Pipeline Upgrade®

Chief Operating Officer

PropUp Corporate

Marketing Director

NFON

Senior Software Engineer

Verivox GmbH

Verado

Chief Technology Officer

Langwiser

Founder

https://www.linkedin.com/redir/suspicious-page?url=tankards%2eru

I help financial leaders generate leads with AI on LinkedIn | LinkedIn Marketing Specialist

Growing an IT services business should not feel like guesswork. 

Most providers still rely on referrals or wait for inbound leads which leaves their pipeline empty and competitors ahead.

The truth is:
→ Buyers are already choosing providers before you reach them
→ Renewal cycles, migrations and security projects are happening without you in the room
→ Every missed lead is lost revenue your competitors collect

At Pipeline Upgrade, we replace uncertainty with a pay-on-results outbound growth system designed for IT firms:
✅ Verified, qualified leads and decision-maker meetings.
✅ Precision targeting around buying triggers such as contract renewals
✅ Predictable, scalable pipeline from 10 to 100 opportunities per month

Imagine starting each week with a calendar and CRM already filled with IT buyers ready to talk. 

That is what our clients experience: 
→ consistent growth
→ stronger authority
→ freedom from the referral trap

Ready to take control of your pipeline?

→ Click below 👇

Tim Sergeev

Most business owners build systems for perfect people.

But perfect people don't exist.

Here's the truth:

Your team has three types of people.

→ Task-driven who follow every step
→ People-driven who skip the checklist
→ Big-picture players who ignore details

And all of them need to follow the same process.

That's the real challenge in operations.

You're not building systems for robots.
You're building them for humans.

So instead of perfect documentation, I focus on this:

→ Build constraint into the system
→ Make the right path the easiest path
→ Design for the worst case

The person who ignores details?
↳ They still hit validation points.

The person who skips steps?
↳ The system catches them.

The person who follows everything?
↳ They move faster.

You don't hire people for your systems.
You hire them for the job.

The system just makes sure they all deliver it the same way.

So here's the question for you.

Are you building systems for perfect people or designing constraint that works for everyone?

---

Tim Sergeev
COO @ Pipeline Upgrade
I make business run smoothly

Predictable Pipeline for UK IT Providers | 2,500+ Meetings Delivered | £5M+ Client Revenue | Pay-Per-Result Model | COO @ Pipeline Upgrade

Most business owners confuse operations with delivery.

They think it's the same but it's not even close.

Operations is the railway. Delivery is the train.

Without tracks, the train goes nowhere.
Without delivery, the tracks are empty.

In operations you:
→ Define systems
→ Outline processes
→ Set sequences and validations

In delivery you:
→ Execute the work
→ Follow the systems
→ Produce the output

Operations ensures everyone works predictably.

With the right operations in place:
1️⃣ Teams know what to do
2️⃣ Output becomes predictable
3️⃣ Chaos disappears

Operations is invisible but essential. 
It’s the backbone of every scalable business.

So here's the question for you.

Do you have the right tracks laid or are you hoping the train will figure it out on its own?

---

Tim Sergeev
COO @ Pipeline Upgrade
I make business run smoothly

Most people think they have resilience.  

Until they actually need to use it.

Here is what usually happens:

→ 7 days with no results  
Motivation drops

→ 14 days with no visible progress  
Doubt kicks in

→ 30 days still at zero  
They quit completely

But nothing meaningful works on that timeline.

- Skills compound slowly  
- Pipelines warm up gradually  
- Systems need cycles to settle  
- Trust builds long before it pays off

The winners are not the most talented.  
They are the ones who survive the quiet stretch.

The stretch where:
1. Nothing moves  
2. No one notices  
3. You question yourself every single day

However if you can keep going when the scoreboard shows zero  
You will eventually pass everyone who stopped early.

Stick through the quiet part and everything changes.
Always remember: most never make it that far.

- Tim

P.S. Wishing you the resilience to achieve your goals.

🚨 Calling all software engineers

The way people find information is changing.

Less search
More AI
And the gap is widening every month.

That's why I'm excited about what my friend Domien Van Dammei s building.

His project Visiblie just secured seed funding and they are looking for a Product Engineer to join the team.

If you want to enter the fast-growing AI market and help shape the future of it, send him a DM.

