Maija Tanner

About the Expert

Maija Tanner is Head of IBM, currently heading Data Platform, Data & AI Technology Ecosystem Partnerships and Growth for the Northern, Central, and Eastern Europe market. Based in Finland, she brings over 20 years of experience across supply chain management, sales leadership, strategic accounts, and IT delivery at both local and European levels. In recent years, Maija has played a key role in building and scaling IBM’s ecosystem partnerships, working closely with system integrators, ISVs, distributors, and strategic technology partners. Her focus is on helping enterprises adopt data and AI technologies responsibly, securely, and at scale, turning innovation into measurable business value.

What does it really take to scale AI in large organizations?

While many organizations are actively experimenting with AI and running numerous pilot projects, scaling AI across the entire organization remains a major challenge. Maija Tanner shares insights from IBM’s work with hundreds of clients and partners, explaining why moving from experimentation to production is still difficult — and what companies often overlook on that journey.

The three pillars of enterprise AI: cost, security, and governance

Maija outlines three critical pillars that organizations must address to successfully scale AI:

Cost and energy efficiency – Large language models are powerful, but expensive to train and operate. Maija explains why smaller, business-process-specific models often deliver better results with lower energy consumption.

Data privacy and security – Enterprises must maintain full control over their data, especially when dealing with sensitive customer, employee, or citizen information.

Data and AI governance – Clear guardrails, transparency, and explainability are essential to ensure ethical and compliant AI usage across cloud, private, and on-premise environments.

Consumer AI vs enterprise AI: why the rules are different

While consumer AI focuses on creativity and convenience, enterprise AI operates under strict regulations, industry standards, and legal frameworks. Maija explains why AI for business requires a fundamentally different approach — one that prioritizes data sovereignty, intellectual property protection, and trust.

She also emphasizes the importance of defining clear business outcomes before choosing AI tools or technologies.

AI for people: enabling employees to do more meaningful work

A central theme of the interview is AI for people. Maija highlights how AI can automate repetitive and manual tasks, allowing employees to focus on higher-value work such as analysis, innovation, and decision-making. Rather than replacing people, AI should empower them — improving productivity while making everyday work more efficient and purposeful.

The future of AI, diversity, and continuous learning

Looking ahead, Maija stresses the importance of diversity in AI development, particularly the need for more women in leadership and decision-making roles. Since many end users of products and services are women, diverse perspectives are essential for building better and more inclusive solutions. She also encourages professionals and students to start learning and experimenting with AI early — using available tools, building simple agents, and demonstrating hands-on experience as part of their professional portfolio.

More interviews