AI success has a new scorecard—and it’s not just about ROI
From pilot projects to portfolio thinking, find out how to operationalize your AI with the metrics that matter most now.
Most organizations have now experimented with AI, from deploying off-the-shelf solutions to releasing proof of concepts (PoCs). It’s no longer about whether to invest—it’s about delivering real business value while managing risk.
As a result, leaders are redefining how AI is assessed by moving beyond experimentation to focus on execution. Success now depends on prioritizing the right investments, measuring impact beyond traditional ROI, and ensuring productivity gains translate into real outcomes.
Here’s what’s shaping the AI-to-ROI journey for organizations right now:
1. Factoring risk into AI prioritization
Leaders are discovering that not all AI use cases are created equal. Beyond technical feasibility and ROI projections, things like compliance exposure, ethical considerations, and workforce impact are major risk factors in prioritization decisions. It’s time for leaders to move away from a purely opportunistic approach and embrace a risk-adjusted AI portfolio strategy, ensuring AI investments align with both business ambitions and risk tolerance.
Actions for leaders to take:
Align on key risk factors: Define clear risk categories—such as ethical, reputational, operational, legal—and assign a “score” based on business impact.
Integrate risk assessment: Establish a risk evaluation framework as a formal part of AI use case prioritization.
Involve governance, risk, and compliance early: Make sure AI projects align with regulatory requirements and internal compliance standards before scaling.
Develop AI risk mitigation playbooks: Define proactive measures for high-risk AI initiatives, such as bias audits, explainability requirements, and fallback mechanisms.
2. Adopting a portfolio approach to AI investments
AI success isn’t about a single moon shot—it’s about managing a balanced portfolio. Leading companies invest in big bets for long-term impact while unlocking quick wins from low-effort, high-value use cases. This ensures early ROI funds future innovation, keeping momentum and buy-in strong. Disciplined AI investments deliver both near-term gains and lasting competitive advantages.
Actions for leaders to take:
Categorize AI investments into tiers: Define a balanced AI portfolio with…
- Quick wins: Low-cost, high-impact use cases that drive near-term ROI.
- Scalable initiatives: Midterm projects with measurable business outcomes.
- Big bets: Transformational AI programs requiring multiyear investment.
Implement an AI investment allocation model: Dedicate a fixed percentage of AI budget to each category to maintain a balanced approach.
Leverage quick wins to fund long-term initiatives: Reinvest efficiency-driven savings into high-impact AI innovation.
Monitor ROI across the portfolio: Continuously assess financial and operational impact across all AI investment.
3. Better utilizing productivity gains
Most leaders understand that AI drives efficiency, but turning that into tangible business value is another story. According to our research report, 79% of organizations are still running pilot AI initiatives, while 68% have already seen productivity gains. Whether it’s recalibrating hiring plans or scaling their agentic workforce, companies are still figuring out how to operationalize this new value.
It’s no longer just about saving time—it’s about doing more without adding more. Without a clear strategy to embed and measure AI-driven productivity into operating models, these gains remain elusive and are centered around cost avoidance.
Actions for leaders to take:
Adopt a persona- and workflow-based AI rollout: Map AI solutions to specific job roles and tasks to measure direct impact.
Embed AI-driven efficiency into workforce planning: Reallocate productivity gains (e.g., reducing manual tasks) to higher-value work rather than just focusing on cost savings.
Create AI productivity benchmarks: Track efficiency improvements by measuring work output per employee before and after AI adoption.
Redefine performance metrics: Shift KPIs from “time saved” to “value created” (e.g., increase in customer satisfaction, reduction in errors, speed to decision-making).
4. Measuring the value of AI agents
As more companies deploy AI agents, they’ll need to continue to track core financial metrics while also expanding how they measure agent performance. By establishing a structured performance framework that includes tracking task completion rates, accuracy, responsiveness to dynamic conditions, and team integration, organizations can ensure that AI agents are driving sustained value.
Actions for leaders to take:
Develop a comprehensive AI agent performance measurement framework that balances…
- Business value contribution: Customer impact, process efficiency, and cost reduction.
- AI operational performance: Task accuracy, relevance, error rates, and run completion.
- Adoption and effectiveness: Automation success rate, human override rate, and user engagement.
Set AI governance thresholds: Define acceptable accuracy and reliability benchmarks that allow AI agents to maintain trust and performance.
Use A/B testing for AI-driven decisions: Quantify incremental value by comparing AI-assisted processes to unassisted processes.
From hype to execution and from experimentation to value realization, the AI conversation has greatly evolved. The companies seeing real returns aren’t just piloting AI; they’re strategically embedding it into their operations, measuring success beyond ROI, and balancing quick wins with bold investments.
The AI front-runners won’t be those who experiment the most, but those who execute with clarity, scale with purpose, and integrate AI where it truly moves the needle.
Complex decision-making
When it comes to high-impact decisions, agentic systems can pull from diverse data sources, weigh pros and cons, and refine recommendations over time. This is particularly critical in dynamic environments where every situation requires a tailored approach.
At Slalom, we use a coordinated approach called "Fleet of Analysts" (FoA), where multiple specialized agents work together. Each agent focuses on a specific area of expertise, analyzing large volumes of data, identifying actionable insights, and supporting strategic decision-making. These agents can operate independently or collaboratively with human oversight, ensuring both precision and adaptability.
For example, one customer leveraged FoA to scale their ability to dynamically price products globally across 80+ countries. This helped them monitor dozens of internal and external sources of information that might influence pricing, analyze that against historical pricing strategies and current economic environments, and create automated pricing decisions.
Another customer applied this approach to enhance their enterprise architecture capability. Teams submitting new project proposals were able to rely on an intelligent agent to conduct architecture reviews. The agent coordinated research across internal guidelines, external regulations, security requirements, and industry best practices to deliver detailed assessments and improvement recommendations. This significantly reduced the workload for enterprise architects, allowing them to focus on higher-value strategic initiatives.
In both cases, the AI agents provided much-needed scalability to expert roles that traditionally spend an inordinate amount of time manually grinding out complex decisions, allowing them to focus on strategic priorities.
Slalom's AI-powered accelerator, enhanceIQ, shows how different roles can benefit from AI solutions. By uncovering the ways AI and automation can benefit work across teams, you’re empowered to make data-driven decisions that help you build a more adaptable workforce.
Essential building blocks
Deploying agentic systems calls for more than just a good LLM—it requires structure in five critical areas:
Business and customer value: Identify real-world outcomes for the agent. Does it cut costs, speed up resolutions, or enable new user experiences?
Strategy alignment and orchestration: Ensure stakeholders understand how an agent’s autonomy aligns with broader goals. Cross-functional teams are crucial to unify efforts.
Security, ethics, and governance: Agents can amplify both good and bad outputs. Implement robust guardrails such as iteration limits and human checkpoints to maintain oversight and avoid negative consequences.
Technology and data: Agents rely on up-to-date knowledge. Solid data engineering, real-time APIs, and logging infrastructure keep them accurate and auditable.
Organization and workforce: Employee readiness matters. Provide training on how to collaborate with an AI agent and clarify roles so staff know when to step in and override the system.
