Florian Decludt

Demand Generation Manager @ Clutch

Growth Marketer

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

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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😱 𝗜 𝗹𝗼𝘀𝘁 𝟴𝟰% 𝗼𝗻 𝗨𝗶𝗣𝗮𝘁𝗵, 𝗯𝘂𝘁 𝗵𝗲𝗿𝗲'𝘀 𝘄𝗵𝘆 𝗜 𝗰𝗮𝗻'𝘁 𝗹𝗼𝗼𝗸 𝗮𝘄𝗮𝘆... 

10 years ago, I started following UiPath's journey & even wrote a book about it. Here's what's happening:

✅ What UiPath did right:
• Built an amazing community, especially the free version
• Consistently innovated - the best in the space
• Showed better financial growth than others

🚨 But here's the painful truth nobody's talking about...
They have an identity crisis.
- Be a UI-based automation (RPA) company?  
- Become a hyper-automation company (RPA vs Everything company)? 
- Be an Agentic process automation company or Agentic automation company (RPA vs AI company)?

💡 But a better time to solve RPA / UI automation with AI has never been. 
Just ask Rabbit MQ, Adept AI, Anthropic, etc.   

🌐 The market desperately needs a Universal API through UI
• Most business apps have NO APIs 
• AI agents need universal APIs 
• Current RPA is brittle

🏆 The gold rush has begun:
• Anthropic released "computer use"
• Google has Project Mariner
• OpenAI added "Operator"

🤖 Yet they're all starting from scratch.

Meanwhile, UiPath already has: 
✅ UI automation expertise 
✅ Security Framework 
✅ Orchestration layer
✅ Enterprise trust 

💥 They are best placed to find the UI - Solution/Path w/AI. ("UI -Path")
The first to crack it will be the next trillion-dollar $ AI company. 

I wish UiPath would get back to its roots, crack this, and let me break even on my investment. 😅 and yes becomes a true "AI company".

What do you all think? 


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𝟱 𝘁𝗵𝗶𝗻𝗴𝘀 𝘁𝗵𝗮𝘁 𝗱𝗲𝘀𝘁𝗿𝗼𝘆 𝘁𝗵𝗲 "𝗥𝗣𝗔 𝗶𝘀 𝗱𝗲𝗮𝗱" 𝗮𝗿𝗴𝘂𝗺𝗲𝗻𝘁. 🤖

Spoiler: RPA is more alive than ever.  Here's what the naysayers are missing... 👇

🎭 Doesn’t hallucinate: RPA is structured, logical, and systematic. This makes it ideal for critical, repetitive tasks.

🏗️ Global API: It's the API for all places where API is not there for many reasons (legacy, extra effort, etc).

💰 Cost Effective: Still the best for repetitive tasks that do not need dynamic reasoning. No token consumption costs as in agentic systems. 

⚡ Energy Efficiency: RPA consumes much less energy than complex LLM-based AI systems.  

🎯 It still delivers in-year benefits. Something Agentic AI still has to prove. 


RPA isn't just surviving the AI boom – it's THRIVING.

✅ It's the silent workhorse powering the future of automation.

✅ RPA handles the boring stuff and enables AI for cognitive tasks.

✅ Like a primer on RPA? Here is a complete guide: https://lnkd.in/ggSF37Dd

What do you think? 

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

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

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

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

🔹 Chief AI Officer: Leads AI strategy and oversight.

🔹 Head of AI: Directs AI initiatives and teams.

🔹 AI Architect: Designs scalable AI system architecture.

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

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

🔹 AI Ethicist: Addresses ethical implications of AI.

🔹 Model Manager: Oversees the AI model lifecycle.

🔹 Model Validator: Verifies AI model accuracy and reliability.

🔹 AI Developer: Builds and implements Gen AI & Agentic systems.

🔹 Model/ML Engineer: Develops and deploys machine learning models.

🔹 AI Application Developer: Integrates AI into business applications.

🔹 AI App Dev Platform Architect: Designs AI development platform infrastructure.

🔹 AI Cybersecurity Analyst: Mitigates AI-specific cybersecurity risks.

🔹 AI Cybersecurity Researcher: Researches AI system vulnerabilities.

🔹 AI Redteam Engineer: Tests AI systems for weaknesses.

