Tech Trends 2026: From AI strategy to production impact

Most Nordic companies lack a clear, well-defined AI strategy. 

On last quarter of 2025, our on-the-ground research¹ with Finnish companies and boards discussions reveals a critical governance gap: Out of 35 large companies in Finland, only six (17%) had clear designated AI Strategy ownership, a defined path forward communicated to investors, or AI governance integrated into board reports and investor calls.

This governance gap creates three risks:

1. Reputational risk: Investors increasingly expect boards to demonstrate clear AI governance and strategy. A lack of clear AI Strategy signals weak governance and strategic clarity.
2. Financial risk: Without clear AI Strategy and governance, AI investments become fragmented and unaligned. Pilots proliferate without scaling discipline. ROI is not realized. Capital is wasted.
3. Competitive risk: Organizations with clear AI Strategy and governance move faster from pilots to production. They realize ROI faster. They gain competitive advantage. Organizations without clear AI Strategy fall behind.

The EU AI Act adds urgency. Compliance requires governance clarity. But competitive advantage comes from moving faster than competitors.

What it means to me as a Board Member

As a board member, you are accountable for defining and overseeing AI Strategy. This is not a CIO responsibility. This is a board-level governance responsibility. Your role includes:

Strategy definition: Work with the CEO to ensure a clear AI Strategy aligned with corporate strategy is defined. What are your AI ambitions? What business outcomes are you targeting? What is your path forward?

Governance oversight: Structure a board-level governance for AI adoption. This might be an AI committee, a technology committee with AI oversight, or board-level AI governance integrated into existing committees. The structure matters less than clarity and accountability.

Investor communication: Communicate your AI Strategy to investors and stakeholders. Include AI Strategy in board reports, investor calls, and governance disclosures. Investors want to understand your AI ambitions and governance approach.

Risk management: Ensure governance frameworks for AI adoption. This includes risk management, compliance (EU AI Act), data sovereignty, and ethical AI practices. Governance is not about preventing innovation—it’s about enabling innovation safely.

Measurable targets: Set measurable AI adoption targets and ROI metrics. Track progress. Hold leadership accountable. Without measurable targets, AI adoption becomes a vague aspiration rather than a strategic imperative.

Potential areas to act on

• Establish AI strategy ownership: Designate clear ownership for AI Strategy definition and execution. This might be the CEO, a Chief AI Officer, or a board committee. The key is clarity and accountability. Ensure the AI Strategy owner has authority to drive enterprise-wide alignment.
• Define AI governance framework: Establish board-level governance for AI adoption. Define oversight mechanisms, risk management processes, and investment prioritization. Ensure governance is integrated into existing board processes (strategy, risk, audit committees).
• Communicate AI strategy to investors: Include AI Strategy in board reports, investor calls, and governance disclosures. Articulate your AI ambitions, governance approach, and measurable targets. Investors want to understand your strategic direction.
• Set measurable AI adoption targets: Define specific, measurable targets for AI adoption (e.g., percentage of processes with AI agents, ROI targets, risk management KPIs). Track progress quarterly. Hold leadership accountable.
• Align AI strategy with corporate strategy: Ensure AI Strategy is not a separate initiative—it’s integrated into corporate strategy. AI adoption should support your overall business objectives, not be a technology initiative for its own sake

^{1 Internal research conducted in December 2025 and January 2026 with 35 top Finnish companies order by revenue. Additionally, validated during 4 different board members qualitative questionaries led by Deloitte through DIF (Directors Institute Finland).} 

Finnish organizations are strong at experimentation. You launch pilots, demonstrate proof-of-concept, and generate excitement. But then the hard part begins: moving from pilots to production-scale impact.

This is where many organizations stumble. Pilots proliferate. Budgets are consumed. But ROI is not realized. Competitive advantage is not gained. The organization remains in “pilot mode” rather than moving to production-scale impact.

Why does this matter? Because competitive advantage comes from execution speed, not technology. The organizations that move fastest from pilots to production will define the next era of competitive advantage.

This is particularly true in Finland, where labour costs are high and automation ROI is compelling. The organizations that realize ROI fastest will have competitive advantage. The organizations that remain in pilot mode will fall behind.

