based on your AI opportunities & readiness.  

It's the assessment I wish existed when I started scaling AI Automation programs.

Want to try it? Comment below or DM me, and I'll send you the link.
Or drop me a line: https://lnkd.in/gex3-pV4

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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

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𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗶𝘀 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗿𝗼𝗹𝗲𝘀.
Nobody is talking about the 𝟭.𝟯𝗠 𝗻𝗲𝘄 𝗿𝗼𝗹𝗲𝘀 𝗔𝗜 𝗷𝘂𝘀𝘁 𝗰𝗿𝗲𝗮𝘁𝗲𝗱.

The LinkedIn Labor Market Report 2026 just dropped, and here's what it shows:
A new collar workforce is forming around AI at the top of the org chart to the bottom.

New real-world roles that barely existed 2 years ago:
- 𝗛𝗲𝗮𝗱 𝗼𝗳 𝗔𝗜: leads AI strategy and model development (298K created)
- 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: builds and operationalizes AI systems (177K, up 13x)
- 𝗙𝗼𝗿𝘄𝗮𝗿𝗱-𝗗𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 / 𝗣𝗠:  ensures AI gets embedded in actual workflows (9K, up 42x)
- 𝗔𝗜 𝗙𝗼𝗿𝗲𝗻𝘀𝗶𝗰 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: keeps AI systems accountable and aligned with human judgment (49K)
- 𝗗𝗮𝘁𝗮 𝗔𝗻𝗻𝗼𝘁𝗮𝘁𝗼𝗿: provides the human-labeled data that trains every model (774K)

𝗔𝗜 𝗶𝘀 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗷𝗼𝗯𝘀 𝗮𝘁 𝗯𝗼𝘁𝗵 𝗲𝗻𝗱𝘀 𝗼𝗳 𝘁𝗵𝗲 𝘀𝗸𝗶𝗹𝗹 𝘀𝗽𝗲𝗰𝘁𝗿𝘂𝗺.
- 𝗛𝗶𝗴𝗵-𝗲𝗻𝗱: Forward-deployed engineers who can build AND embed AI into real business workflows. 42x growth. Hottest role in tech right now.
- 𝗟𝗼𝘄-𝗲𝗻𝗱: Data annotators doing the invisible labor that makes all of it work. 774K jobs and growing. Highest single tole and this role is growing!

The uncomfortable truth?
𝗨𝗦 𝗷𝗼𝗯 𝗽𝗼𝘀𝘁𝗶𝗻𝗴𝘀 𝗿𝗲𝗾𝘂𝗶𝗿𝗶𝗻𝗴 𝗔𝗜 𝗹𝗶𝘁𝗲𝗿𝗮𝗰𝘆 𝗴𝗿𝗲𝘄 𝟳𝟬% 𝘆𝗲𝗮𝗿-𝗼𝘃𝗲𝗿-𝘆𝗲𝗮𝗿.
Only 3% of US professionals list AI skills on their profiles.

That gap is not a warning sign.
It's the biggest career opportunity of this decade.

The fastest-growing skills pair AI fluency with what AI can't replicate: 
Process optimization, conflict mitigation, adaptability, and innovative thinking.

𝗧𝗵𝗲 𝗿𝗮𝗰𝗲 𝗶𝘀𝗻'𝘁 𝗵𝘂𝗺𝗮𝗻𝘀 𝘃𝘀. 𝗔𝗜.
It's humans who work with AI vs. humans who don't.

53% of US professionals plan to proactively learn new AI skills in the next 6 months.

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

What are you planning? 

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𝗦𝗧𝗢𝗣 𝗰𝗮𝗹𝗹𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 "𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜" 🛑

There's still massive confusion between task automation, agentic workflows, and true agentic orchestration.

Here's what you ACTUALLY need to know:

🤖 RPA = Task Automation
→ Structured, repetitive tasks
→ High-volume processing
→ Lower costs (no AI model fees)

🧠 APA = Agentic Workflows
→ Controlled AI agents in workflows
→ Operate within defined workflows
→ Agency within a defined context

⚡ Agentic AI = True Autonomy
→ Complex autonomous decision-making
→ Real-time reasoning and planning
→ Adaptive architectures that evolve

Most companies think they only need #3.
The winning companies? They're using the RIGHT automation for each task.

What automation challenges are you facing?

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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?

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🤖 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?

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Nandan Mullakara

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