Can the technology you've always used to measure results still be useful when artificial intelligence acts before humans?

How to measure technology results It's not the same as it used to be. For decades, organizations used productivity, efficiency, and satisfaction to evaluate changes. Today, artificial intelligence automates tasks and changes which cases reach your team.

In the United States, businesses are looking for resilience, security, and efficiency. That's why you need simple metrics that offer clear and actionable information. We won't talk about numbers for the sake of display, but rather indicators that reflect real impact.

I invite you to think critically: we'll question traditional metrics and explore practical alternatives. You'll find everyday examples, ideas for combining quantitative and qualitative data, and an approach for making quick decisions without overwhelming your team.

Context 2025: Why measuring results in technology is changing

The tipping point is simple: Artificial intelligence no longer just accelerates tasks; it avoids them. That generates changes what you see and how you interpret them data.

From classical efficiency to the disruption of artificial intelligence

Before, you counted resolved tickets and speed. Now, an assistant can prevent a bug from reaching support. In that example, traditional metrics hide real impact.

Less than half of CXOs are satisfied with how they measure developer productivity. Furthermore, many teams They expect AI to transform their role in the coming years. years.

Relevance to organizations in the United States

In the US, companies use modern tools, but they still fail to combine prevention, quality and safety. The advantage of future It will not be having more data, but to use them in time.

• Shift the focus from “how much we did” to “what impact we achieved.”
• Includes prevention indicators, not just tickets or speed.
• Adjust the measurement method according to sector and regulatory requirements.

Instead of relying on a single number, choose a few clear measures. This way you prioritize what really moves the needle and reduce ambiguity in your organizations.

Measuring technology results: foundations, objectives, and scope

To move your business forward, you must first agree on what success means in clear and measurable terms. Write a simple sentence that describes the visible change you are seeking in revenue, cost, risk, or experience.

Define what “success” means for your company and your team

Keep it short: one sentence that anyone can understand. For example: "Reduce critical support tickets by 40% in six months." That extent serves as a reference for daily decisions.

Business results vs. technical activity: avoid vanity metrics

Count what matters. Lines of code or commits are activity. Impact in production, security or income are results.

• Choose 3–5 metrics that connect technical work with business.
• Assign an indicator per team for focus and responsibility.
• Ask the team and users for brief qualitative information for context, not just numbers.

Support measurement methods with frameworks such as DORA and value streams. Organizations should review indicators every quarter and adjust them based on needs and risk.

Traditional metrics under scrutiny: MTTR, productivity, and satisfaction

Classic indicators deserve a review when automation changes the mix of incidents.

MTTR It remains useful for critical incidents that affect users or revenue. In those cases, the time recovery has a direct impact on business success.

However, the artificial intelligence Avoids many simple tickets. This leaves more complex cases and increases the average MTTR without decreasing the efficiency of the equipment.

To balance your reading, add complementary measures:

• Tickets avoided and self-resolution percentage.
• Breakdown by severity and root cause.
• Separate times: detection, diagnosis, correction and validation.

Don't use a single threshold for everything. Adjust MTTR targets by category and combine data quantitative with brief context of the team.

Redefine productivity: integrate quality, rework, and satisfaction. Ask yourself: What did you avoid and what did you learn? This measure also reflects real impact.

New metrics for AI: capturing real impact beyond speed

AI-driven solutions require metrics that get to the point: what changed for the user and the business.

Satisfaction and experience in AI-assisted interactions

Ask the user after the interaction: Did it help? Was it resolved as expected?

Clear indicators: post-interaction rating, first-time resolution, and user effort in the flow.

Learning effectiveness: adaptation and continuous improvement

Measures the rate of improvement per iteration, error reduction, and time to stable performance.

It also records failed sessions and reasons; those data They are opportunities for improvement.

Autonomy and real-time orchestration

Evaluates the percentage of complex tasks completed end-to-end without intervention.

Monitors decision latency, action quality, and consistency under load.

Comparing AI with AI: Cross-platform benchmarks

Use the same set of tasks, dataset, and criteria (success, safety, traceability). Document context and platform for fair comparison.

• Satisfaction rating
• Rate of improvement per iteration
• End-to-end autonomy percentage
• Latency and consistency in real time

A success story: a service desk that recorded "ticket avoidance" through proactive suggestions and improved the reported experience. Remember: speed matters, but the real impact is seen in sustained satisfaction and fewer escalations.

