Thomas Helfrich

Win More Marketing Clients, Get Verified | Build trust faster, overcome skeptical buyers, & win more clients | Get Verified, Risk-Free | 1 Mil YouTube Subs / Top 10% Global Podcast

I deliver on the promise of Ai marketing to generate leads and build your brand. In fact, I guarantee it.

I bring together AI, automation, and human expertise to drive measurable growth for clients ranging from billion-dollar engineering firms to $100M SaaS companies to high-performing solopreneurs. Book a time with me here: Calendly.com/instantlyrelevant

My team and I provide end-to-end growth solutions:
• Social media management (LinkedIn, Meta, and more)
• Content production (posts, podcasts, YouTube, newsletters)
• Direct outreach (DMs, connections, network growth)
• Thought leadership + AI-SEO human-written articles
• 1:1 coaching to sharpen offers, systems, and sales flows
• Integrating Ai into you business to achieve measurable results

In the past five years, we’ve helped secure over $500M in contracts, doubled revenue from $40M → $80M in just two years, and scaled small businesses from $1M → $3M ARR. Every engagement is customized—and all of it comes for less than the cost of an intern.

Before launching Instantly Relevant, I spent 20 years in consulting (including KPMG) and led intelligent automation and AI strategy implementations at Eli Lilly and Stanley Black & Decker.

🎙️ In 2024, I launched the Cut The Tie podcast + YouTube channel, which grew into a top 2.5% globally ranked show with 1M+ subscribers—proof that the same growth systems we use for clients work at scale.

👉 Want to see how your lead gen stacks up? Take my 3-minute assessment: assessment.instantlyrelevant.com

Chief Executive Officer

Let's double your LinkedIn revenue with the Instantly Relevant sales and marketing system.

Raj Goodman Anand

Chief Product

Founder of Goodman Lantern (UK) | CEO at AI-First Mindset (UK) | President of EO BM | AI Speaker | AI Workshops

I grew up believing there was one safe path: study hard, get a good degree, find a stable job, follow the plan. Getting into IIT Delhi felt like proof I could do hard things. But I soon learned that being “qualified” isn’t the same as being “aligned.” I wanted to research computer science, not general engineering, so I chose a different road.In my early twenties I moved to the UK and started my first business at 22. Around the same time I began my PhD thesis in artificial intelligence. That was two decades ago. Since then I’ve watched AI fail, evolve, and restart many times. The lesson that stayed with me was simple: technology only creates value when complex ideas are translated into practical, commercially viable strategies.I later built and exited Spicy Corporation, my first full business cycle, and at 28 became Global Innovation Director—and then CEO for the UK and Asia—at the 700-person Actualize Group, the youngest and only non-Spanish CEO.Over the years I’ve launched and scaled multiple ventures, including GoPinLeads, an AI-powered lead generation platform that helped organizations drive measurable growth and efficiency. I’m also the founder and CEO of Goodman Lantern, a B2B content marketing agency serving innovative businesses across five continents.I remain close to the frontier of AI as an investor in OpenAI, xAI, and Anthropic, and as author of The AI Era: Adapting &amp; Thriving. My work has been recognized with BusinessWeek Europe’s Young Entrepreneur (2007) and Revolution Magazine’s 50 Most Influential People in Digital (2009). I’ve spoken at the Foreign &amp; Commonwealth Office, British Library, and London School of Economics, and been featured in The Telegraph, The Guardian, BusinessWeek, and The Observer.Today I work with leaders of traditional businesses who want results, not hype. That’s why we built AIFM OS — the AI-First Mindset Operating System: a practical framework that integrates AI into real workflows to save time, improve decisions, and reduce manual dependency.Through AI-First Mindset®, we support organisations across the Indian sub-continent, South East Asia, East Africa, and the Middle East with:✅ AI operating model design✅ Workflow transformation✅ Leadership alignment✅ Practical AI adoption✅ Implementation sprintsMy focus is simple: helping traditional businesses become AI-first in practice.🔑 Join us: https://aifirstmindset.ai/agentic-operating-system/

Founder & CEO

Goodman Lantern is an award-winning content marketing agency specialising in content writing, strategy, design, motion-graphic videos, and SEO services for industries such as technology, telecommunications, and financial services.

We're a fast-growing business that serves clients across five continents. We help our clients establish themselves as thought leaders in their industries with storytelling that inspires and converts. Our content allows them to build trust with their audiences to scale their businesses.

