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.

Bot Nirvana

Bot Nirvana is a specialized AI consulting firm focused on helping businesses build and deploy AI agent teams that deliver measurable results. Our AI practitioners guide organizations through proven methodologies to integrate AI agents strategically, moving beyond theory to practical implementation with guaranteed outcomes.

Our consulting is powered by and contributes to a premier global community of AI leaders from 50+ organizations across 15 countries, creating a continuous cycle of real-world insights and proven strategies.

Founder

Packt

The best way to learn RPA is by doing real-life projects.

So, I co-authored an RPA Projects book with step-by-step instructions to get you started on real-world projects.
The projects use free community RPA versions and free Saas apps so that you can easily do them yourself.

Author - Robotic Process Automation Projects

Fujitsu Americas

Exploiting  Robotic Process Automation (RPA), Cognitive computing and Fujitsu IP to enable our Digital Workforce. Leading the evolution of this Managed platform with People, Process and Technology.

Head of Robotic Process Automation (RPA)

Manage Transitions, Transformations & Service Delivery for complex global AMS engagements 

Implemented Intelligent Automation and Digital Transformation leveraging Fujitsu IP 

Led and participated in multiple multi-million dollar RFPs through the entire life cycle from solution design, estimation, proposal, solution walk through, due diligence and transition. 

Successful in building new accounts; growing existing accounts and ensuring Customer satisfaction.

Practice Manager

Associate Practice manager for E-Business Practice (Business unit). Lead the Java and Middleware application practice. Practice management including Customer relations, Complex Bids and Delivery.

Associate Practice Manager 

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 strategic advisor, author, and podcaster focused on AI‑led automation and agentic AI. A recognized voice in Onalytica’s “Who’s Who in Automation,” he co‑authored bestselling books Agentic AI and the  RPA Projects books that have been cited widely across academia and industry.

Nandan previously led the RPA consultancy at Fujitsu Americas, where he helped build the Fujitsu Digital Automation offering and scale enterprise automation programs. He shares practical playbooks at nandan.info and leads Bot Nirvana - a community, advisory practice, and top‑ranked AI & Automation podcast at BotNirvana.org.

Nandan Mullakara

⏳ 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. 

What are the new roles you are seeing emerge? 

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Repost if this helped you see the shift ♻️

The careers that will dominate 2026 are being built today.

88% of execs plan to increase AI budgets.
So, 81% of Tech professionals want to use AI.
But, only 12% have the skills to do so effectively.

Here are the AI roles & skills that will define the next era:

🔹 Head of AI: Leads the organization's overall AI strategy and teams. Skills: Strategic Planning, Leadership, Business Acumen

🔹 AI Architect: Designs the end-to-end blueprint for enterprise AI. Skills: System Design, Cloud Platforms, MLOps

🔹 AI Product Manager: Manages the AI product vision and lifecycle. Skills: Product Strategy, GenAI Understanding, Market Research

🔹 Data Engineer: Builds and manages large-scale data pipelines for AI. Skills: Data Pipelines, Python, SQL

🔹 ML Engineer: Builds and deploys production-grade machine learning models. Skills: Python, ML Algorithms, MLOps

🔹 Data Scientist: Extracts insights and builds predictive model prototypes. Skills: Statistical Analysis, Python, ML Algorithms

🔹 LLM Engineer: Specializes in building systems using Language Models. Skills: LLMs, RAG, Vector Databases

🔹 Forward Deployed Engineer: Deploys custom AI solutions directly with clients. Skills: Customer-Facing, Python, System Design

🔹 AI Risk & Governance Specialist: Ensures AI is compliant, ethical, and safe. Skills: AI Ethics, Risk Assessment, Compliance

🔹 Prompt Engineer: Optimizes inputs for Generative AI model performance. Skills: Prompt Engineering, LLMs, Python

🔹 Decision Engineer: Designs and builds automated decision-making systems. Skills: Decision Modeling, Business Rules, ML