Software Development and DevSecOps: How to Measure Without Relying on Lines of Code

Balancing speed and reliability is the priority for modern platforms. Before using more metrics, define what impact you're looking for on the business and the user experience.

Use DORA to balance speed, quality and reliability

DORA It gives you four clear signals: deployment frequency, time to completion, average time to resolution, and change failure rate.

Set realistic goals for each team and compare them to your track record, not to other people's averages.

End-to-end value stream: lead time, cycle, and defects

Map from idea to production. Measure lead time, cycle time, and defects in production.

A example: Reduce the time required for critical changes from 7 to 3 days without increasing the failure rate.

AI ROI on Integrated Platforms: Productivity and Security

Platforms with artificial intelligence and DevSecOps tools detect flaws and vulnerabilities before production.

• Includes automated testing and dependency scanning in every pipeline.
• Capture data in real time and display a shared dashboard.
• Consider an adaptation curve: productivity may drop at the beginning.

Advice: Measures the effectiveness of AI suggestions: quality of proposed code, avoided errors, and time saved. This way, you can assess value without rushing to calculate monetary success.

ITSM and AI: Beyond Ticketing and MTTR

Key indicator: Percentage of tickets avoided by proactive actions and intelligent self-service.

From “solving quickly” to “preventing”

Records how many AI interactions prevented a ticket from being generated. This figure complements the MTTR and shows worth hidden.

Quality of service with agents and assistants

Measures satisfaction post-interaction, user effort and first-contact resolution.

• Report time in three stages: prevention, containment and resolution.
• Monitor peaks in real time and query patterns.
• Use text analytics to identify emerging issues and root causes.

Practical advice: train your teams to write good operational prompts and measure the health of the catalog: items that prevent queries and their effective usage rate.

"Avoided incidents, stable satisfaction, and fewer visible disruptions are the language the business understands."

Practical guide: How to measure success with simple and actionable metrics

A clear plan saves you noise. Start by defining short-term goals long term that connect with income, risk and user experience.

Quick steps:

1. On one page: business objectives, acceptable risks and expected short and long term results. long term.
2. Choose 3 metrics of result and 2 of process with follow-up simple in real time.
3. Launch pilots in low-risk areas; document actions and ethical boundaries before scaling up.

Use the tools that you already have: integrate simple dashboards and alerts. Capture data minimum viable and adds brief feedback from the equipment and users.

"Automating unit tests on a critical microservice reduced defects in production without lengthening cycles."

A monthly lightweight committee reviews security and privacy. Iterate every quarter: clean up useless metrics and focus on those that guide decision-making. So you company and team maintain effectiveness by using artificial intelligence with good governance.

Risks, governance and ethics: measuring impact today and in the long term

Not everything that generates value is harmless. Adopting models accelerates deliveries, but can increase technical debt and errors that appear later.

To assess long-term impact, you need clear policies for governance, traceability, and controls in the change process.

Technical debt, security flaws, and downstream costs

AI can propose code that works today and creates problems tomorrow. That requires more reviews and evidence to avoid vulnerabilities.

Start in low-risk areas. Use a platform Integrated DevSecOps to detect failures early and reduce costs later.

Transparency, traceability and periodic evaluations

Document versions of models, datasets, and applied controls. Maintain evidence in a common repository for audits.

Evaluate biases, privacy and access at least once a quarter. Adjust the metrics when usage patterns or threats change.

• The extent Impact assessment should also include risk: technical debt, vulnerabilities, and subsequent costs.
• Formalize evidence and safety checks on each delivery.
• Prioritize availability, integrity, and confidentiality before optimizing speed.
• Prepare contingency plans if third-party dependencies or models fail.

"Less is more: Set a few metrics and use them with discipline so that the information guides real actions."

If you have any doubts about compliance or regulations, consult official sources and adapt controls to the needs of your business. Measuring impact involves closing lessons learned and correcting problems, not just reporting numbers.

Conclusion

As we close this tour, remember that few well-chosen metrics are worth more than a dashboard full of numbers.

To measure success, combine business indicators with user and team experience. This way, you'll see real impact and be able to take better action in the short and long term. long term.

Avoid relying solely on traditional metrics. Prioritize value and security over apparent speed. The right tool is the one that fits your platform and your way of working.

Define clear objectives, collect minimum data, and adjust transparently. Consult specialists or official sources when you have questions about security, privacy, or compliance.

Your company It requires judgment and discipline: technologies change, but well-thought-out metrics will give you direction over time.