Your role as CEO is to drive scaling discipline:

• Communicate clear criteria for moving pilots from experimentation to production

• Cross-functional governance for pilot-to-production transitions

• Organizational culture that supports continuous learning and hybrid workforce

• Investor communication about AI strategy and scaling progress

• Shared accountability with Business Leaders for moving from pilots to production-scale impact

   

What it means to me as a CEO

Your role is to drive organizational transformation around AI adoption. This means:

Strategic alignment: Ensure AI adoption is aligned with corporate strategy. AI is not a technology initiative for its own sake. It’s a strategic imperative that supports your overall business objectives. Every AI initiative should answer: How does this support our corporate strategy?

Scaling discipline: Establish clear criteria for moving pilots from experimentation to production. Not every pilot should scale. Some pilots will fail. Some will be deprioritized. But the pilots that do scale must move fast. Establish governance, timelines, and accountability for pilot-to-production transitions.

Organizational transformation: AI adoption requires organizational transformation. This includes culture change, job redesign, skills development, and change management. You are responsible for driving this transformation. Work with your CHRO to manage change. Work with your CIO to enable process redesign. Work with your CFO to manage costs and ROI.

Investor communication: Communicate your AI strategy and scaling progress to investors. Investors want to understand your AI ambitions, governance approach, and progress toward production-scale impact. Regular communication builds confidence and supports your competitive positioning.

Competitive advantage: Position your organization for competitive differentiation. The organizations that move fastest from pilots to production will gain competitive advantage. Speed is your competitive weapon.

Potential areas to act on

• Define clear scaling criteria: Establish clear metrics for moving pilots from experimentation to production. This might include business case validation, governance approval, resource allocation, timeline, success metrics, and risk management. Not every pilot should scale. Be disciplined about which pilots move forward and hold Business Leaders accountable.
• Establish cross-functional governance for pilot-to-production Transitions: Create a governance structure for managing pilot-to-production transitions. This should include representation from business, IT, finance, and HR. Establish clear decision-making processes, timelines, and accountability.
• Drive organizational culture change: AI adoption requires culture change. This includes embracing continuous learning, supporting hybrid workforce, managing change, and building trust in AI systems. You are responsible for setting the tone and driving culture change across the organization.
• Communicate AI strategy and progress to investors: Regularly communicate your AI strategy, governance approach, and progress toward production-scale impact. Investors want to understand your strategic direction and execution discipline.
• Measure and track AI adoption impact: Define metrics for tracking AI adoption impact (cost savings, efficiency gains, customer experience, competitive advantage). Track progress. Hold Business Leadership accountable. Use data to optimize and scale.

Here’s a hard truth: ROI is only realized at scale, not in pilots. A successful pilot that never scales is an investment with no return.

This is particularly important in Finland and the Nordic region, where labour costs are high and automation ROI is compelling. The business case for AI adoption is strong. But only if you can realize ROI at scale.

This requires:

• Cost management: As AI adoption scales, infrastructure costs scale. You must manage these costs proactively and hold CIO accountable to define the right infrastructure for the right AI case. This includes “AI-cloud”, “AI-on premises” or “AI-on the edge” approaches, AI model selection costs, APIs, and FinOps practices.

• Investment prioritization: Not all AI initiatives have equal ROI potential. You must prioritize investments based on scaling potential and ROI. This requires rigorous business case analysis and financial discipline you must ensure Business Leaders understand.

• Scaling economics: You must understand the unit economics of AI adoption at scale. What is the cost per transaction? What is the ROI per process? How do these economics change as adoption scales?

• Financial discipline: You must define criteria and communicate to Business Leaders to ensure that pilots move to production only when criteria are met. This prevents capital waste and ensures ROI realization.

• Centralised shared services centres (SSCs) reorganisation:  Without entry-level professionals and AI taking basic tasks, there’s no internal expertise to audit AI-generated accounting entries, detect anomalies, or challenge incorrect outputs. The SSCs structure and capabilities needs review.

   

What It Means to Me as a CFO

Your role is to ensure ROI is realized at scale. This means:

Cost management: Establish cost management and optimization practices for AI infrastructure. This includes infrastructure cost optimization for AI, AI model cost management, and FinOps practices. As adoption scales, costs scale. You must manage these proactively.

Investment prioritization: Prioritize AI investments based on scaling potential and ROI. Not all pilots should scale. Some will have strong ROI. Some will have weak ROI. Be disciplined about which pilots move to production.

Financial metrics: Define financial metrics for tracking AI adoption ROI. This includes cost savings, productivity improvements, revenue impact, and profitability impact. Track these metrics rigorously. Use data to optimize and scale.