We've made it our Big Hairy Audacious Goal (BHAG) to create consistent, reliable, and fair income opportunities for women in marketing and tech. And along with our clients, we're already making great strides: 90% of our team members are women. Our ambition, however, is to ultimately make a difference on a global scale.

Are you keen to be a part of our success story? Our highly creative team members are experts at developing valuable content that speaks to target customers' pain points. So let's collaborate and create content that will grow your business.

🎤 Super proud to announce: I'm speaking at the EO Global AI Summit #4!

After last year's tremendous success with thousands of participants from around the globe, I'm thrilled to be part of this year's lineup on February 26, 2026 – joining world-class AI experts and forward-thinking entrepreneurs for the biggest EO event on "Transforming to an AI-First Company".

My session: Your AI Agent Stack: Practical Tools Leaders Use in Real Work

What to expect at the summit:

✨10 industry-leading speakers sharing real-world insights and battle-tested strategies
✨Concrete use cases and best practices for AI integration at scale
✨Live voting for the EO AI Award recognizing the most innovative AI applications (back by popular demand!)
✨Networking opportunities with 20,000+ EO members worldwide
✨ Actionable takeaways you can implement immediately in your business

From AI agents in daily operations and scaling prototypes to production to leadership in the AI era and securing your AI future – we're covering everything you need to know to position your company for success in an AI-first world.

This isn't just another AI conference with buzzwords and hype. This is about practical implementation, real results, and honest conversations about what works (and what doesn't) when transforming your business with AI.

📅 February 26, 2026 | 4:00 PM – 10:00 PM CET
📍 Online – Free registration for EO members, Accelerators, key executives, and life partners

EO Global AI Summit #4 – Full Speaker Lineup

• Jamie Pujara (Interim Executive Director | EO) – Opening Words
• Victor Knapp (Founder Media.Monks & CEO Monks EMEA) – AI Impact in the Service Business: From Disruption to Opportunity
• Marek Janetzke (CEO & Founder | Lucid Labs) – From AI Assistants to Agents: Lessons, Do's & Don'ts on the Road to an AI-First Company
• Jaap van Zomeren (Head of Trade & Master Data & Reporting | Operations ASML) – Applications Mature Like Fish, Data Matures Like Wine
• Stefanie Chiras (Senior Vice President, AI Innovation Hub | Red Hat) – From AI prototype to scale: Open innovation strategies for global entrepreneurs
• Sven Smit (Chairman of the McKinsey Global Institute and Senior Partner) – Robots, Agents and Us: How will we lead?
• Keegan Da Costa (AI Development Strategist | Too Many Robots) – Why AI Projects Fail and How to Fix It
• Winnie Hart (CEO & Founder | TwinEngine) – Stand Out, Stay Human: How to Build Your Digital Twin Before One Is Built for You
• Jacob Bank (Founder & CEO | Relay app) – Build Your Own Team of AI Agents
• Erik Brieva (CEO at Dedomena & Chairman Bcombinator) – Securing the AI Future: Synthetic Data, Federated Intelligence, and the Rise of Secure Data Spaces

Looking forward to sharing my experience and learning from this incredible community! Huge thanks to the hosts Robert van de Zwart, Dr. Markus Wuebben, and Luis I. Cortes for putting together this incredible event!

Who else will be joining? See you there! 🚀

Helping organizations build AI operating systems | Founder, AI-First Mindset®

EO AI Summit

The past few weeks, I have been running AI workshops with business leaders across geographies.

This week though, I’ll be attending the *AI Impact Summit* hosted by the Ministry of Electronics and Information Technology (MeitY), Government of India, at Bharat Mandapam, New Delhi.

I am deeply humbled and grateful for the invitation. It tells me how quickly AI has moved from internal capability discussions to sovereign priority.

The Summit will bring together policymakers, researchers, technology companies, startups, investors and members of civil society. I will be curious to see how the world's largest democracy with an enviable record for providing seamless digital services to the world's largest population, is moving to measurable impact in AI.

The AI ecosystem is becoming truly interconnected now. It is a place where tool builders cannot operate without infrastructure, operators cannot scale without governance clarity, investors cannot underwrite without technical depth and policymakers cannot regulate in isolation from innovation velocity.

Over the past few years, my work has taken me across many of these conversations. From implementation inside operating teams to strategic discussions with founders and capital allocators. Each conversation has informed the next. 

Now here we are. 

