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.

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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#AI #innovation #technology #automation

Nandan Mullakara

Follow for Agentic AI, Gen AI & RPA trends | Co-author: Agentic AI & RPA Projects | Oanalytica Who's Who in Automation | Founder, Bot Nirvana | Ex-Fujitsu Head of Digital Automation

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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#AI #innovation #technology #automation

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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#AI #innovation #technology #automation

Automation 101

Here's your checklist in the order of priority: 

🗑️ Eliminate
⚙️ Optimize
📏 Standardize

🤖 Then Automate

What do you think? 

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𝗟𝗶𝗳𝗲 𝗶𝗻 𝗮𝗻 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝗘. 

It doesn't have to be that way. Avoid these automation pitfalls:

🚫 Unrealistic timelines.
✅ Instead: Plan for phased implementation over months, not weeks

🚫 Overambitious goals.
✅ Instead: Set realistic ROI, incremental targets that compound over time

🚫 Perfectionism paralysis (especially in doc processing)
✅ Instead of 100% accuracy upfront, think iterative & continuous improvement

🚫 Technology/Tools overload.
✅ Instead: Start with high-impact, low-complexity processes and scale gradually

🚫 Chasing vanity metrics.
✅ Instead: Focus on tangible business outcomes and continuous value tracking. 

Let's STOP sabotaging your automation projects! 🛑

Remember: Successful automation is a marathon, not a sprint.
Patience, strategy, and realistic expectations are your best allies in this journey.

What do you think? 

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

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

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

Agentic AI-led automation will change all that.

Here's why this time is different:

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

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

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

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

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

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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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Mind Blown! 🤯 This image perfectly captures the surprising truth about AI coding!

You buy tokens, hit 'Generate,' and pray for a bug-free jackpot... 
But there is only one winner in the end. 😏
Hint: It's not you 😀

How do we move beyond 'vibe coding' and truly harness AI's power for valuable outcomes?

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𝗧𝗿𝘂𝗲 𝘀𝘁𝗼𝗿𝘆: 𝗟𝗼𝗻𝗴 𝘁𝗶𝗺𝗲 𝗯𝗮𝗰𝗸, 𝗮 𝗯𝗼𝘁 𝘃𝗲𝗻𝗱𝗼𝗿 𝘀𝗼𝗹𝗱 𝘂𝘀 "𝗲𝗮𝘀𝘆" 𝗥𝗣𝗔 𝗯𝗼𝘁𝘀.  

We arranged an RPA training, which cost us around 5k per participant. 

I attended the training w/ the team. You know, it's easy, drag-and-drop, right? 

And then, I realized that the use cases are great, and the optics are awesome, but...

The reality behind "easy" RPA bot implementation:

 📅 Needs hours of intensive training 
🧠 Requires actual development skills past drag-and-drop 
⚙️ Complex use cases need intricate bot-building experience

🕰️ 10+ years later, honestly, it's still not truly "easy" for non-developers

Don't fall for the "anyone can do it" hype. Effective automation needs skilled professionals.

What do you think? 


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𝗣𝗹𝗼𝘁 𝘁𝘄𝗶𝘀𝘁: 𝗧𝗵𝗲 𝗵𝗮𝗿𝗱𝗲𝘀𝘁 𝗽𝗮𝗿𝘁 𝗼𝗳 𝗔𝗜 𝗶𝘀𝗻'𝘁 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗶𝗹𝗼𝘁𝘀. 𝗜𝘁'𝘀 𝗺𝗮𝗸𝗶𝗻𝗴 𝗶𝘁 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆. 🎯

AI pilots have a 90% success rate. 
AI production has a 90% stall rate. 
The gap is real.

Here's what catches everyone off guard:

📊 Scale explodes: 100s → 10,000s of documents
👥 Users multiply: 10 friendly testers → 1,000s with opinions
🏗️ Infrastructure evolves: Shared resources → Dedicated fortress
🔒 Security shifts: "What could go wrong?" → "Everything WILL go wrong"

Here's why this happens:

AI in production isn't just "pilot at scale."
It's a completely different beast requiring NEW approaches:

🔧 TECHNICAL: Robust monitoring, compliance, error handling, performance optimization

🎯 STRATEGIC: Governance frameworks, change management, dedicated teams, organizational alignment

Bottom line:
✅ Pilots prove the AI works
✅ Production proves your ORGANIZATION works with AI

Most companies nail the first part.
The second part? That's where the magic (or chaos) happens.

