Forrester Predictions for 2026: The Shift From AI Hype to Hard Business Outcomes

EXPERT COMMENTARY

Gunnar Rohde

CEO, Striped Giraffe

CFOs are stepping in because the value story no longer matches the invoices. AI programs that can’t link outcomes to the profit and loss statement will stall — and executives who don’t instrument value creation will lose budget before they lose faith. This shift isn’t anti-AI; it’s anti-wishful thinking.

Executives should approach AI with the same rigor and discipline as any other strategic capital investment:

• Define measurable value paths by tying every AI initiative to a specific revenue, margin, or cost line.
• Instrument cost and utilization by tracking model consumption, inference efficiency, and unit economics in real time.
• Rebuild vendor negotiations by demanding transparent performance metrics and consumption-based contracts.
• Stop funding experiments by gating PoCs through finance-led reviews that require quantifiable baselines.

The businesses that benefit from this correction won’t be the biggest spenders — they’ll be the ones that treat AI as an economic engine, not a storytelling exercise.

EXPERT COMMENTARY

Alexandru Ilea

Head of AI Initiatives, Striped Giraffe

Many AI initiatives start fast but collapse just as quickly. And then the spotlight always shifts to the CIO. At that point, the problem isn’t a single failed model — it’s the lack of enterprise discipline behind it.

CIOs who want to avoid being called in as “last-minute fixers” need to set the guardrails now, before agent deployments scale and accuracy issues become business-critical.

• Establish a single governance model — define one cross-functional board that sets rules, decision rights, and escalation paths.
• Treat data as a managed product — assign owners, create update cycles, and enforce consistent standards across all sources.
• Build automated quality gates — integrate validation checks directly into agent workflows before any output is released.
• Set clear autonomy limits — specify which actions agents may execute independently and which always require human approval.
• Align cross-functional accountability — use shared metrics, shared review cadences, and joint sign-off on key agent decisions.

Without these foundations, CIOs won’t just inherit failed projects — they’ll be tasked with restoring organizational trust in AI.

EXPERT COMMENTARY

Juliane Bauch

HR Manager, Striped Giraffe

Demand for developers who can design AI-enhanced architectures is growing faster than the talent supply — and no recruitment campaign will solve this. To keep pace with a rapidly shifting market, build a controlled, strategically empowered internal pipeline.

Take the steps that actually shift the talent equation in your favor:

• Formalize AI upskilling tracks for engineers; pair them with senior architects on real delivery work, not sandbox projects.
• Redesign roles so your most senior engineers focus on system design and oversight, not ticket work.
• Use low-code, no-code, and AI-assisted tooling to handle routine development tasks, so your engineers can focus their time on the specialized, high-complexity work that truly requires human expertise.
• Leverage experienced IT partners who can take over part of the development workload and provide hands-on support during complex builds.

Organizations that orchestrate internal upskilling, smart tooling, and partner support will overcome talent shortages and accelerate AI delivery.

EXPERT COMMENTARY

Julia Riegger

Data Protection Officer, Striped Giraffe

Employees are already using personal AI tools to complete work tasks — often more effectively than stalled enterprise initiatives. However, this creates real business risks since sensitive data can leave company control, exposing the firm to regulatory, contractual, and reputational issues.

To manage shadow AI without stifling innovation, leaders should:

• Identify and classify critical data across systems to understand what can safely be processed by AI.
• Create secure, monitored AI environments for employees, including anonymization and access controls, rather than banning private tools outright.
• Raise awareness and train staff on responsible AI usage and data handling, establishing clear organizational rules.
• Implement real-time monitoring to detect unusual data flows or risky prompts, enabling proactive intervention.

By combining tech, governance, and culture, enterprises can harness the productivity benefits of AI while mitigating compliance and security risks, turning shadow AI from a threat into a bridge for adoption.

EXPERT COMMENTARY

Sophia Weiss

VP Digital Experience, Striped Giraffe

Generative AI will flood buyers with content — not confidence. What buyers lack is the ability to distinguish what is correct and commercially viable. Even a single error — a hallucinated specification or an inaccurate product recommendation — can erode trust. This is where human expertise regains strategic importance.

You must redesign the role of product experts and customer success teams:

• Equip experts with AI copilots to pre-surface insights and recommendations.
• Verify AI outputs against authoritative product, pricing, and compliance data.
• Train experts to validate AI insights and quantify its impact for buyers.
• Engage experts in the funnel as strategic validators, not reactive support, while letting AI handle routine queries.
• Consolidate data on products, configurations, and customers so AI and humans share the same reliable information.

Companies that seamlessly combine AI efficiency with human validation will set a new standard in buyer trust, experience, and conversion.

EXPERT COMMENTARY

Gunnar Rohde

CEO, Striped Giraffe

The real breakthrough in B2B payments will be agent-to-agent workflows handling validation, credit checks, and settlement without human intervention. But none of this works if the underlying data is fragmented. When invoice fields, contract terms, or supplier records exist in multiple versions, agents can’t reason reliably — they simply replicate human errors at machine speed.

Executives should begin by:

• Harmonize core financial data — ensure invoices, purchase orders, contractor details, credit limits, and payment terms share a single authoritative schema.
• Expose unified data via secure APIs — give agents controlled, machine-readable access to operate transparently.
• Translate settlement rules into machine-readable logic — codify discounts, disputes, and approval thresholds for automated execution.
• Define exception pathways — let agents flag mismatches intelligently instead of escalating everything to humans.

When the data layer is coherent, agents don’t just automate payments — they compress the entire commercial cycle.

EXPERT COMMENTARY

Krzysztof Wiśniewski

VP Data Engineering, Striped Giraffe

The real challenge is not the law itself, but the management, sharing, and verification of high-quality data with suppliers, contractors, and partners in order to meet sustainability and governance standards.

Executives should act strategically now:

• Implement ESG and compliance scoring for suppliers and partners across the value chain.
• Standardize data formats and APIs to make information machine-readable and easily auditable.
• Provide enabling tools and guidance to critical suppliers for reporting on environmental, social, and governance metrics.
• Integrate data-sharing protocols into enterprise systems to track and verify compliance continuously.

Companies that proactively manage ESG data and supply-chain transparency will reduce downstream bottlenecks, maintain trust with stakeholders, and avoid costly disruptions when regulatory scrutiny intensifies.

EXPERT COMMENTARY

Alexandru Ilea

Head of AI Initiatives, Striped Giraffe

European companies face unique challenges in scaling AI across their organizations. Operations spread across multiple countries create fragmented data, diverse systems, and siloed IT initiatives, making it hard to move generative AI beyond pilot projects.

To address this complexity, executives should:

• Establish centralized AI governance to streamline decisions and reduce cross-market delays.
• Ensure high-quality, structured data across systems and partners, enabling AI models to operate reliably.
• Invest in internal AI skills and cross-functional teams that blend technology, compliance, and business insight.
• Standardize cross-border frameworks to share models and datasets, avoiding redundant pilots in each market.
• Partner with experienced tech providers to leverage prebuilt platforms, accelerate deployment, and supplement internal capabilities.

Firms that integrate governance, data, and partnerships will transform European AI from a slow experiment into a competitive advantage, closing the gap with US peers while maintaining operational control.