As I head into the Summit, I am less interested in announcements and more interested in signals to see how AI genuinely improving decision quality. Where is it strengthening institutional capacity and where are we building durable advantage beyond pilots.

If you are attending, I would value the opportunity to exchange perspectives.

And if you’re building, investing in or shaping AI policy, what does “impact” mean in your world right now?

#AIImpactSummit #ArtificialIntelligence #AIGovernance #AIStartups #IndiaTech

What’s changed in the last year isn’t technology. It’s investor patience.

In 2025, AI budgets moved easily. In 2026, every line item is challenged. The question is direct: where is the margin impact.

Tolerance for delayed returns is gone. If AI doesn’t improve cost, throughput or downtime quickly, it becomes a visible expense. Companies now expect measurable results within two quarters. Adoption metrics no longer protect programs. If plants miss output targets, AI initiatives get shut down. 

Agentic AI increases the pressure. These systems now execute decisions inside workflows. I’ve seen them influence scheduling and approvals that affect throughput. In this environment, weak governance shows up as production variance.

Ownership is shifting as well. AI delivers value only when tied to how work actually runs across ERP and MES systems. Projects led in isolation rarely produce results. Business leaders must anchor AI in real operations.

Two patterns fail repeatedly.

First, running AI as scattered pilots. Manufacturing depends on consistency. Fragmented efforts increase variance and strain legacy systems. Clear central ownership reduces friction and speeds correction.

Second, automating before fixing data. Errors compound at scale. In thin-margin operations, bad data turns automation into waste.

If you’re starting now, map one workflow where decisions stall between shifts, such as maintenance prioritisation. Use AI where rules are stable and data is reliable. Avoid areas where quality or safety still depend on human judgment.

#AI2026 #AILeadership #EnterpriseAI #AgenticAI #AIOperatingModel #AIROI #DataStrategy #FutureOfWork #DigitalExecution #BusinessTransformation #AIGovernance #AIForCEOs

AI regulation in the U.S. is entering a phase where state laws are active while federal preemption remains unresolved, which increases execution risk for operating businesses. This matters because AI already influences pricing, hiring, customer communication and clinical decisions, all of which carry direct legal exposure. Waiting for regulatory clarity will deploy AI under overlapping obligations that conflict with actual workflows. 

State AI laws are becoming prescriptive because they regulate disclosures and internal controls rather than abstract principles. This is happening because states are regulating where visible harm intersects with deployed systems. That directly affects how models are trained and monitored inside revenue-critical operations.
This means: AI systems without documented controls will slow execution.

Federal efforts to override state rules are accelerating because fragmented regulation increases cost and litigation risk at scale. This is happening because national competitiveness now depends on AI throughput rather than experimentation.
This means: rigid compliance strategies will increase rework and delay returns.

To make good on AI investments companies need to operate AI safely instead of reacting to regulation.

#AIStrategy #Cybersecurity #DigitalTransformation #EnterpriseAI #TechLeadership #AIGovernance #ZeroTrust #CIOInsights #SecurityArchitecture #AIRisk

AI Search Engine Optimization (AISEO)

The Real Constraint on AI Performance Is Leadership Execution

AI programs stall when they are treated as missing technology instead of weak execution. Many organizations already operate with slow decisions and unclear ownership, which increases operating risk. AI amplifies this risk because it depends on shared data and clear authority to deliver results. When this is ignored, failure spreads from individual projects into everyday operations.

What is changing is that AI efforts now run across teams and data boundaries that were previously separate. This is happening because models depend on connected workflows rather than isolated functions. The impact appears as slow rollouts and wasted investment because existing friction becomes visible.

This means: AI exposes leadership weaknesses faster than traditional change programs.

The capability to deliver AI already exists inside organizations because success depends on decision ownership and coordination rather than technical fluency. Problems appear when specialists are hired while authority gaps remain unresolved, which blocks outcomes.

AI adoption breaks down when incentives and data ownership do not align. Models then reinforce silos instead of improving execution. Delivery also fails when governance is added after deployment rather than built into how decisions are made.

One practical way to begin is to map a recurring decision where delays affect cost or revenue and document who owns the inputs and the final call. Do not apply AI if ownership or data access is unclear, because automation will accelerate conflict rather than resolve it.