What's the biggest challenge you've seen getting AI to prod?

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𝗜'𝗺 𝗕𝗲𝘁𝘁𝗶𝗻𝗴 𝗠𝘆 𝗖𝗮𝗿𝗲𝗲𝗿 𝗼𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 (𝗛𝗲𝗿𝗲'𝘀 𝗪𝗵𝘆 𝗬𝗼𝘂 𝗦𝗵𝗼𝘂𝗹𝗱 𝗧𝗼𝗼)

The automation game is changing, and here's why & how I'm making my leap:

🌍 Market Shift:

RPA Market: $18B in 2024 → $73B by 2032 (Fortune Business Insights)
Agentic AI Market: $14B in 2025 → $141B by 2032 (MarketsandMarkets)

Traditional automation is slowing. 
Agentic AI is exploding.


🔄 My Transition Plan: From "Bots" to "Brains"

A Fundamental Transformation Across the Automation Lifecycle.

From: Building rigid bots
To: Creating intelligent agents

Here's my 6-stage pivot:

 1. PLANNING
❌ Old way: Map fixed processes
✅ New way: Frame strategic business problems

 2. DESIGN  
❌ Old way: Linear workflows
✅ New way: Multi-agent architectures

 3. DEVELOPMENT
❌ Old way: Script basic bots  
✅ New way: Engineer agents with the right context

 4. TESTING
❌ Old way: Test predictable rules
✅ New way: Validate emergent behaviors

 5. DEPLOYMENT
❌ Old way: Schedule task automations
✅ New way: Launch autonomous systems

 6. MAINTENANCE
❌ Old way: Fix broken bots
✅ New way: Continuous agent learning

 The Reality Check:

Risk: High (emerging field)
Upside: Exponentially higher

Disruption favors the prepared, not the comfortable.

What do you say? What's your 2025 skill transformation?

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88% of Senior executives are accelerating AI transformation, but 95% fail to deliver value.
I'm at #IBMPartner TechXchange this week connecting with developers & leaders, and the one topic dominating every conversation?

Agentic AI - autonomous agents that don't just answer questions, but reason, decide, and act.

The problem everyone's hitting?

The agents work perfectly in pilots.
Then they touch real data, real users, real scale → and break.

The issue isn't the agents themselves. It's the foundation.

Most organizations jump into agentic AI without the critical capabilities needed for production:

🔧 TECHNICAL FOUNDATION:
→ Rapid agent development frameworks
→ Enterprise-grade error handling
→ End-to-end orchestration
→ Controlled autonomy

🎯 OPERATIONAL FOUNDATION:
→ Business-grade reliability
→ Built-in governance from day one
→ Real-time observability and monitoring
→ Change management and organizational alignment

At IBM TechXchange, from the labs to the solution booths, I'm learning firsthand how production-ready orchestration transforms agents from pilots into deployed systems.

What Production-Ready Agentic AI Actually Needs:

✅ Rapid development of robust agents that reduce setup from hours to minutes
✅ Controlled autonomy with predefined workflows and conditional logic
✅ End-to-end orchestration that sequences multiple agents reliably
✅ Built-in governance from pre-deployment through production
✅ Real-time observability across distributed agent systems
✅ Business-grade reliability with audit trails and guardrails

That's exactly what IBM watsonx Orchestrate provides to scale AI productivity across the enterprise.

Agentic Workflows, Langflow Integration, Built-in Governance, Pre-built Agents - making production-ready enterprise agentic AI possible.

Check it out:
AI Advancements: https://lnkd.in/gTWk5bQy
watsonx Orchestrate: https://lnkd.in/gWBpX3pm
AI Productivity: https://lnkd.in/gizFdpEe

Early results from leaders using this:
→ 62% faster speed to action (Supply Chain)
→ 76% improved process efficiency
→ Resolution times are dropping while costs fall

The future of AI isn't more pilots.
It's production-ready Agentic AI at scale.

What's your biggest blocker in getting AI agents to production? 👇

#IBMTechXchange #IBMPartner #AI #Technology IBM

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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Standing on stage in São Paulo, I realized something:

I was the only person in the room who didn't speak Portuguese. 😀

Automation leaders from all over Brazil. 2 translators working non-stop. 
One American sharing a vision for the future of AI & Automation.
And you know what? It was absolutely incredible.

