How To Measure The Business Impact Of AI Services

With competition ramping up and businesses always thinking about what’s next, nothing seems to move quickly enough. There is always a pressing urgency to get things done and meet ever-more ambitious targets.

Integrating AI into processes can be daunting, but businesses must avoid the handbrake and use AI as a transformation vehicle. Establishing a strong business case and clear Key Performance Indicators (KPIs) that extend beyond Return on Investment (ROI) calculations is crucial to maintain momentum.

Collaboration among the right stakeholders is fundamental. They create the urgency and motivation needed for teams to embrace change, addressing technology and security requirements while keeping KPIs in sight.

Understanding what the entire AI integration journey looks like, along with creating smaller proof points and consistently delivering on them, becomes paramount.

Why Measuring the Impact of AI on Business Is Critical

Businesses are struggling to measure the impact of AI. This creates a ripple effect, making it challenging to demonstrate its value and justifying further investment.

Integrating AI services into your workflows can rapidly transform and positively impact your business with quick wins. However, AI adoption extends beyond project implementation and anecdotal wins.

It involves achieving tangible, measurable outcomes and delivering concrete business-wide results. Otherwise, AI risks being perceived as a black box, where the real benefits often remain hidden.

Why Integration Metrics Matter

Measuring AI’s integration impact is not just about monitoring performance. It’s about crafting a narrative that showcases AI’s potential to deliver consistent value, linking projects to business metrics and needs. That’s why AI performance metrics aren’t limited to standard measurements and include several additional aspects such as:

• Business impact. Criteria like ROI, cost savings, customer satisfaction and retention enable you to measure AI’s real value, build stakeholder confidence and justify investments.
• Operational efficiency. Response time, error rate and scalability are among the metrics that quantify the level of adaptability and reliability of AI for business. They are invaluable for increasing user satisfaction, productivity and reducing costs.
• Technical performance. These metrics reveal the level of accuracy and help you identify areas for improvement by highlighting errors in predictions (e.g., sales forecasts) and false negatives (more about it momentarily).

Business Value Needs Proof

To build confidence in AI and its capabilities, businesses require proof points and a structured approach. This is where KPIs and Objectives and Key Results (OKRs) come in.

These two frameworks, as part of a dedicated, actionable roadmap, let you associate your AI services initiatives with clear goals whilst reinforcing the message that AI should be treated as a vital business resource, just like any other asset.

Stakeholder Expectations Around ROI in AI

Gaining buy-in from your stakeholders can be a real challenge. Boards, investors and executives expect reliable evidence of ROI at every step of each AI project. They want proof that the money they’re investing in AI will also generate guaranteed financial returns such as cost reductions and increased revenue.

By embracing the combination of the specific metrics we have just discussed, along with KPIs and OKRs, you can harness the full potential of AI while reassuring the stakeholders that they will receive the envisioned outcomes and returns.

Common Challenges in Measuring AI’s Business Impact

Low Confidence, Unclear Value and Trust Barriers

One of the major issues causing hesitations toward AI s is a real lack of trust.

This problem arises from a lack of confidence in the actual capabilities of AI services and businesses often wonder whether AI will fulfil its promises. The absence of building use cases, and aligning AI use with business goals, causes scepticism, leaving decision-makers reluctant.

This is also tied to a poor understanding of the financial gains and value associated with AI implementation, frequently stemming from unclear goals and KPIs as well as ignoring AI’s long-term and indirect benefits.

It’s time to build trust and turn sceptical into confidence by ensuring that your AI goals align with your business strategy. And involve all stakeholders from the very beginning to set shared and clear OKRs and KPIs across teams.

Organisational Misalignment and Communication Gaps

Lack of communication and misalignment among stakeholders and technical teams often result in siloed operations, where employees struggle to understand how their work can impact broader business goals. This generates conflicting priorities that may lead to disjointed projects, delayed timelines and wasted resources.