🔹 Model Validator: Independently assesses AI model accuracy and reliability. Skills: Statistical Analysis, ML Algorithms, Risk

🔹 Analytics Engineer: Transforms raw data into clean, analysis-ready datasets. Skills: SQL, dbt, Data Modeling

🔹 AI Engineer: Generalist role building and integrating AI systems. Skills: Python, ML/DL Frameworks, Software Engineering

🔹 Knowledge Engineer: Structures human expertise for knowledge-based AI systems. Skills: Ontology Design, Logic, Data Modeling

🔹 AI Ethicist: Provides expert guidance on AI's ethical implications. Skills: Ethical Frameworks, Policy, AI Principles

🔹 UX Designer (for AI): Designs human-centered experiences for AI applications. Skills: User Research, Prototyping, Interaction Design

🔹 Model Manager: Oversees the entire AI model lifecycle and strategy. Skills: Leadership, MLOps, AI Governance

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

What new roles and skills do you see emerging?

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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.

What do you think?

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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.

What do you think? 

Image credit: Ralph Aboujaoude Diaz 

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Repost if this helped you see the shift ♻️

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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Repost if this helped you see the shift ♻️

𝗧𝗵𝗲 $𝟲𝟬𝟬𝗕 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗺𝗮𝗿𝗸𝗲𝘁 𝗶𝘀 𝗮𝗯𝗼𝘂𝘁 𝘁𝗼 𝗰𝗵𝗮𝗻𝗴𝗲..."

A big convergence is happening. UiPath, AA, Pega, MuleSoft, etc are all racing towards the same future - but most people don't see it coming.

🔄 The Evolution of Enterprise Automation:

• RPA → Intelligent Automation
• BPM/BPA → Digital Process Automation
• ESB → Integration Platform as a Service

All roads now lead to one destination: Agentic Process Automation (APA) or Agentic Automation

Why? Because AI agents are the future of automation. They can:

🤖 Think and reason like humans
🔍 Makes decisions autonomously
🔄 Adapts quickly to changing conditions
⚡ Work across multiple systems seamlessly

The future of automation is not about one tech or the other —it's convergence.

What do you think?

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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 AI deployment success.

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

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Repost if this helped you see the shift ♻️

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

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.

📊 Our 80+ page APA report breaks it down:
- How AI Agents are rewriting the automation game
- The great automation convergence with Agentic AI
- Who’s winning the APA race: capabilities, strengths, and weaknesses
- The ROI goldmine: saving costs, moving fast, beating competition
- Step-by-step APA rollout plan

https://lnkd.in/g4VHJx5M

What automation challenges are you facing?

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Now we have many hammers... 

But the approach remains the same 😃

What do you think? 

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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?

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JSESSIONID

Talks about #ai, #rpa, #automation, #innovation, and #technology

Ashish Vats

🤖 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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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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𝟵𝟬% 𝗼𝗳 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘄𝗶𝗹𝗹 𝗳𝗮𝗶𝗹 𝗶𝗻 𝟮𝟬𝟮𝟱 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘁𝗵𝗲𝘆'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 Agentic AI in the wrong order.

BCG just proved it with data and a detailed roadmap.
95% of enterprises should focus on Steps 0-2. Most are trying to build Steps 3-4.
(This aligns perfectly with the prioritization quadrant I shared: https://lnkd.in/gVghgJ8G)

Their message? Stop trying to build the future. Build what works TODAY.