Or tag someone who should see this.

More info is in the first link in the comments 👇

Most sales teams fail long before the outreach even starts.

Not because they're bad but because their targets are impossible.

A lot of business owners set KPIs that look ambitious on paper but feel hopeless in real life.

Here is what actually happens:

→ SDR sees an unreachable number
→ Stress goes up
→ Motivation drops 
→ Output collapses
→ Pipeline dies

Targets are not motivational if they feel like fiction.

They need to sit in the sweet spot hard enough to stretch the person but close enough to feel achievable.

↗ Too high and the team gives up.
↘ Too low and they never grow.

In our business we avoid this problem by using data, not wishes.

We look at:  
→ Historical conversion rates
→ Average reply numbers
→ Meeting ratios across industries
→ Volume benchmarks from previous SDRs

When we set a KPI we know it has been achieved by others so the new SDR sees it as a challenge, not a punishment.

That small shift changes everything.

1️⃣ Realistic targets build confidence.
2️⃣ Confidence creates consistency.
3️⃣ Consistency produces results.

Now tell me this: are your KPIs helping your team win or making them fail before they start?

---

Pipeline Upgrade
Predictable pipeline for UK IT providers with zero upfront risk.

I hate inbounds.

Even though I run an outbound growth agency ))

Not because people come to me 
but because most of them come the wrong way.

Cold.  
Generic.  
Copy pasted.  
Zero thought.

And every time I see one of those messages, I feel the same thing  
this person has no idea who I am, what I do or what problem they're trying to solve.

That's why pitching doesn't work anymore.  
❌ People are tired.  
❌ They're overwhelmed.  
❌ They're drowning in noise.

Nobody gets a cold email today and thinks  
"Finally, this is the service I've been waiting for"  
It just doesn't happen.

If you want conversations that actually lead somewhere, you need to go deeper.

Understand their world  
→ their pressure  
→ their blockers  
→ their desired future  
→ and the gap between the two.

Only then you can honestly ask  
↳ can I help this person move from where they are to where they want to be?

If the answer is yes, you have a real opportunity.  
If the answer is no, there is no point forcing it.

This is what works today  
1. real curiosity  
2. real empathy  
3. real conversations.

Nothing in between works nowadays!

Anyway, enough about me, let's talk about you.

How do you separate real interest from people who are just kicking tyres?

---

I build pipelines for UK IT providers with zero upfront risk.

This quote hit me hard.

Because I've seen both sides.

---

I've seen systems with 30+ tools.

I've seen systems with 5.

The 5-tool system always wins.

---

Here's what actually happens with complex infrastructure:

Most people think:
→ More tools = more capability
→ More integrations = better automation
→ More complexity = more professional

Reality:
→ More tools = more data loss
→ More integrations = more failure points
→ More complexity = more overhead

---

I spent 10+ years as a software engineer.

I've seen codebases where 90% could be deleted and the app would work better.

The same principle applies to business systems.

The math is simple:

30 tools = 30 places data gets lost, mutated, or corrupted
↳ No single source of truth
↳ Constant firefighting
↳ 50% overhead minimum

5 tools = Clear data flow
↳ One source of truth
↳ Predictable outcomes
↳ Actual scalability

---

Every connection is a point of failure.

Every integration needs maintenance.

Every tool costs money and attention.

Most businesses are paying thousands per month for complexity they don't need.

---

Here's what I tell people:

Your goal isn't the most sophisticated setup.

Your goal is the minimum viable infrastructure that actually works.

Ask yourself:
→ Which tools are redundant?
→ Where is data getting lost right now?
→ What could be consolidated?

Strip it down to what matters.

---

I know it's tempting to share your 30-tool stack.

But it takes more courage to say "we run everything on 5 tools and it works."

Simplicity is hard.

Complexity is easy.

---

How many tools are you currently using to run your business?

PS. Curious to hear if anyone else has simplified their stack and seen the difference.

Most business owners confuse documentation with operations.

They write SOPs.
Store them in folders.
Tell the team to follow them.

And wonder why everything still breaks.

Here's the truth:

SOPs tell people what to do.
But they don't stop people doing it wrong.

People want shortcuts.
People get tired.
People assume.

