Every advisor on this team has spent 15 or more years deploying production AI systems at enterprise scale. Not studying them. Not advising on frameworks. Building, debugging, and shipping them across some of the world's most demanding technology environments.
AI Advisory Practice was founded in 2022 by Fredrik Filipsson and Morten Andersen — two practitioners who have been building AI-powered businesses since AI became practically deployable. They founded this practice to give enterprises access to the same quality of AI thinking they apply to their own ventures.
Fredrik co-founded AI Advisory Practice to bring genuine practitioner-level AI guidance to enterprises. He has been applying AI in commercial contexts since 2022, building multiple businesses with AI as a core operational advantage. His advisory focus is AI strategy, use-case prioritisation, and vendor selection.
He advises from direct experience making the same build versus buy decisions, facing the same data quality constraints, and navigating the same organisational resistance that enterprise AI leaders encounter daily. His writing takes a direct, contrarian stance against AI hype and focuses on what actually works in production.
Morten co-founded AI Advisory Practice with Fredrik Filipsson, bringing direct production AI experience to enterprise advisory. He has been building AI-powered businesses since 2022, giving him first-hand knowledge of what enterprise AI implementation actually requires beyond what frameworks and research papers describe.
His advisory focus covers AI implementation architecture, MLOps platform selection, data strategy, and the technical governance structures that allow AI programmes to operate reliably at scale. He bridges the gap between strategic intent and technical execution.
At this practice, senior partners personally lead every client engagement. There are no handoffs to junior team members. The advisor you meet is the advisor who does the work.
Marcus led AI product and engineering programs at Google Cloud for nine years, including the development of Vertex AI enterprise deployment frameworks adopted by more than 300 global organizations. He built and managed machine learning infrastructure serving 40,000 concurrent users, oversaw the deployment of large language model systems into regulated financial services environments, and designed the AI governance frameworks that became Google Cloud's enterprise standard.
Before Google, Marcus spent eight years at enterprise software and financial services organizations deploying predictive analytics and recommendation systems. He has a deep technical background in MLOps, distributed ML infrastructure, and production model monitoring at scale.
At this practice, Marcus leads AI strategy engagements for Fortune 500 clients across financial services, manufacturing, and healthcare. His engagements have resulted in an average time to first production deployment of 12 weeks and documented ROI of 280% to 420%.
Sarah spent 11 years at Microsoft in senior roles across Azure AI, the enterprise Copilot program, and Microsoft 365 AI integration. She led the design and deployment of enterprise Copilot programs for more than 50 Fortune 500 clients, built the adoption measurement frameworks used across Microsoft's enterprise AI customer success organization, and was a principal contributor to Microsoft's responsible AI implementation guidelines for regulated industries.
Her data strategy work covers the full lifecycle from data platform modernization to AI-ready data infrastructure. She has architected enterprise data platforms on Azure Synapse, Databricks, and Fabric that underpin production AI systems processing more than two billion daily events.
Sarah leads generative AI and data strategy engagements at this practice. Her specialty is designing Generative AI programs that achieve measurable adoption across 10,000 or more employees, with an average of 2.1 hours saved per employee per day in documented deployments.
James spent 12 years at McKinsey as a senior engagement manager and later a principal in the Global AI Practice, where he led AI transformation programs for Fortune 100 financial services, insurance, and healthcare organizations. He designed AI governance frameworks for organizations operating under EU AI Act, DORA, and US federal AI regulatory requirements, and managed concurrent AI programs with combined budgets exceeding $800M.
His risk management experience spans model risk, algorithmic bias assessment, AI audit frameworks, and board-level AI governance reporting. He developed McKinsey's proprietary AI risk taxonomy, which has since been adapted by more than 20 global financial institutions.
At this practice, James leads AI governance and risk engagements for regulated industries. He works with CROs, Chief Compliance Officers, and boards to design governance frameworks that enable AI innovation without creating regulatory exposure. His frameworks have passed regulatory examination at three major central banks.
Priya led Accenture's AI Center of Excellence practice for six years, designing and standing up internal AI CoE organizations for 25 Fortune 500 enterprises across manufacturing, retail, and telecommunications. She built the CoE operating model frameworks that became Accenture's global standard, covering team structure, model governance, technology stack selection, and talent development pipelines.
Her implementation specialty is the end-to-end deployment of production AI systems, from data engineering and feature engineering through model training, evaluation, MLOps infrastructure, and organizational change management. She has deployed AI systems across Kubernetes, Azure, AWS, and GCP environments, with particular depth in hybrid cloud architectures common in regulated industries.
At this practice, Priya leads AI implementation and AI CoE engagements. Her average time from engagement start to first production model is 11 weeks. She has built AI CoEs that have become self-sustaining within 18 months, reducing ongoing advisory dependency for clients while delivering 3x to 5x increases in internal AI deployment velocity.
Senior partners are supported by a bench of associate advisors with deep specialization in AI-adjacent domains. All bring a minimum of 10 years of enterprise experience.
Our staffing model is designed around one principle: your organization deserves the senior practitioner on every call, not just the first one.
Start with our free AI Readiness Assessment to understand where your organization stands, then connect with the partner best suited to your challenge.