About Our AI Advisory Practice | AI Advisory Practice

Our Story

Founded by operators who built businesses on AI

AI Advisory Practice was founded by Fredrik Filipsson and Morten Andersen, two successful SMB entrepreneurs who used AI to build their own businesses to multimillion dollar scale. They made the build versus buy calls with their own capital, fixed the messy data nobody else would touch, and shipped models that had to earn their keep. That operator experience is the foundation of everything this practice does.

The catalyst was simple: watching large firms send MBA analysts with six months of AI experience to advise Fortune 500 companies on technology decisions worth hundreds of millions of dollars. The decks were beautiful. The strategies were theoretical. Almost nothing shipped.

We started this practice on a contrarian premise: enterprise AI advisory should be delivered by people who have actually put AI into production and lived with the result. Not people who have studied deployments. Not people certified in AI frameworks. Founders who have run the profit and loss on an AI bet, extended by a bench of senior practitioners with C-suite AI programme leadership on enterprise AI initiatives.

We have advised 200+ enterprises across financial services, manufacturing, healthcare, retail, insurance, and government. We have deployed more than 500 AI models into production environments. We measure our success in production deployments, not strategy documents.

We remain completely independent and vendor-neutral. We evaluate Azure, AWS, GCP, OpenAI, Anthropic, Google, and every major AI platform on objective merit. We do not accept referral fees from technology vendors. We do not have preferred partnerships that create conflicts of interest. Our only interest is delivering the right outcome for your organization.

If you want transformation theater, we are not the right fit. If you want AI that ships, drives measurable ROI, and scales across your organization, let's talk.

Track Record

$4.2B+

Documented client value created

94%

Of engagements reach production deployment

14wks

Average time to first production model

0

Vendor referral fees accepted

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Meet the Founders

Two entrepreneurs who built on AI

Both founders are successful SMB entrepreneurs who used AI to build their own businesses to multimillion dollar scale. Every engagement is led by one of them personally.

See the Full Team →

FF

Fredrik Filipsson

Co-Founder · AI Strategy

A successful SMB entrepreneur who used AI to build his own business to multimillion dollar scale, Fredrik leads AI strategy, readiness assessment, and vendor selection. He advises from direct experience, not theory.

LinkedIn → Full Profile →

MA

Morten Andersen

Co-Founder · AI Implementation

A successful SMB entrepreneur who used AI to grow his own business to multimillion dollar scale, Morten leads AI implementation, MLOps, and data strategy. He moves AI systems from proof of concept into reliable production.

LinkedIn → Full Profile →

Our Differentiation

Why practitioners outperform consultants

Every major AI strategy firm will tell you they have senior advisors. Here is what that actually means in practice.

01

Senior practitioners, not senior consultants

At large advisory firms, a senior engagement manager typically has 8 to 10 years of consulting experience, most of it in frameworks and client management. At this practice, senior means 15+ years of hands-on engineering, architecture, or AI program delivery. Our advisors have written the production code, managed the data pipelines, and debugged the inference infrastructure. They know where enterprise AI fails because they have been in those situations.

02

We measure success in production deployments

Most advisory firms measure success in deliverables: strategy documents, roadmaps, assessment reports. We measure success in production deployments, model uptime, inference latency, and business outcomes. 94% of our engagements result in a model deployed to production. That number is the only one that matters to us.

03

Genuine vendor neutrality

We do not have preferred vendor partnerships. We do not accept referral fees from Microsoft, Google, AWS, OpenAI, Anthropic, or any AI platform. Our evaluation framework is objective: technical capability, enterprise fit, total cost of ownership, and vendor risk. When we recommend a platform, it is because it is the right choice for your organization, not because it improves our margin.

04

We tell you when AI is not the right answer

One of the most valuable things we do is advise clients NOT to build the AI system they are planning. In a third of our initial assessments, the right recommendation is to fix the data infrastructure, the governance model, or the organizational readiness before investing in AI. This advice costs us short-term revenue. We give it anyway because it is correct, and because clients who follow it come back with better projects that actually succeed.