Not because they're bad but because they're human.

So instead of writing more procedures and adding quality control layers, I focus on this:

→ Build systems that reject wrong inputs
→ Add validation before the next step can start
→ Make the correct path the only path

Not through rules but through structure.

Operations isn't about writing procedures.

It's about building railways, not roadmaps.

The railway doesn't ask the train to stay on track.
It makes staying on track the only option.

That's how we approach every system we build.

So here's the question for you.

Do you rely on documentation or do you build constraint into your systems?

---

Tim Sergeev
COO @ Pipeline Upgrade
I make business run smoothly

Most people think operations is just automations.

- Move data from A to B.
- Trigger a workflow.
- Send a notification.

But real operations sit on three layers.
And if even one of them is weak, the whole system collapses.

Here is how it actually works.

→ Layer 1: Data
This is the foundation.
Data must be consistent, structured and clean.
If the data model is messy, everything becomes unstable.

→ Layer 2: Process
This is where most people stop.
Automations, workflows, logic, rules.
But this layer only works if the data layer is solid.
Otherwise you simply automate chaos faster.

→ Layer 3: Display
This is what the team sees.
Dashboards, tables, views, permissions, insights.
If the first two layers are wrong, this one becomes noise instead of clarity.

Think of it like this:
1️⃣ Data at the centre.
2️⃣ Process wrapped around it.
3️⃣ Display wrapped around everything.

Get all three right and the business runs like an engine.
Get one wrong and the entire thing shakes.

Do you agree with this?

---

Tim Sergeev
COO Pipeline Upgrade
I make business run smoothly

AIMS (AI Made Simple)

We help companies build real AI capabilities through bespoke trainings and learning journeys – tailored for business teams. Focus areas:
- AI Literacy & Strategic Prompting
- AI Assistants, Automations & AI Agents
- Microsoft Copilot Enablement
- No-Code AI Builder Journeys for Sales, Marketing, Ops 

Creator of the AI Revenue Accelerator: a 6-week program helping small business owners implement plug-and-play AI systems for lead generation, marketing, and sales.

Bots & People

Freelance AI Trainer

Women of AI

AI Speaker | Available for Speaking Engagements

As an active speaker on AI (AI in Business, Digital Transformation, The Future of Work, practical AI workflows), I regularly share insights at conferences, corporate events, and expert panels — helping audiences understand how to turn AI into real business impact.

Speaker

KI Bundesverband

German AI Association is Germany’s largest AI network, representing the interests of over 400 AI companies in politics and business.

At AIMS, we’re proud to be part of this influential network. Our membership enables direct exchange with leading AI experts, keeps us up to date on the latest industry developments, and strengthens our role as a trusted AI education & consulting partner.

Registered Member of German AI Association 

Kantar

- Led marketing analytics projects for Fortune 500 clients that consistently delivered 10%+ return on marketing investment.
- Responsible for renewals and acquisition of new clients for Kantar’s Marketing Effectiveness and Analytics solutions.

Drove thought leadership and client education:
- Led webinars focused on Marketing Effectiveness for 500+ marketing professionals
- Gave keynote speech and moderated panel discussions on Artificial Intelligence at Kantar Insights Day
- Delivered guest lectures at the Wiesbaden Business School combining marketing strategy and AI applications

Senior Analytical Consultant

Consultant

Karer Consulting

Led digital transformation initiatives for clients:
- Developed data-driven decision-making frameworks
- Designed automation strategies for operational efficiency
- Achieved 40% efficiency gains through strategic tech implementation

Built client education programs:
- Trained teams on new digital tools and methodologies
- Facilitated change management workshops
- Established measurement frameworks for digital transformation ROI

Business Analyst

Lufthansa Systems

Intern 

AI-driven strategies for your business. I helped Fortune 500 companies to scale their marketing with data-driven insights.

For years, I helped Fortune 500 companies optimize strategy with data. But behind the dashboards and models, I saw the same problem everywhere: brilliant people buried in manual work, overwhelmed by complexity, and disconnected from impact.

That’s what pushed me to pivot — from analytical consulting to empowering businesses with AI that’s practical and human-centered.

Through my company AIMS (AI Made Simple), I now help forward-thinking teams turn AI from a buzzword into real, everyday results.