Scaling economics: Understand the unit economics of AI adoption at scale. What is the cost per transaction? What is the ROI per process? How do these economics change as adoption scales? Use this understanding to prioritize investments and manage costs.

Business case discipline: Require rigorous business case analysis for pilot-to-production transitions. Not every pilot should scale. Only pilots with strong business cases should move to production. This prevents capital waste and ensures ROI realization.

Reorganise centralized shared services centres: SSCs rely on tiered expertise, juniors handle routine work, seniors validate and escalate. By removing the junior layer, the entire governance model breaks. The roles of junior tiers need to be focused on AI control and validation reshaping the functions, capabilities and expectations.

Potential Areas to Act On

• Establish AI cost management and ROI tracking frameworks: Define metrics for tracking AI adoption costs and ROI. This includes infrastructure costs, model costs, labour costs, and ROI metrics (cost savings, productivity improvements, revenue impact). Track these metrics rigorously. Use data to optimize and scale.

• Prioritize AI investments based on scaling potential and ROI: Evaluate AI initiatives based on scaling potential and ROI. Prioritize initiatives with strong business cases and high scaling potential. Deprioritize initiatives with weak business cases or limited scaling potential.

• Define unit economics for AI adoption at scale: Calculate the unit economics for AI adoption at scale. What is the cost per transaction? What is the ROI per process? How do these economics change as adoption scales? Use this understanding to prioritize investments and manage costs.

• Implement FinOps practices for AI infrastructure: Establish FinOps practices for managing AI infrastructure costs. This includes cloud cost optimization, resource allocation, and continuous cost monitoring. As adoption scales, costs scale. You must manage these proactively.

• Establish financial governance for pilot-to-production transitions: Require rigorous business case analysis and financial approval for pilot-to-production transitions. Not every pilot should scale. Only pilots with strong business cases should move to production.

• Repurpose entry-level roles at your SCCs as “AI Validators”: Instead of eliminating entry-level roles, repurpose them as “control monitors”, for example, consider creating new job category such as “Accounting Control Analyst” at the entry level, whose primary responsibility would include spot-checking AI-generated entries, validating high-risk transition (large sums, general ledger codes, intercompany transactions), document and escalating anomalies to senior colleagues. This helps them to gain experience in different forms to take on high valuable tasks.

The traditional IT modernization narrative is outdated. CIOs are no longer just modernizing IT infrastructure or providing the right AI capability. CIOs are catalysts for enterprise-wide organizational redesign to support hybrid human-agent workforces.

This is a fundamental shift. AI agents are not just tools for IT to deploy. They are a new kind of workforce that requires new operating models, new governance frameworks, and new ways of working.

The key insight: Process redesign is not just IT’s responsibility. It’s an enterprise-wide responsibility and the CIO is the catalyst while Business Leaders have the mandate to lead AI adoption.

Why? Because AI agents operate across business functions. They touch finance, customer service, supply chain, HR, and every other business area. The CIO must partner with business leaders to redesign how work gets done across the entire organization and enable them.

This requires thinking about three operational models:

1. Human only: Traditional human-centric processes (baseline)

2. Human + Agents: Hybrid workflows with AI agents assisting humans (efficiency gain)

3. Agents only: Fully autonomous agent-driven processes with human supervision (scale)

For each business process, the question is: Which operational model is right? And how do we transition from Human Only to Human+Agents to Agents Only?

What It Means to Me as a CIO

Architecting the enterprise-wide process redesign is being add to your role. This means:

Partnership with business leaders: You are not imposing a technology solution. You are partnering with business leaders to redesign how their work gets done. You bring technology expertise and they bring business process expertise and cases. Together, you define the optimal operating model, AI capability, controls and governance.

Process mapping and redesign: Work with business leaders to map current processes and identify redesign opportunities. For each process, challenge them together with Business Leaders: Can this move from Human Only to Human+Agents? Can it move to Agents Only? What does a new process flow look like?

Governance and control: As processes become more autonomous, governance becomes more critical. You are responsible for establishing governance frameworks for autonomous agent systems. This includes risk management, control, oversight, and escalation mechanisms.

Workforce enablement: Partner with the CHRO to enable the hybrid workforce. This includes training, job redesign, change management, and continuous learning. The workforce is your most critical enabler.