📊 The BCG 5-Horizon Agent Development Framework:

🎯 HORIZON 0 | Constrained Agents
→ Status: Deploy at scale NOW
→ What: Rule-based, single-task automation
→ Example: Customer service Q&A chatbots
→ Why: Proven ROI, minimal risk

🎯 HORIZON 1 | Single Agents
→ Status: Adoption rising - prioritize here
→ What: Multi-step planning in controlled environments
→ Example: Chatbot that diagnoses issues AND takes action
→ The shift: From scripts to autonomous decision-making

🎯 HORIZON 2 | Deep Agents
→ Status: adoption rising - prioritize next
→ What: Orchestrator + specialist agents
→ Example: Employee onboarding agentic AI solution
→ The breakthrough: Managing complexity without chaos

⏸️ HORIZON 3 | Role-Based Agents
→ Status: TOO EARLY - wait
→ What: AI agent teams with distinct roles
→ Why wait: LLM model reasoning isn't reliable enough yet

⏸️ HORIZON 4 | Agent Mesh
→ Status: R&D phase only
→ What: Self-organizing agent networks
→ Reality check: 2-3 years out for most enterprises

🔑 YOUR Golden Rule:
"Start simple. Add complexity ONLY when simplicity fails."

Real example - Loan application processing:
→ Try first: Single agent handles validation + updates
→ If it breaks: Add orchestrator + split validation into separate agent
→ Still breaking? Orchestrator still manages the flow, and a Remediation Agent is added to handle the next steps. 

⚠️ Why this matters for AI & Automation leaders:

Every vendor pitches autonomous agent ecosystems. 
BCG's data shows that's not where ROI lives in 2025.

Companies winning with AI agents:
✅ Deploy constrained agents at massive scale
✅ Prove value with focused single-agent use cases
✅ Add orchestration and remediation as and when needed

💡 Bottomline for AI & Automation leaders:
Your job isn't to build the most sophisticated agent system.
It's to drive business outcomes with the simplest reliable solution.

Master Horizon 1 before you dream about Horizon 3.

Where is your organization on this roadmap? And more importantly—where SHOULD you be?


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𝗠𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗯𝗶𝗴 𝘁𝗵𝗶𝗻𝗴 𝗶𝗻 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. 

Here are the 6 Agentic architectures that are driving the big shift: 

🧠 Hierarchical: A supervisor agent orchestrates specialized sub-agents for structured decision flow. (most common)
Example: Supervisor coordinates retrieval from internal data, web searches, and personal accounts.

🤝 Human-in-the-loop: Requires human input before proceeding, especially with sensitive data.
Example: Agent asks for human approval before entering invoice data into the invoice system.

🔧 Shared tools: Different agents have access to the same set of shared tools.
Example: Agents access a central database with user information using a search tool.

🔄 Sequential: Agents process tasks one after another, the output of one becoming input for the next.
Example: A query agent retrieves information using vector search, then another uses web search.

🔗 Network (Horizontal) Architecture: Agents communicate directly, forming a decentralized network.
Example: Multiple agents in a customer service system collaborate to resolve a customer issue

🖥️ Data Transformation Architecture: Dedicated to enriching and transforming data.
Example: Agent transforms customer reviews by generating review summaries

🔁 Here are some patterns that can be combined with the above:
- Loop: Iterative for continuous improvement
- Parallel: Agents work simultaneously
- Router: Central agent determines tasks
- Aggregator/Synthesizer: Collects and synthesizes outputs

From the Weaviate Agentic Architectures e-book:
https://lnkd.in/gZzDCuQg

Embrace these architectures to future-proof your tech strategy.

What's your favorite architecture? Share your thoughts!

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4 Agentic Architectures quietly changing the AI game

🚨 Google panicked over ChatGPT. OpenAI panicked over Gemini 3. 𝗕𝗜𝗚𝗚𝗘𝗥 𝗣𝗔𝗡𝗜𝗖: 𝟵𝟱% 𝗼𝗳 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 𝗰𝗮𝗻'𝘁 𝗴𝗲𝘁 𝗽𝗮𝘀𝘁 𝘁𝗵𝗲 𝗽𝗶𝗹𝗼𝘁 𝘀𝘁𝗮𝗴𝗲.


In 2022, Google declared "code red" over ChatGPT.