🔹 Prompt Engineer: Optimizes prompts for AI models.

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

🔹 Decision Engineer: Designs AI-driven decision systems.

🔹 AI Process Automation Engineer: Automates processes using AI.

🔹 Data Scientist: Analyzes data for AI insights.

🔹 Data Engineer: Manages data pipelines for AI.

🔹 Analytics Engineer: Builds AI-driven analytics solutions.

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

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

What are the new roles you are seeing emerge? 

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

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

The architecture is already here:

🎯 𝗔𝗣𝗣𝗟𝗜𝗖𝗔𝗧𝗜𝗢𝗡 𝗟𝗔𝗬𝗘𝗥

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

🤖 𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗟𝗔𝗬𝗘𝗥

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

⚙️ 𝗜𝗡𝗙𝗥𝗔𝗦𝗧𝗥𝗨𝗖𝗧𝗨𝗥𝗘 𝗟𝗔𝗬𝗘𝗥

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

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

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

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

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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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🚀 𝗔𝗜 𝗶𝘀 𝘀𝗺𝗮𝗿𝘁𝗲𝗿 𝘁𝗵𝗮𝗻 𝗲𝘃𝗲𝗿, 𝗯𝘂𝘁 𝗶𝘁'𝘀 𝘀𝘁𝗶𝗹𝗹 𝗮𝗹𝗹 𝗮𝗯𝗼𝘂𝘁 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴: 𝗗𝗮𝘁𝗮.


🔍 In the Machine Learning days:
Bad data = Bad models.

🧠 In the Generative AI era:
Bad data = Bad outputs (Code, Marketing material, DNA sequences).

🤖 In the Agentic AI world:
Bad data = Bad actions across apps, platforms & databases.


Bad data = AI becomes a burden, not a differentiator.

Garbage in 💩💩, lots of garbage out 💩💩💩💩💩 😀

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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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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𝗔𝗳𝘁𝗲𝗿 𝟯𝟬𝟬+ 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀, 𝗢𝗽𝗲𝗻𝗔𝗜 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗯𝗿𝘂𝘁𝗮𝗹:
Most AI agents fail before they even launch.

Not because of the technology.
Not because of the budget.
Not because of talent.

They fail because teams skip the foundation.

Here's what actually separates production-ready agents from expensive experiments:

𝗧𝗵𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 (𝗚𝗲𝘁 𝘁𝗵𝗶𝘀 𝘄𝗿𝗼𝗻𝗴, 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗲𝗹𝘀𝗲 𝗰𝗿𝘂𝗺𝗯𝗹𝗲𝘀):

→ Core Components working in harmony (Model + Tools + Instructions)
→ Smart Orchestration (Single vs Multi-agent patterns)
→ Layered Guardrails (Smart systems need defense)

The 6-Step Implementation Roadmap:
1️⃣ Define Role & Goal - What does it do? For whom? Skip this = expensive paperweight
2️⃣ Structure Input/Output - Use schemas (JSON), not messy text. Clean data in = reliable results out
3️⃣ Instruct & Tune - System prompts + consistent persona. Your instructions matter more than you think
4️⃣ Add Reasoning & Tools - Think-Act-Observe framework. This is where the Agentic magic happens
5️⃣ Implement Memory - Context retention strategy. Agents without memory are less useful
6️⃣ Format Output - Human AND machine readable. Usability = adoption

The reality in 2025:
- 120% of leadership wants AI agents NOW
- 100% of IT teams fear security nightmares
- 90% have ZERO implementation roadmap

The gap between excitement and execution is killing initiatives across every industry.

Which step is your team struggling with most?

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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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Now we have many hammers... 

But the approach remains the same 😃

What do you think? 

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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 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?

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♻️ Repost if this resonated

𝗗𝗲𝗹𝗼𝗶𝘁𝘁𝗲'𝘀 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺 𝗳𝗼𝗿 𝗜𝗧 𝗦𝘂𝗽𝗽𝗼𝗿𝘁.

It uses specialized AI agents & people in a workflow to autonomously resolve tickets.

Here is how it works:  

🔍 Incident Creation: Business users submit support tickets via the IT portal. 

🔄 Workflow Trigger: The software incident support workflow is initiated to resolve.