I keynoted BotCity's premier AI & Automation event with a talk on RPA to AI Agents.

Here's the roadmap I shared:
🔨 𝗕𝗨𝗜𝗟𝗗 on your existing RPA/DPA and Intelligent Automation foundations
🚀 𝗣𝗜𝗟𝗢𝗧 Agentic Process Automation (APA) & RAG projects now
🎯 𝗣𝗥𝗘𝗣𝗔𝗥𝗘 for fully Agentic AI systems

But here's what really matters—the 𝟰 𝗺𝗲𝗴𝗮-𝘁𝗿𝗲𝗻𝗱𝘀 reshaping how we work:

1️⃣ 𝗙𝗿𝗼𝗺 𝗥𝗶𝗴𝗶𝗱 → 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀
The era of "if this, then that" is ending. AI agents will learn, adapt, and make decisions in real-time w/ humans orchestrating them.

2️⃣ 𝗙𝗿𝗼𝗺 𝗜𝘀𝗼𝗹𝗮𝘁𝗲𝗱 𝗔𝗽𝗽𝘀 → 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗢𝗦
Forget disconnected tools. Imagine one intelligent layer where specialized agents collaborate across your entire tech ecosystem.

3️⃣ 𝗙𝗿𝗼𝗺 𝗛𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝘆 → 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻
The traditional org chart is becoming obsolete. The future is an "orchestration graph" - dynamic collaboration between humans and AI agents.

4️⃣ 𝗙𝗿𝗼𝗺 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 → 𝗩𝗮𝗹𝘂𝗲
We're done celebrating "hours worked" and "hours saved." The new metric? Value created. Output delivered.

Here's what blew me away: the Brazilian automation scene! Watch out, world. 🇧🇷

Brazil's automation leaders aren't waiting for the future - they're creating it. 
BotCity's Python-first orchestration platform is a perfect example of the innovation happening in Brazil right now.

The language barrier? Irrelevant. Great ideas transcend words.

Obrigado, BotCity and São Paulo! 🙏

Question: Which phase are you in—BUILD, PILOT, or PREPARE?

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

Everyone talks about it. 

Nobody really knows how to do it. 

Everyone thinks everyone else is doing it... 

So everyone does pilots to claim they are doing it 😊

Stop building Agentic AI pilots. Instead, add controlled agents with APA. 
https://hi.switchy.io/iF8U

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Is your vendor choice based on outdated information? It’s time for a change!

In an AI-first world, static quadrants mislead you on what's possible next.

Gartner realizes this and now has an "Emerging market quadrant":

⏳ Garner "aims" to update this new quadrant every 4 weeks

📊 This new quadrant plots vendors based on features and future potential.
 
🔄 Vendors are required to submit information more frequently to stay relevant.

🚀 They are starting with "generative AI" vendors. (which is almost everyone? 😀)
          

In terms of AI & Automation vendors: 

📈 Microsoft leads the quadrant, showcasing the most features and future potential.

🚀 UiPath & AA follow behind and almost have the future potential of Microsoft. 🤔

📊 BPM players like Appian & Pega are "Emerging leaders" as well.
      (Are they stronger in a converging world?) 

👀 Keep an eye on emerging players like Glean, Writer, etc. 


This new quadrant represents an evolving concept. A step in the right direction but tracking such a diverse range of vendors within one quadrant may not be easy. 

What are your thoughts on this approach? 

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🤖 𝟯 𝗪𝗮𝘆𝘀 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗶𝘀 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗥𝗣𝗔! 

Microsoft now has powerful RPA/APA/Agentic Automation options. 

Here’s the breakdown:

1️⃣ 𝗡𝗲𝘄! 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁’𝘀 "𝗔𝗜 𝗥𝗣𝗔" (𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗨𝘀𝗲):

Copilot Studio’s new"computer use" w/AI agents interacts directly with websites and desktop apps by navigating GUIs. 
(This is like Anthropic Computer use, Open AI Operator, etc - Check links in comments)

- Intelligent & Adaptive: Real-time adjustments ensure smooth workflows.
- User-Friendly: Natural language tasks, no coding, full visibility.
- Use Cases: Automates data entry, research, and more.


