Inside Nokia's AI Strategy to Boost Profits by 60%

As 2025 winds down and planning for 2026 ramps up, one headline cuts through the noise: Nokia is betting big on artificial intelligence to target a 60% profit surge by 2028—aiming for €2.7 to €3.2 billion in annual operating profits. Whether you're in telecom or not, the Nokia AI strategy is a timely signal for every leader rethinking how AI, Technology, Work, and Productivity intersect.

The lesson isn't just about telecom. It's about how complex operations become simpler—and more profitable—when AI moves from pilot projects to production. If your 2026 roadmap includes AI, this post breaks down what Nokia's move means, where value is created, and how to apply the same playbook in your organization.

In our AI & Technology series, we focus on real-world outcomes. Today, we'll unpack the strategy behind the headline and turn it into practical steps you can use to work smarter, not harder.

Why Nokia's AI Bet Matters Beyond Telecom

Telecommunications is a productivity crucible. Networks run 24/7, serve billions of devices, and face seasonal surges—like the upcoming holiday and travel peak—without room for downtime. If AI can improve reliability, cut costs, and unlock new revenue in this environment, it can do the same in other complex operations across manufacturing, logistics, retail, and healthcare.

• AI shifts value from hardware to software and services, increasing margins.
• Automation reduces operational overhead and speeds up decision-making.
• Data-driven optimization compounds over time, improving both performance and cost.

For leaders, the key takeaway is simple: AI is no longer just a research investment—it's a profit strategy. Nokia's move crystallizes how disciplined AI deployment can materially affect the bottom line.

Inside the Nokia AI Strategy: Where a 60% Gain Could Come From

Nokia's target isn't a moonshot if you consider four reinforcing growth levers that apply to many industries.

1) Autonomous Operations and AIOps

Running global networks is a high-stakes, high-cost endeavor. AI for IT operations (AIOps) can automate root-cause analysis, predict failures, and trigger self-healing workflows. Think of it as moving from "monitor and react" to "predict and prevent."

• Predictive maintenance to reduce outages and truck rolls.
• Automated anomaly detection to cut mean time to resolution (MTTR).
• Closed-loop automation to keep service levels high with fewer manual interventions.

Savings here translate directly into higher operating margins—an immediate boost to productivity.

2) Energy-Aware Networks and RAN Optimization

Radio access networks (RAN) consume the bulk of a mobile operator's energy. AI can dynamically power down idle radios, tune spectrum use, and balance load across cells while meeting quality targets.

• Energy spend drops without sacrificing experience.
• ESG metrics improve alongside cost, supporting investor narratives.
• Fine-grained, real-time controls enable seasonality and event-based optimization.

The same pattern applies to any energy-intensive operation—from data centers to factory floors.

3) Cloud-Native, Open Architectures and 6G Readiness

Nokia's pivot to cloud-native software, open interfaces, and edge computing positions it for the next wave: 5G Advanced now and 6G later this decade. Open RAN and cloud-native cores aren't just architecture choices; they're business model accelerants.

• Faster feature delivery with CI/CD for networks.
• Vendor diversity and modularity reduce lock-in risk.
• Software subscription and support models improve recurring revenue.

4) New Revenue via Private Wireless and Network APIs

Enterprises want predictable performance at the edge—think smart factories, ports, and energy sites. Private 5G with AI-enabled computer vision, robotics, and safety monitoring unlocks use cases where Wi-Fi struggles.

• Industrial automation and quality control with real-time analytics.
• AR-assisted workflows and remote maintenance to elevate worker productivity.
• API monetization: exposing secure network capabilities (like location, QoS, or slice control) to app developers.

Put together, these levers stack: lower opex, higher software margins, and new revenue lines. That's the blueprint behind a 60% profit goal.

What Leaders Can Borrow: An AI Playbook That Works

You don't need to run a global network to make this strategy work. Adapt the core elements to your domain.

Start with high-frequency, high-cost decisions

• Identify top cost drivers (energy, support tickets, rework, downtime).
• Map decisions that repeat daily or hourly; they're ripe for automation.
• Define success in business terms: fewer outages, faster cycle time, higher throughput.

Build a minimal but durable data foundation

• Consolidate telemetry and event data into a single observability layer.
• Standardize schemas and quality checks; reliability beats volume.
• Use feature stores or reusable data pipelines to avoid one-off models.

Prioritize closed-loop automation

• Move beyond dashboards. Trigger actions via policy: scale up, pause, reroute, or notify.
• Keep a human-in-the-loop for control and governance, then gradually expand autonomy.

Make energy a first-class KPI

• Track energy per unit output (per call, per order, per part produced).
• Let AI optimize for a composite objective: cost, performance, and sustainability.

Focus on software margins

• Shift value from bespoke projects to reusable platforms and services.
• Monetize capabilities via internal or external APIs.

Risks and Realities: How to De-Risk AI at Scale

Bold targets need grounded execution. Here's how to manage the headwinds.

Model performance and drift

• Use shadow deployments and canary releases for new models.
• Monitor real-world performance, not just lab metrics (e.g., false positives per incident).

Security and data governance

• Classify data and control access with least-privilege policies.
• Log every automated action; maintain audit trails for compliance.

Talent and change management

• Cross-train operations teams on AI tools; pair domain experts with data scientists.
• Incentivize teams on outcome metrics: uptime, energy intensity, cost per ticket.

Vendor complexity and lock-in

• Prefer open standards and portable runtimes.
• Negotiate exit ramps and data egress in every contract.

The healthiest AI programs are boring in the best way: predictable releases, measurable improvements, and repeatable runbooks.

Your 90-Day Action Plan (Steal This)

Use this timeboxed plan to convert ambition into outcomes and improve productivity fast.

Days 1–30: Discover and Frame

• Pick two uses cases: one cost reducer (e.g., predictive maintenance), one revenue enabler (e.g., personalization or API exposure).
• Baseline current KPIs: downtime, MTTR, energy per unit, cost per ticket.
• Inventory data sources and gaps; define governance and access.
• Choose your delivery approach: in-house build, partner, or hybrid.

Days 31–60: Prototype and Automate

• Build thin-slice prototypes with real data and a human-in-the-loop.
• Wire automations to safe actions: enrich a ticket, suggest a fix, throttle a resource.
• Stand up observability: latency, error rates, drift metrics, and business KPIs.
• Draft runbooks and escalation paths; train operators and owners.

Days 61–90: Pilot in Production

• Launch a limited-scope pilot with canary traffic.
• Enable closed-loop automation for low-risk actions; keep manual approval for high-risk.
• Review weekly: KPI movement, model behavior, operator feedback, customer impact.
• Decide to scale, iterate, or retire; lock in a quarterly release cadence.

The Bottom Line

Nokia's AI strategy isn't just a telecom story; it's a template for profit-focused transformation. By tying AI to energy optimization, autonomous operations, software-first models, and new revenue streams, a 60% target becomes a structured pathway—not wishful thinking.

As you finalize 2026 plans, borrow the best of this approach: pick measurable outcomes, automate the highest-frequency decisions, and build toward reusable platforms. In our AI & Technology series, we'll keep sharing playbooks that raise Productivity across real Work, not just proofs of concept.

Where can the Nokia AI strategy inspire your next 90 days—and what profit lever will you pull first?