🧐 Incident Classification Agent: AI classifies the ticket and assigns it to the right agent. 

🔧 Incident Analysis Agent: Analyzes past articles, SOP's, etc for similar fixes and solutions. 

📈 Solution Validation/Escalation: Agent validates solution w/KB else escalates to Human L3. 

👥 Human-in-the-Loop: Humans validate and fully implement the resolution before finalizing.

 ✅ Resolution Completion & Update: The resolution agent updates the status in the system for users. 

📊 Workflow Monitoring and Metrics: The system tracks interactions and reports metrics for improvements.


And here's why it stands out: 

💡 Automated Resolution: Reduces human intervention to essential. 

📚 Knowledge Base Integration: Quickly locates possible resolutions. 

👨‍💻 Human Oversight: Human judgment ensures reliability and accuracy. 

🔍 Agent Specialization and Levels: Optimizes workload and response times. 

📈 Continuous Monitoring and Improvement: Gathers metrics for ongoing enhancement.


This multi-agent system demonstrates the fusion of AI and human expertise for IT support. 

What do you think? 

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I just analyzed McKinsey's 2025 AI report. One number jumped out:
6%

That's the percentage of companies actually winning with AI.
Not experimenting. Not piloting. WINNING.

While 90% of companies use AI, only one-third move beyond pilots.
But that elite 6%? They're playing a completely different game.

Here's their playbook (and it's eerily similar to what we learned from RPA/IA):

What they DON'T do:
Bolt AI onto existing processes
Chase efficiency metrics alone
Run endless PoCs with no real budget
Let AI sit alongside legacy systems

What they DO:
Rebuild workflows from the ground up (3x more likely than average)
Set growth + innovation objectives (not just cost reduction)
Deploy centralized governance teams
Invest in real infrastructure that supports AI at scale
Move fast with iterative development cycles

The biggest lesson?
These high performers measure how fast their AI agents can transform work.
Everyone else just measures how much faster the old work gets done.

History doesn't repeat, but it sure rhymes.
The best practices for AI transformation aren't new—we learned them through intelligent automation.

The question is: will we apply them this time?

Check out the complete report 👇🏽

McKinsey State of AI in 2025

🚨 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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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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𝗧𝗵𝗲 𝟮𝟬𝟮𝟱 𝗥𝗣𝗔 𝗤𝘂𝗮𝗱𝗿𝗮𝗻𝘁 𝗳𝗿𝗼𝗺 𝗚𝗮𝗿𝘁𝗻𝗲𝗿. 

Mostly the same with some movement:

- Same top 4 leaders - UiPath, AA, Msft & Blue Prism
- Microsoft's completeness of vision is best (always was?) 
- Pega and Appian have moved from visionaries to challengers 
- EvoluteIQ has entered (congratulations) while Nintex has gone out. 

Every "new" RPA Quadrant seems the same as the past. 
Now that we have a BOAT Magic quadrant, which includes everything, would it replace this? 

What are your thoughts? 

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#AI #innovation #technology #automation

2025 RPA Magic Quadrant

𝗨𝗻𝗽𝗼𝗽𝘂𝗹𝗮𝗿 𝗼𝗽𝗶𝗻𝗶𝗼𝗻: 𝗧𝗢𝗢𝗡 𝗶𝘀 𝗼𝘃𝗲𝗿𝗵𝘆𝗽𝗲𝗱.

My feed has been full of posts claiming it's the next big thing for LLM token optimization.
Yes, TOON uses 154 characters versus JSON's 412 for the same data. Impressive on paper.

But here's what nobody's talking about:

❌ Token savings are typically single-digit percentages
❌ CSV achieves comparable results for flat data
❌ You still need JSON for actual integrations
❌ Another syntax to learn, and debug 
❌ Zero structured-output support

It's essentially a CSV with a line count.

Look, I'm all for optimization. But when you're building enterprise automation, you need:

✅ Readability (for your team to maintain)
✅ Validation and schema support
✅ Works with existing systems
✅ Measurable ROI

TOON delivers none of these while saving not many tokens.

The real optimization opportunities are elsewhere, not your data format.

Here's my advice: Measure before you migrate. JSON isn't the enemy - complexity is.

What do you say? 

Image source: programmerhumor .io

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