#AILeadership #EnterpriseAI #OperationalExcellence #AIFirstMindset #LeadershipExecution #AIAdoption #BusinessOutcomes #DigitalOperations #DecisionMaking #TransformationReality #CXOStrategy #FutureOfWork

Stop learning new skills for AI

AI reasoning models don’t fail because they are slow. They fail because they are judged by the wrong standards

According to an MIT report, enterprise workflows are expected to provide instant responses because older software was built to return simple answers. That expectation clashes with reasoning models that need time to evaluate complex situations. When leaders ignore this gap, correct outputs are mistaken for errors and useful systems are shut down before they deliver value.

Reasoning models pause because they break problems down before responding. This is happening because they are now used in areas like risk and planning where careful evaluation matters. The business impact shifts from how fast an answer appears to whether the decision can be trusted. Speed without accuracy weakens confidence.

This means: organizations that allow time for reasoning make better decisions at a manageable cost.

Traditional benchmarks fail because they reward fast responses instead of correct ones. This pushes reasoning models to produce shallow answers that look efficient but lead to poor outcomes.

Deployments also fail when AI is added to workflows that do not allow time to think. Proper analysis is then seen as delay rather than value.

One practical way to start is to choose a decision where people already wait for judgment and place AI reasoning there. Avoid using reasoning models in real-time interactions where delays directly affect revenue.

#AILeadership #EnterpriseAI #AIOperations #DecisionIntelligence #AIExecution #BusinessAI #AIWorkflows #AIReality #OperationalAI #CXOInsights #AIAdoption #AIFirstMindset

AI adoption in 2026 will punish shallow deployment

AI matters now because spending is rising faster than measurable returns across most enterprises. Many firms have committed seven-figure budgets while productivity metrics remain flat because workflows were never rebuilt. This gap creates operational drag and workforce confusion at scale. 

The outlook is that AI capability is accelerating while organizational readiness is lagging because tooling evolves faster than data. This disconnect creates short-term productivity loss before long-term gains materialize in firms that redesign workflows deliberately.

This means: AI becomes a performance multiplier only after operational friction is removed.

AI increases output volume while trust in outputs declines because monitoring and accountability are underinvested. This raises review effort and latency across knowledge work.

This means: unmanaged AI increases cost per decision instead of reducing it.

1. Legacy approaches fail because leaders deploy AI as a layer on top of broken processes, which preserves inefficiency at machine speed. Execution improves only when AI is embedded inside decision ownership and workflow control.

2. Legacy approaches fail because talent strategies chase AI skills without redefining roles, which leaves accountability gaps across operations. Results improve only when human judgment is explicitly designed into AI governance.

Begin by mapping one revenue-critical workflow and removing manual review steps before applying AI, and do not use AI where decision accountability cannot be clearly assigned.

#AILeadership #AIFirstMindset #EnterpriseAI #BusinessExecution #OperationalExcellence #FutureOfWork #AIGovernance #DigitalStrategy #CEOPerspective #AIReality #WorkflowDesign #BusinessTransformation

Data culture is now the limiting factor in AI performance

AI programs risk getting stalled because the data behind them isn’t supporting real decisions.

This is because the data is unreliable or poorly governed. Only a small share of companies see meaningful financial impact because their data cannot be trusted at scale. Companies that ignore this keep funding systems that raise cost without improving results.

For a clear outlook with tangible business outcomes, AI needs to move from experimentation to embedded decision support because boards now expect measurable return. Models need to be used closer to revenue and risk, where data problems show up immediately.

This means: AI success is limited by data reliability, not by model strength.

Another shift is underway. Responsibility for data is moving from IT teams to business owners because decisions happen inside workflows and not reports. This is happening because delays and manual fixes block scale in live operations.

This means: data quality becomes a leadership responsibility tied directly to outcomes.

Traditional approaches fail because companies place AI on top of fragmented data, which spreads errors instead of reducing cost. Execution improves only when data standards match how work is actually done.

Governance also fails when it exists only as policy. Models amplify weak data faster than teams can correct it. Clear operational ownership produces usable data because incentives are linked to decision impact.

One practical way to begin is to map a single revenue or risk workflow and identify where people override data today. Apply AI only where data is stable and decisions repeat. Keep AI out of areas that rely on judgment.

#AILeadership #DataStrategy #EnterpriseAI #BusinessOutcomes #OperationalExcellence #AIFirst #DataGovernance #DecisionMaking #DigitalTransformation #CEOPerspective #AIAdoption #EnterpriseData

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Thomas Helfrich

Raj Goodman Anand

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taplio

The Battle

Thomas Helfrich

Raj Goodman Anand

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Engagements

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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);}Raj Goodman Anand's LinkedIn Performance

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