In 2025, OpenAI declared "code red" over Google's Gemini.


Meanwhile, the real code RED:

95% of enterprise AI pilots are FAILING. (MIT)

We spend $6.5M annually on AI initiatives. Yet 80% projects fail outright.


Here's why & what nobody's talking about:

→ ~80% cite poor data quality as the real blocker

→ 50% lack skilled AI professionals - most pilots collapse w/talent gaps

→ Many struggle with legacy system integration when deploying agentic AI

→ No clear ROI or business objectives - most pilots are "proof-of-cost" experiments

→ 80%+ of failures are organizational - change resistance, poor management, cultural barriers


The models are ahead. Your organization isn't. 

𝗧𝗵𝗲 𝗼𝘃𝗲𝗿𝗵𝗮𝗻𝗴 𝗶𝘀 𝗿𝗲𝗮𝗹:
→ Models evolve at warp speed
→ Business adoption is little to none
→ The gap costs billions in unrealized value


𝗪𝗵𝗮𝘁 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝗰𝗵𝗮𝗻𝗴𝗲?

1. Enterprises: Invest in data quality, security & other foundational blocks

2. Businesses: Work on mindset shifts, change mgmt & skill development

3. Model providers: Partner with enterprises on use cases that deliver real value

4. Consulting providers: Move from selling hours to outcome-based models with AI


The winners aren't waiting for better models.

𝗧𝗵𝗲𝘆'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝘁𝘁𝗲𝗿 𝗢𝗥𝗚𝗔𝗡𝗜𝗭𝗔𝗧𝗜𝗢𝗡𝗦 𝘁𝗵𝗮𝘁 𝗮𝗿𝗲 𝗿𝗲𝗮𝗱𝘆 𝘁𝗼 𝗮𝗽𝗽𝗹𝘆 𝗔𝗜.


Your take? What's YOUR biggest AI implementation barrier?

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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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𝗜𝘁'𝘀 𝗻𝗼𝘁 𝗥𝗣𝗔 𝘃𝘀. 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀. 𝗜𝘁'𝘀 𝗥𝗣𝗔 + 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀.
UiPath just proved this with results that beat expectations:

→ $1.78B ARR (up 11% YoY) 

→ 2,500+ customers at $100K+ ARR 

→ First GAAP-profitable quarter EVER


But here's what really matters...

Their APA strategy seems to be working:

- Start with installed base ✓
- Add Agentic AI layer ✓
- Expand wallet share ✓

They still call it “agentic automation,” but it is APA as is its AI gents in existing RPA workflows.

This isn't about replacing automation. It's about amplifying it.

The market noticed. PATH jumped 20%+ post-earnings.

Am I celebrating yet? No.

The brutal truth:

- Growth is nowhere near hyper-growth RPA days
- Big Tech - Google, Amazon, etc., are coming for this space
- There are APA startups (e.g., Simular) that are raising millions

But UiPath has something competitors don't: 2,500+ enterprises already running their workflows.

That's not a moat. That's a launchpad.

I'm holding my position. Why? Because the hardest part of enterprise AI adoption isn't the technology. It's the trust and integration layer.

UiPath already has both.

The question isn't IF APA wins.

It's WHO wins at agentic process automation.

What do you think?

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🚨 𝗛𝗔𝗥𝗗 𝗧𝗥𝗨𝗧𝗛: 𝟰𝟬% 𝗼𝗳 "𝗔𝗜 𝗔𝗴𝗲𝗻𝘁" 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘄𝗶𝗹𝗹 𝗳𝗮𝗶𝗹 𝗯𝘆 𝟮𝟬𝟮𝟳 (𝗚𝗮𝗿𝘁𝗻𝗲𝗿)

While everyone's jumping on the agentic AI bandwagon...
Gartner just dropped some sobering statistics:

- Only 130 out of THOUSANDS of "agentic AI" vendors are real
- Many vendors are just "agent washing" existing chatbots and RPA tools
- 19% of companies made significant investments, but most lack a clear ROI

The brutal reality? Most current AI models aren't mature enough to autonomously handle complex business goals.