You can work with us in two ways:

1. Bespoke AI Training for Companies
We design custom learning journeys that empower your teams to solve real problems with AI — from prompting for better insights, to building custom AI assistants, setting up automated workflows, or deploying agents that handle repetitive work. Ideal for business like Marketing, Sales, Ops, L&D and beyond — no technical background needed.

2. AI Revenue Accelerator (for SMBs)
A 6-week program for small business owners who want to use AI to scale sales, marketing, and operations with plug-and-play AI systems. Zero fluff. No coding. Just proven results and step-by-step guidance.

Whether I’m on stage speaking at industry events or building AI workflows with your team — my mission is simple: Make AI accessible, practical, and profitable.

Valeriya Pilkevich

You know, I’m not the “share every shiny update” kinda person. But these 3 earned a permanent spot in my workflow ⬎

The result?
Hours saved. Better output. Less mental friction.

Let’s break it down:

1. ChatGPT Atlas (aka my new default browser)

I must admit, it took me a moment to stop jumping back to Chrome for the classic copy-paste routine. But once you switch your brain to the new workflow, the time savings are almost immediate:

→ It helps me directly on any page: LinkedIn, YouTube, landing pages... you name it
 → The in-line writing help feels like Grammarly… but with a brain 
 → Lost tabs? I just type “find the speaker application I opened a few days ago” - and it opens the tab I thought was gone forever
 → Agent Mode: I use it sometimes to fill in applications with my details. Still having some concerns about this one though...

2. Claude Skills (aka your portable specialists)

Think of them as custom “onboarding packets” that make Claude behave exactly how you need. Two of my favorite use cases are: 

→ Copywriting: I taught Claude how I write on LinkedIn and it follows flawlessly.

Guide here: https://lnkd.in/eGpfJX88 

→ Notion brain extension:
If your entire life and work lives in Notion (think, client notes, lead magnets, projects, content) - this one is essential. I use it to:
1. Synthesize ideas from multiple pages and enrich them with Claude’s research
2. Search across notes and past projects to help me draft LinkedIn posts and push content directly into my content library in Notion

Setup guide: https://lnkd.in/eAmK_zuD

3. Google AI Studio (“Build” feature) - WHY is no one talking about this??
I recently trained business teams on Gemini in Workspace + Google AI Studio. At first, I hesitated to include “vibe coding” because the word coding makes many non-techies mentally shut down.

But… within 15 minutes they were building apps like:
→ proposal generators
→ knowledge bots
→ pitch builders
→ ad creators

Zero code. Zero overwhelm. Massive empowerment.

Here’s the recipe generator I built in under 5 minutes: https://lnkd.in/eUdy5gdc

P.S. Which recent AI launches became integral part of your workflow? Let's inspire each other.

Upskilling teams in AI | Certified AI Trainer | Keynote Speaker

How much money do I actually spend on AI subscriptions each month? And… what’s the real ROI? ⬎

Yesterday I audited all my AI subscriptions.

Not because I'm a financial wizard (I'm clearly not).

But because after months of testing tools for trainings and running my own AI workflows, I wanted to see: what's actually pulling its weight?

So here's the breakdown - what I pay, what I use, and what ROI I'm actually getting:

→ ChatGPT (€17/month): 10 hours saved weekly mostly on brainstorming, research and as a strategy copilot. Though when I asked ChatGPT itself how much time it saves me based on my usage patterns, it claimed 20-25 hours weekly. You choose who to believe 😁 

→ Claude (€17/month): 2 hours weekly, I’m heavily using new Claude skills (esp. for copywriting and Notion workflows)

→ Gemini in Google Workspace (€13.60/month - needed for client trainings): 1 hour weekly, NotebookLM is my secret weapon for synthesizing research papers and prepping trainings. 

→ Microsoft (€10/month): not a Microsoft ecosystem person, need it for client trainings.

→ Zapier (€0): 5 hours weekly with Zapier agents that repurpose conversations into content and research prospects before discovery calls.

→ Make (€9/month): 3 hours weekly on business automations of very manual tasks.

→ Jotform agents (€0): 2 hours weekly with one Gmail agent that auto-labels and drafts replies in my style.