Technology enablement: Provide the AI infrastructure, agent platforms, and integration capabilities that business leaders need. This is your core responsibility, and you need to consider the right model for each case: AI on cloud, on-premises and on the edge as the Tech Trends report explores. But it’s not enough—you must also enable the organizational redesign.

Potential areas to act on

• Map current processes and identify redesign opportunities: In addition to CIO’s own realms, work with business leaders to map current processes and identify which can move from Human Only to Human+Agents to Agents Only. Prioritize based on impact (cost savings, efficiency gains, customer experience) and feasibility.

• Partner with business leaders to define hybrid workforce operating models: For each business function, define the optimal operating model. Finance might move to Agents Only for routine transactions with human oversight for exceptions or human validators. Customer service might use Human+Agents for routine inquiries with humans handling complex issues. Supply chain might use Agents Only for demand forecasting with human oversight.

• Establish governance frameworks for autonomous agent systems: Define governance, risk management, control, and oversight mechanisms for autonomous systems. This includes exception handling, escalation, audit trails, and human supervision. Governance is not about preventing automation—it’s about enabling it safely.

• Upskill workforce for human-agent collaboration: In AI investments, companies are allocating 93% of budget to Tech and only 7% to People2. Partner with CHRO to develop workforce skills for human-agent collaboration. This includes prompt engineering, AI literacy, change management, and continuous learning. The workforce is your most critical enabler.

• Measure and optimize process efficiency across all three models: Track efficiency gains across Human Only, Human+Agents, and Agents Only models. Measure cost savings, productivity improvements, quality metrics, and customer satisfaction. Use data to optimize and scale.

AI adoption is fundamentally about workforce transformation. As AI agents become more capable, the nature of work changes. Humans and AI agents will collaborate in new ways. Some processes will be fully automated. Some will be hybrid. Some will remain human-only.

This requires workforce transformation:

1. Skills development: Employees need new skills for human-agent collaboration. This includes prompt engineering, AI literacy, change management, and continuous learning.

2. Job redesign: Jobs will change as AI agents take on routine tasks. Humans will focus on higher-value work: strategy, creativity, complex problem-solving, relationship management. But this requires intentional job redesign.

3. Change management: Workforce transformation is disruptive. Employees may fear job loss. They may resist change. You must manage this change proactively through communication, training, and support.

4. Governance and ethics: As AI agents become more autonomous, governance and ethics become more critical. You must ensure ethical AI practices, bias mitigation, transparency, and accountability.

    

What It Means to Me as a CHRO

Your role is to enable the hybrid workforce. This means:

Workforce assessment: Assess your workforce’s readiness for human-agent collaboration. What skills do employees have? What skills do they need? What is your skills gap? Use this assessment to guide your talent development strategy.

Talent development: Develop training programs for human-agent collaboration skills. This includes prompt engineering, AI literacy, change management, and continuous learning. Partner with your CIO to ensure training is aligned with technology capabilities.

Job redesign: Partner with business leaders and your CIO to redesign jobs for human-agent collaboration. As AI agents take on routine tasks, humans focus on higher-value work. This requires intentional job redesign, not just automation.

Change management: Manage organizational change around AI adoption. Communicate the vision. Address fears and concerns. Provide training and support. Build trust in AI systems. Change management is critical to successful AI adoption.

Governance and ethics: Establish governance and ethical AI practices. This includes bias mitigation, transparency, accountability, and employee voice. Ensure AI adoption is ethical and fair.

Potential Areas to Act On

• Assess workforce skills gaps for human-agent collaboration: Conduct a skills assessment to identify gaps in human-agent collaboration capabilities. What skills do employees have? What skills do they need? Use this assessment to guide your talent development strategy.

• Design upskilling and training programs: Develop training programs for human-agent collaboration skills. This includes prompt engineering, AI literacy, change management, and continuous learning. Ensure training is accessible and engaging.

• Redesign jobs for human-agent collaboration: Partner with business leaders and your CIO to redesign jobs for human-agent collaboration. As AI agents take on routine tasks, humans focus on higher-value work: strategy, creativity, complex problem-solving, relationship management.

• Establish change management programs: Manage organizational change around AI adoption. Communicate the vision. Address fears and concerns. Provide training and support. Build trust in AI systems. Change management is critical to successful adoption.

• Establish governance and ethical AI practices: Define governance and Trustworthy AI practices. This includes bias mitigation, transparency, accountability, and employee voice. Ensure AI adoption is ethical and fair.