But before you panic, here's what smart leaders are doing differently:

❌ WRONG: Jumping on every "AI agent" solution
✅ RIGHT: Asking "Does this deliver measurable ROI?"

❌ WRONG: Retrofitting agents into inefficient workflows
✅ RIGHT: Redesigning workflows with AI-first thinking

❌ WRONG: Believing vendor promises about "revolutionary agents"
✅ RIGHT: Recognizing only a few vendors are legitimate

The massive opportunity? By 2028:
- 15% of work decisions will be autonomous
- 33% of enterprise software will include real agentic AI

The winners will be those who:
✅ Focus on clear business value over hype
✅ Rethink workflows from the ground up
✅ Use agents for decisions, automation for routine tasks

Stop chasing shiny objects. Start building sustainable AI strategies.

What's your take on the agentic AI hype? 👇

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𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝗲 𝗵𝗮𝗿𝗱. 

This reference architecture provides a blueprint from user interfaces to performance monitoring: 

🌐 Interaction Layer:
- Seamless user & app interaction with multi-agent system. 
- Robust interfaces mitigate errors while guiding the agents.

🔄 Workflow Layer:
- Enables Controlled and Predictable agent interactions 
- Adds Human oversight to ensure risk management

🤖 Agents Layer:
- Create, manage, and deploy role-specific agents
- Industrializes the creation of agents with LLMs and:
   - Tools like search engines, financial analysis, code interpreters
   - Data Sources like customer records, market data, incident history
   - Memory including short & long-term memory for seamless task execution

📈 Agent Operations Layer:
- Continuous performance monitoring via operational, qualitative, and thought metrics

What do you think? 

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𝗧𝗵𝗲 𝗨𝗻𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲 𝗧𝗿𝘂𝘁𝗵: 𝗘𝘃𝗲𝗿𝘆 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝘄𝗮𝗻𝘁𝘀 𝘁𝗼 𝗵𝗶𝗿𝗲 𝘀𝗲𝗻𝗶𝗼𝗿 𝗔𝗜 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀.

Zero companies want to create them.
See the problem? 🤔

I came across this photo recently, and it hit me - we're literally expecting people to start their careers with decades of experience. 📚

𝗦𝗼 𝗹𝗲𝘁 𝗺𝗲 𝗮𝘀𝗸 𝘆𝗼𝘂 𝘁𝗵𝗶𝘀:
Where exactly do you think those senior people will come from in 5 years? 🤔

𝗪𝗲'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝘁𝗮𝗹𝗲𝗻𝘁 𝗰𝗿𝗶𝘀𝗶𝘀 𝘄𝗶𝘁𝗵 𝗼𝘂𝗿 𝗼𝘄𝗻 𝗵𝗮𝗻𝗱𝘀.
By refusing to invest in entry-level talent TODAY, we're guaranteeing we'll have NO senior talent TOMORROW.

The pipeline doesn't fill itself:
Entry-level talent → Junior engineers → Mid-level specialists → Senior experts → Leaders

Skip the first step? The whole chain breaks.
Senior talent doesn't appear magically. You have to grow it.

𝗧𝗼 𝗵𝗶𝗿𝗶𝗻𝗴 𝗺𝗮𝗻𝗮𝗴𝗲𝗿𝘀 𝗶𝗻 𝗔𝗜: If you want innovation, hire for potential and curiosity, not just years on a resume.
𝗧𝗼 𝗷𝗼𝗯 𝘀𝗲𝗲𝗸𝗲𝗿𝘀: Apply anyway. Many requirements are wish lists, not gatekeepers.

What do you think? 

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The Battle

Dr. Manish Mehta

Nandan Mullakara

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The Battle

Dr. Manish Mehta

Nandan Mullakara

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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

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