2️⃣ New! UiPath + Microsoft Integration:

The bidirectional integration between UiPath Studio and Copilot Studio enables developers to orchestrate automations across both platforms.

-  Cross-Platform Power: Embed UiPath automations in Copilot Studio or integrate Copilot agents into UiPath Studio.
-  Power Connector facilitates building actions and agent flows, incorporating UiPath automations in Copilot Studio.
-  Use Cases: Claims handling with Intelligent Document Processing, and escalation management


3️⃣ Existing - Microsoft Power Automate + PAD:

Power Automate, paired with Power Automate Desktop, continues to automate repetitive tasks across cloud and desktop environments.

-  Proven Automation: Cloud and desktop workflows integrated.
-  Enterprise-Ready: Secure, scalable solutions with Azure and Dynamics 365.
-  Use Cases: Automates legacy tasks, report generation, and data transfers.

This makes Microsoft a strong contender for RPA, Agentic Process Automation (APA) as well as Agentic Automation. 

Especially if you have Microsoft and/or Microsoft Copilot already in your organization. 

What do you think? 

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𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗶𝗱𝗲𝗮𝘀? 

💡 Here are 100 ideas.

Download more formats and in high resolution here:
https://hi.switchy.io/i4OR

Are you ready to integrate AI agents, and if so, where are you at? 

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I'm a Digital automation guy, and I am SICK of buzzwords

So I decided to translate them into simple terms:

- Hyperautomation: Everything Enterprise Digital (=Nothing 😃 ) 

- Low code: Proprietary development for a premium price 

- RPA: Rule-based automation using screens 

- Artificial Intelligence: linear regression & decision trees

- Intelligent Automation: Screen automation with basic linear regression 

- Centre of Excellence: Another corporate fancy team name

- Process Mining: Event log analysis for a premium 

- Task Mining: Screen data analysis

- Advanced Analytics: SQL

- Conversational AI: Chatbots with basic linear regression

- Generative AI: Mix and match, create random stuff 


Here is a fun exercise - Google each of these terms 

And you will get the hype definition for these 

Compare w/above and Enjoy 😃

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I asked ChatGPT to write a battle rap between RPA & AI.

It delivered! 🤣

Certainly! Here's the battle rap without any markdown formatting:

𝗥𝗣𝗔'𝘀 𝗩𝗲𝗿𝘀𝗲:
Yo, I'm RPA, the king of automation,
Taking over tasks without any hesitation,
I'm the future of work, no need for frustration,
Your processes streamlined, that's my foundation.

I don't need to think, I just execute,
Your manual tasks? Consider them moot,
I'm efficient, precise, no room for dispute,
AI, step aside, it's time for the truth.

𝗔𝗜'𝘀 𝗩𝗲𝗿𝘀𝗲:
Hold up, RPA, don't get too bold,
I'm AI, the future, never growing old,
I learn, I adapt, I break the mold,
Your static scripts? They're just plain cold.

I'm the brain, the thinker, the real game-changer,
Your automation's cool, but I'm no stranger,
To innovation, creation, I'm no mere arranger,
I'm the real deal, RPA, you're in danger.

𝗥𝗣𝗔'𝘀 𝗥𝗲𝗯𝘂𝘁𝘁𝗮𝗹:
Danger? Ha! I'm the one in control,
Your learning's slow, I'm on a roll,
I'm the workforce's ultimate goal,
Your complex models? They take a toll.

I'm here and now, no need to wait,
I'm changing businesses at an incredible rate,
You talk big game, but it's never too late,
To see that I'm great, and you just imitate.

𝗔𝗜'𝘀 𝗥𝗲𝗯𝘂𝘁𝘁𝗮𝗹:
Imitate? Nah, I innovate and create,
Your tasks are simple, mine are first-rate,
I'm the future of tech, no room for debate,
Your automation's fine, but I'm truly great.

I'm deep learning, neural nets, and so much more,
Your robotic tasks are frankly a bore,
I'm the one who's really going to soar,
RPA, it's time to show you the door.

Listen to the audio, created by Josh McCarver  
https://lnkd.in/gC5Eq9At

𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻:
Two technologies, both unique in their way,
RPA and AI, shaping the future every day,
Who won this battle? It's hard to say,
They both have roles in this modern-day ballet.

What do you say? 😃

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