→ Descript (€20/month): 3 hours weekly on video editing (I do record a lot of training videos for companies). The new Underlord AI agent streamlines the whole process. 

→ Fathom (€0) - AI notetaker: 1 hour weekly on meeting notes and follow-ups (plus all the things you can do with these transcripts, from content to proposal creation to sales coaching).

→ Gamma (€10/month): 4 hours weekly creating or adjusting training slides for different clients.

Total monthly spend: €96.60.
Total hours saved monthly: ~124 hours

ROI: ~1,3 hours saved for every €1 I spend
And since my hourly rate is far above €1… the math is almost unfair.

But here's what matters more than the time savings: I'm not just doing MORE work. I'm buying back capacity to work ON the business instead of IN it.

P.S. Do you audit your AI subscriptions? What AI tool brings you the highest ROI? 

P.P.S. I didn't include HubSpot, La Growth Machine, Notion, Calendly, Butter, Tally, Mailerlite or Slack - these are daily business tools but either not AI-focused or I'm not using AI features.

I was so happy to have found my passion for teaching AI
until I realized it came with a baggage ⬎

Non-stop. Video. Editing.

Let me explain:

Most of my trainings are virtual (live) but there still around 50% that are recorded:

→ companies that want recordings for their LMS
→ pre-work or short explainer videos for students
→ our AI Revenue Accelerator (now 100% self-paced)

Back in May, I recorded and edited 30+ videos for AI Revenue Accelerator within just a few weeks (each 20+ minutes long). By the end of it, I couldn’t even look at a timeline or open another recording app. 

That is until recently I discovered the Descript’s update Underlord - a smart AI agent that helps you edit videos with simple, conversational commands.

And suddenly, editing didn’t feel like punishment anymore.

Here’s my 2 favorite ways to use it (and 3 others where it excels):

→ Record directly in Descript and let Underlord clean everything i.e. “Clean audio, remove background noise, cut filler words, add captions”

→ Turn slide decks into full, narrated videos i.e. “Take this PDF of my deck and turn it into a video about my company strategy.”

→ Transform long-form content (i.e. webinar or podcast recordings) into engaging social media clips i.e. “Make a TikTok-style explainer with subtitles, music, and a hook in the first 5 seconds.”

→ Add B-roll using stock libraries i.e. “Fill in all the media placeholders with stock video, based on my camera recording.”

→ Use templates to find more pre-built use cases: explainer, social clip, slides-to-video, and more.

If you want to give Descript a try, check it out here: https://lnkd.in/eqZBbVrr

P.S. What would you ask Underlord to do first? Clean audio? Pick the best cuts? Turn a deck into video?

#DescriptPartner #ad

"Where are all the women?"
Many of you asked me last time I posted about AI Meetup in Bangkok.

Yesterday I discussed this with James Devonport, co-founder of the largest UK’s AI conference and community Create With (with 50:50 male and female speakers btw). 

And we might have found an answer (keep reading).

Yesterday’s event was all about practical AI solutions - the kind non-techies can build with no-code tools:
→ Jake Hissitt emphasized human-centered AI adoption and reminded us that AI is an amplifier (read: if your content is weak, AI & Automation will only make it worse)
→ Boriwat Chanruang showed us how he alone identified and implemented 20+ AI use cases inside a 600-employee company - saving tens of thousands of euros.
→ Then Nattaphorn Natteerapong gave us the security reality check: "Don’t let AI tools code your business logic (if you don’t understand the code)"

(Unfortunately I was stuck in traffic 1h instead of planned 15 minutes - so yes, I missed the first two talks. And no, I’m not taking a motorbike next time 😅)

So, back to the “why so few women?” question.

After enough AI & tech events, I’m convinced:

It’s not a pipeline problem.
It’s a visibility problem.

Women are in tech. Women are in AI. Women are building workflows, leading transformations, and pushing innovation.

But they’re often:
 → not the ones representing the company 
 → not the ones sharing their work publicly
 → not the ones taking the mic
And in Asia, this gap feels even wider.

To make my point stronger: last week I attended a completely different event - a panel “Future of Talent & Al in HR”. 

The discussion went deep into the psychology, ethics, and safety of AI: how it shapes organizational decision-making today and what it means for the future workforce. We talked about the real blockers to adoption, the guardrails companies need, and how to use AI in a way that’s safe, human-centric, and performance-driven.