05

Former leaders at organizations you know

Our advisory team has led enterprise AI programs at scale. We bring the institutional knowledge of how enterprise AI is built at scale, combined with the independence that lets us apply that knowledge honestly. When a Fortune 500 CIO asks whether they should build, buy, or partner on a particular AI capability, we have been on both sides of that decision.

How We Work

Our operating principles

Not a manifesto. The actual rules we follow on every engagement.

Outcomes over deliverables

We are evaluated on production deployments, not PowerPoint decks. Every engagement defines success in business outcomes before work begins. If we cannot measure it, we do not promise it.

Speed to production

Our average time from engagement start to first production model is 14 weeks. We do this by starting with the smallest viable AI component, validating in production, and scaling from proven results. We do not spend six months in discovery.

Independent counsel only

We refuse vendor referral arrangements. We refuse preferred platform relationships. When we evaluate your options, the only variable is what is best for your organization. That independence is worth more than any commission structure.

Honesty about limitations

We tell clients when AI is not ready, when their data is not good enough, or when the problem is better solved without machine learning. This costs us short-term work. It builds the long-term trust that defines this practice.

Knowledge transfer built in

We do not build AI systems your team cannot maintain. Every engagement includes structured knowledge transfer so your internal team can own the model, the infrastructure, and the governance. The goal is independence, not dependency on us.

Enterprise scale, not startup theory

We have deployed AI in organizations with 5,000 to 200,000 employees, legacy infrastructure, complex governance requirements, and global regulatory environments. We know the difference between what works in a demo and what survives contact with enterprise reality.

Our Background

Where we come from

The experience behind our advisory practice spans the full spectrum of enterprise AI, from research labs to boardrooms.

Meet the Full Team →

Large-scale AI engineeringSenior engineering and product leadership on enterprise AI and cloud AI platforms. Hands-on experience with large language model deployment, ML infrastructure at scale, and enterprise retrieval and vector systems.

Enterprise AI platform strategyProgram leadership across enterprise AI platforms, assistant rollouts, and productivity AI integration. Deep knowledge of enterprise licensing, AI governance in regulated industries, and secure cloud AI deployment patterns.

AI transformation advisorySenior engagement leadership advising large financial services and manufacturing enterprises on AI transformation programs. Experience running multiple concurrent engagements from strategy through production delivery.

AI governance and CoE designDevelopment of enterprise AI assessment frameworks used across many client engagements. Specialization in AI Center of Excellence design, AI governance frameworks, and regulated industry deployments.

Enterprise data architectureCombined 60+ years of enterprise data platform experience across the team. Data strategy, data governance, and data engineering for AI workloads at Fortune 500 scale. Experience with Databricks, Snowflake, dbt, and all major cloud data platforms.

MLOps and production AI infrastructureHands-on MLOps experience across Kubernetes-based inference infrastructure, model monitoring, A/B testing frameworks, and model drift detection at scale. Our advisors have managed production AI systems serving 40,000+ concurrent users.

Industries Served

Enterprise AI across every major sector

We do not have a single industry specialization because enterprise AI problems are fundamentally similar across sectors. The data challenges, governance requirements, and organizational change management patterns repeat.

Financial Services Manufacturing Healthcare Insurance Retail & CPG Energy & Utilities Technology Government Telecommunications Logistics

Case Study

Fortune 500 manufacturer: 94% defect detection

Computer vision AI deployed across 12 production lines. $4.2M annual savings in year one.

Case Study

Top 20 bank: credit risk AI in 16 weeks

ML model reducing false positive rates by 67%. Deployed across 3 business units.

Case Study

Healthcare system: GenAI clinical documentation

40,000 clinicians on AI-assisted documentation. 2.1 hours saved per clinician per day.