1. Train teams and individuals in AI
2. Build AI strategy to deliver transformational value for my clients
3. Conducted business assessment (Technology, People, Processes, and Data), tool evaluation, building a business case and AI implementation roadmap to deliver value 
4. Expertise to define client’s business requirements, defining use cases, improving business processes, conducting fit-gap analysis, developing functional designs, and working with technical teams to implement digital solutions
5. Creative self-starter with integrative thinking skills, capable of forming and maintaining positive and productive working relationships in internal, external, independent, and team environments
6. Experience to manage senior stakeholders and building alignment within teams.

AI Advisor and Investor

Accenture

Responsible to help clients build AI strategies and transform their businesses by leveraging my experience in Artificial Intelligence, assessment, tool evaluation, building a business case, implementation roadmap, and implementation of AI solutions using Agile and Waterfall methodology

Digital Transformation Advisor

Navsparry

Founder and CEO of my AI start-up to build customer service solutions for small businesses.
1. Built a new AI-based solution from scratch
2. Managed a small team to launch the product in the market
3. Client and Stakeholders management

Founder and CEO 

Tata Consultancy Services

Responsible to help clients build digital strategies and transform their businesses by leveraging my experience in Machine Learning, assessment, design, and implementation of ML solutions using Agile methodology.

Digital Consultant

Cybage Software

Business Analyst: 
1. Helped client assess, design, and implement digital solutions
2. Built data strategy 
3. Interacted with the client to understand their business need 
4. Ensure the successful launch of digital solution

Business Analyst

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

🔹 Contact me for:
1. Speaking engagement
2. Corporate: AI training, workshop and business strategy
3. Individual: AI training and career coaching

🔹 Follow me for Artificial Intelligence news, insights, future trends, career advice and hiring posts.

🔹 With 20 years of consulting experience in AI and digital transformation to help F100 clients create over $2B in value. As a tech enthusiast, I stay at the forefront of AI developments, ensuring that my content reflects the latest trends and advancements. From machine learning and natural language processing to computer vision and neural networks, I possess a deep understanding of diverse AI disciplines

🔹 On a mission to help more than 1 billion professionals by transforming complex AI concepts into engaging content. Join me in my mission 🚀

🔹 Working with businesses, I offer tailored content strategies that harness the power of AI to drive success. I collaborate closely with clients to understand their unique goals and craft compelling content that captivates audiences, educate/train potential customers, and positions them as thought leaders in their industries

🔹 Through my articles, blog posts, and thought leadership pieces, I share insights and perspectives on AI's impact across various sectors, from healthcare and finance to manufacturing and e-commerce. By staying ahead of the curve, I enable readers to navigate the AI landscape confidently and make informed decisions

🔹 With a passion for both technology and effective communication, I create captivating and educational content that demystify AI concepts and showcase its transformative potential. By blending my expertise in storytelling and technical knowledge, I aim to inspire viewers and ignite their curiosity about the incredible possibilities of AI.

🔹 My Specialties: 

1. Artificial Intelligence
2. Machine Learning
3. Deep Learning
4. Digital Strategy and Transformation
5. Business Strategy 
6. ChatGPT (Basic and Advanced)
7. AI workshop and training to businesses and individuals
8. Career Coaching and Planning
8. Keynote Speaker

Hashtags: 
#artificialintelligence #technology #machinelearning #chatgpt #productivity #data #careergrowth #strategy #business

-------------------------------------------------------------
My posts are my own and do not reflect the opinions of my current, past, or future employers.

Naveeta Sehgal

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? 

----

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

Repost if this helped you see the shift ♻️

𝗜'𝗺 𝗵𝗲𝗮𝗿𝗶𝗻𝗴 𝘀𝘁𝗼𝗿𝗶𝗲𝘀 𝗮𝗯𝗼𝘂𝘁 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗳𝗮𝗶𝗹𝘂𝗿𝗲𝘀.

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

And I know exactly why...

All fancy AI licenses are worthless because you are:

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

The truth?

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

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

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

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

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

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

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

Remember: Having AI isn't enough.

Success lies in your people adopting it.

What do you think?