A completely different crowd - HR, L&D, leadership.
Industries where women dominate.

Still guess who was the audience? Mostly men.
(Except for two brilliant women on stage: Mila Devenport and January Jidapa P.)

So here’s my takeaway: we need more women
 → showing up
 → sharing their wins
 → being the face of AI, not the hidden engine behind it
 → stepping into the conversations that shape the future of work

P.S. What’s your take - is it visibility, culture, or something else entirely?

P.P.S. This time I took a more representative photo with James and Paphasanun - taken right after she told us this winter is extremely cold (25 °C at night and 32 °C during the day) we couldn't stop laughing 😂

No company today is truly agent-ready. And culture is the biggest bottleneck ⬎

Last week I came across a brilliant 3-part podcast series with Nufar Gaspar.

Her company does something unique: they employ voice agents to interview employees and leadership across enterprises, then analyse thousands of responses and map where agents + AI can actually create value.

The result?

Some of the clearest, most honest insights I’ve heard on the agentic future.

Here are the takeaways that stuck with me (and match what I see inside companies every week):

1. Transparent top-down communication is key

Most companies now have AI playbooks, guidelines, or “responsible AI” PDFs somewhere on the intranet. But few clearly communicate how AI will impact roles, responsibilities, or workflows.

Even fewer answer basic questions like:
→ If an agent saves an employee 5 hours/week… what happens next?
→ Are people expected to deliver more? Learn new skills?
→ How will this be measured?
→ Are leaders actually using the same tools they expect teams to adopt?

From my experience I can say for sure: when I have directors and VPs in the training room, building workflows and agents together with business units, I know this AI learning journey won't be forgotten 2 days after its ending. 

2. AI Champions & AI Builders: the missing middle

Early adopters in the company are the cornerstones of AI adoption. Yet, in many companies I work with, they aren't given the permissions to connect all the right tools to actually move the needle. 

The companies that win invest in two roles:

AI Champions (internal) → bridge business needs, identify opportunities, and act as translators
AI Builders (often new hires) → design workflows, configure agents, integrate systems and make things real.

3. The skill gap is bigger than we think

In most companies I worked with, I teach teams prompting, basics of M365 or tools like ChatGPT and Gemini. In few companies it’s about building workflows, vibe coding apps or agents with no-code tools.

Yet, how many companies train on agent management skills?
→ Data hygiene
→ Troubleshooting 
→ Safety & oversight
→ Workflow orchestration
→ Delegation & escalation rules

AI literacy is not the same as agent literacy. And almost no company today is teaching the second.

I also liked Nufar’s take on managing agent use cases like a well-balanced investment portfolio (i.e. balancing between solutions to build internally vs. buy, and onboarding highly-specialized vertical agents vs. horizontal agents like company knowledge retriever).

Really worth a listen if you’re curious about where the agentic future is heading (links to all the episodes in the comments).

P.S. If you were designing an agent-readiness program, which skills would make your top three?

P.P.S. If you’re thinking about how to get your teams ready for the agentic future - from skills to culture to practical workflows - happy to chat.

Different continent. Different companies. Same AI adoption blockers ⬎

Last week I attended an exclusive AI event hosted by Dexian Asia Pacific – and honestly, it felt like walking a red carpet at Crown Plaza Bangkok.

But the real spotlight?

The conversations happening between sessions.

Because whether in Frankfurt or Bangkok… leaders are wrestling with the exact same 3 blockers: 
→ Scattered data 
→ Unclear use cases 
→ Cultural resistance

Different markets. Same patterns.

Let me share a few standout moments:


1️⃣ “Data is still 80% of the consulting work”

Everyone wants “advanced agents”.

But here’s the truth: for real AI transformation – whether that's rich insights, proprietary models, or agentic AI – you need data foundation first.

This sounded very familiar: when I worked at Kantar, we needed historical weekly data (marketing, sales, competitive landscape) for marketing mix models.

Do you know how often that data existed in one place?

Almost never.

It was scattered across teams, agencies, tools… or simply didn’t exist.

That's why data literacy must come before AI literacy – especially as we move toward agentic future.