----

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

Repost if this helped you see the shift ♻️

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

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

The architecture is already here:

🎯 𝗔𝗣𝗣𝗟𝗜𝗖𝗔𝗧𝗜𝗢𝗡 𝗟𝗔𝗬𝗘𝗥

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

🤖 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗟𝗔𝗬𝗘𝗥

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

⚙️ 𝗜𝗡𝗙𝗥𝗔𝗦𝗧𝗥𝗨𝗖𝗧𝗨𝗥𝗘 𝗟𝗔𝗬𝗘𝗥

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

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

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

The companies building these layers now will dominate tomorrow.
Too aggressive or inevitable? What's your take?.

----

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

Repost if this helped you see the shift ♻️

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 

----

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

Repost if this helped you see the shift ♻️

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

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

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

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

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

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

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

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

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

----

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

Repost if this helped you see the shift ♻️

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?

----

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

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?

----

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

Repost if this helped you see the shift ♻️

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

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

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

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

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

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

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

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

----

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

Repost if this helped you see the shift ♻️

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

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?

----

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

Repost if this helped you see the shift ♻️

Now we have many hammers... 

But the approach remains the same 😃

What do you think? 

----

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

Repost if this helped you see the shift ♻️

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

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

Then there's RPA.

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

How did we get here?

Simple. Business moved faster than IT departments.

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

NOW.

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

The result? 

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

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

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

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

----

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

Repost if this helped you see the shift ♻️

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?

----

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

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?

----

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

Repost if this helped you see the shift ♻️

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?


----

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

Repost if this helped you see the shift ♻️

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!

----

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

Repost if this helped you see the shift ♻️

4 Agentic Architectures quietly changing the AI game

𝗚𝗼𝗼𝗴𝗹𝗲 𝗷𝘂𝘀𝘁 𝗮𝗱𝗺𝗶𝘁𝘁𝗲𝗱 𝘄𝗵𝗮𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗵𝗮𝘃𝗲 𝗸𝗻𝗼𝘄𝗻 𝗳𝗼𝗿 𝘆𝗲𝗮𝗿𝘀.

AI agents don't fail because of LLMs or Tools.

They fail because teams skip the Orchestration Layer.

Google's new report identifies it as the core part of AI agents. 

It's the "𝗡𝗲𝗿𝘃𝗼𝘂𝘀 𝗦𝘆𝘀𝘁𝗲𝗺" - the layer that actually gets agents into production.

Here's what it handles:

🧠 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆→ Decides when to reason vs. act using Chain-of-Thought and ReAct

💾 𝗠𝗲𝗺𝗼𝗿𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁→ Provides contextual recall from databases and long-term memory

🛠️ 𝗧𝗼𝗼𝗹 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻→ Invokes the right API at the exact right moment

🛡️ 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲→ Enforces biz rules over non-deterministic AI


The orchestration "𝗧𝗵𝗶𝗻𝗸, 𝗔𝗰𝘁, 𝗢𝗯𝘀𝗲𝗿𝘃𝗲" loop in action:

🎯 𝗠𝗶𝘀𝘀𝗶𝗼𝗻: User requests "Organize team travel"
🔍 𝗦𝗰𝗮𝗻: Agent gathers context from memory and databases
🧠 𝗧𝗵𝗶𝗻𝗸: LLM plans: "Flight data first, then calendar sync"
⚡ 𝗔𝗰𝘁: Executes APIs and integrations
👁️ 𝗢𝗯𝘀𝗲𝗿𝘃𝗲: Reviews results and loops back

Google is pushing its Agent Development Kit to give us code-first control over this loop.

This is Agentic Process Automation (APA), where rules and cognition work together.

The bottom line?

Without orchestration, your agents are just expensive pilots.

Deep observability, formal governance, and cognitive workflows aren't optional for AI agents - they're the price of production.

Check out the complete Google report: 
https://lnkd.in/gV8Ysp9N

How are you handling orchestration in your Agentic AI deployments?

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

♻️ Repost if this resonated

🚨 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?

----

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

Repost if this helped you see the shift ♻️

🚨 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? 👇

----

🎯 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?

----

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

Repost if this helped you see the shift ♻️

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?

----

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

Repost if this helped you see the shift ♻️

taplio

The Battle

Naveeta Sehgal

Nandan Mullakara

Followers

Posts

Engagements

Likes

Performance ScoreNandan Mullakara's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Naveeta Sehgal

Nandan Mullakara

Followers

Posts

Engagements

Likes

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

Ready to grow your LinkedIn presence?

Performance ScoreNandan Mullakara's LinkedIn Performance