2️⃣ Many companies are stuck in “Optimize” mode

Cutting costs.
Cleaning up workflows.
Automating the obvious.

Important? Yes.
Transformational? No.

The real value lies in:
→ Redesigning entire workflows
→ Using AI to create new revenue streams
→ Innovating business models instead of polishing old ones

You don't get there by dumping GenAI tools into an organization and hoping for magic.


3️⃣ Culture is the real bottleneck

Speakers reiterated something I see in every corporate training:
Expectations shape success.

Teams hear "AI boosts productivity" and immediately wonder:
What happens to the time we save? What happens to us?

AI-first isn’t a tool strategy.
It’s a people strategy.

Your employees storing the data in Excel tables today (and yes, this came up multiple times – many companies are still running their business in Excel) won't magically become AI-fluent tomorrow.

The gap is real. And it requires intentional bridging.

--

And my personal highlight: an inisghtful conversation with Ashu Bhatia (ex-Accenture US, author of "Value Creation"). I also convinced him to be a guest on my new podcast (more on that soon 😉).

P.S. What's blocking AI transformation in your organization – is it data, culture, or strategy?

Are you buying the drill to make more holes? 
Or to fix that crooked shelf? 

Many people I talk to don't know where to start with AI.
So they start by buying tools.

My advise: "Always start with a problem".

Ask yourself:
“What are my biggest bottlenecks? Time drainers? Things I’d happily outsource if I could.”

Here were mine:
→ Living in my inbox: organising and replying to emails 
→ Jumping on discovery calls unprepared (or spending hell lot of time researching somebody)
→ Complaining about no ideas & no time to post on Linkedin… while sitting on hours of webinars, podcasts, and client calls full of gold

So I built 3 agents:
→ One to research prospects & companies before the calls
→ One to repurpose my conversations into content 
→ One to organize my emails and draft replies

And if you’re thinking: “Of course she did - she’s an AI expert.”
I can guarantee you can replicate this entire set up in under 30 minutes. 

How? I will show you this Friday during the Lunch&Learn session organized by wonderful James Cameron from BigRock Intelligence.

[Updated] Recording here: https://lnkd.in/eWraeyns

3 AI Agents I actually use - and you can too!

"Will AI make us dumber?" I addressed this question yesterday at Fraport's Speakers Corner ⬎

A few weeks ago, Arthur Michael Seidel reached out to chat about AI adoption. 

We instantly clicked - he's built something special at Fraport AG: a platform for dialogue around the future of work.

When he invited me to run a session, I wanted to genuinely shift perspectives.

So I took the three biggest concerns I hear everywhere:
→ "AI makes less sharp" 
→ "AI will take our jobs" 
→ "We need to completely reskill"

And flipped them: 
→ AI can enhance our thinking 
→ AI can increase our job satisfaction 
→ AI makes uniquely human skills more relevant

So, does AI make us dumber or stronger?

The research is clear - it depends on how we use it.
→ Passive AI use (just consuming answers) = weaker thinking 
→ Active AI use (as a sparring partner) = enhanced judgment

I showed NotebookLM as an example - not just for summarization, but as a learning companion that explains, questions, and adapts to your learning style.

On job loss:
AI is transforming jobs - it makes us do more meaningful and strategic work.

World Economic Forum projects 90 million jobs will disappear by 2030, but 170 million new ones will emerge.
→ Less data entry and admin
→ More problem-solving and strategic thinking

I did a live Power Automate x Copilot demo showing how one simple workflow can free up mental space for work that actually matters.

And that third concern about needing to reskill in STEM?

The most valuable skills through 2030 aren't coding - they're analytical thinking, resilience, creativity, and the ability to work with AI systems as collaborators.

Think agent management skills: communication, delegation, orchestration.

The reality?
Teams that combine human judgment with AI fluency don't just get faster - they get better.

Big thanks to Arthur and the Fraport AG team for creating spaces where these conversations happen.

P.S. If you're building long-term AI capability in your organisation and want a strategic partner on that journey, let’s talk.

P.P.S. When it comes to the latest research on behavioral science x AI, metacognition, Human-AI collaboration Fendi Tsim is my trusted source. Highly recommend to follow him.

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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);}Tim Sergeev's LinkedIn Performance

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