Many internal IT teams struggle to realise the full value of AI as they are too emotionally invested in the business to make difficult decisions.

You can mitigate these challenges by bringing together the right individuals, allowing you to transition from urgency-driven spending to a measured, commercially viable approach driven by internal regulation and governance.

This bridges the gap between different teams, enabling them to engage in meaningful and constructive dialogue and achieve cross-functional commitment.

Data Limitations and the Complexity of AI Systems

The effectiveness of AI-driven outcomes is directly linked to the quality of the data fed to the AI system. The problem is that, in the real world, businesses are still dealing with fragmented, incomplete, or low-quality datasets. These gaps skew insights, weaken confidence and undermine the reliability of the results.

To counteract these issues, it is important to lay the foundations by prioritising data governance and quality, ensuring that the information entering your system is completely accurate, clean and up to date. It also enables you to set AI models that offer clear insights into the decision-making processes to:

• Better measure the impact of AI on your business.
• Increase your stakeholders’ confidence.
• Evidence AI’s real business impact.

Understanding the Types of Business Value AI Can Deliver

The first step to effectively measure the impact of AI on your business is to understand the multifaceted values that AI for business can generate, such as:

Operational Efficiency and Cost Savings

AI services boost process efficiency and reduce costs. By automating routine and repetitive tasks, AI streamlines workflows, enhancing productivity and reducing overhead.

For example, businesses can leverage AI-based process automation tools to reduce the time spent on reporting from several hours to a couple of hours a week. Intelligent document processing (IDP) is another perfect example of how AI can slash costs and increase efficiency.

AI tools can extract and analyse information from structured, semi-structured and unstructured data sources, enabling insurance companies to automatically process claims, detect fraud and triage insurance policies.

These valuable improvements translate into an increase in overall efficiency and can be easily tracked through KPIs.

Enhanced Customer Experience Through Automation

IBM reports that businesses using AI-powered customer service have a customer satisfaction level that’s 17% higher than those that don’t.

Tools like chatbots, automated support and personalised recommendation engines let you provide immediate answers to customer queries around the clock.

By continuous learning, AI services constantly improve their ability to serve customers effectively and create a more engaging experience that drives retention.

You can assess AI service’s success through CSAT and NPS to demonstrate to your stakeholders how AI can boost customer satisfaction, improve engagement and reduce churn.

New Revenue Streams via AI-Enabled Products

Considering AI services solely as a way to cut expenses and streamline operations is a narrow perspective that may lead you to miss this technology’s true potential.

AI can open the door to new revenue opportunities and offerings. Consider predictive analytics tools. Marketing teams use them to investigate buying patterns, customer preferences and behaviour so that they can tailor suggestions and recommendations based on purchase history.

By identifying new revenue streams, you not only diversify your offerings, but you can significantly enhance your financial growth.

Risk Reduction and Predictive Insights

AI excels in risk mitigation and loss prevention in areas like fraud detection, predictive maintenance and compliance monitoring. AI-based systems can analyse transaction patterns to identify suspicious patterns indicative of fraud, allowing businesses to take swift action before damage occurs.

For instance, in manufacturing, predictive maintenance tools powered by AI can anticipate equipment failures, reducing downtime and costs. Compliance monitoring solutions help you navigate evolving regulatory requirements such as privacy and security laws, ensuring activities align with industry standards.

Ultimately, successful AI adoption isn’t just about technology. It’s multi-dimensional. To flourish, businesses must ensure that no values are disregarded.

Key Metrics and KPIs to Measure AI Business Impact

Now that you have defined the values of AI for business, it’s time to identify the appropriate KPIs.

But you don’t have to do it alone. We believe so strongly in the transformation journey that we work with you to develop measurable and tailored KPIs that capture the full impact of your AI projects.

These balanced sets of metrics will be the vehicle to transform AI into value and better outcomes.

Productivity Gains: Time Saved and Output Increased

One of the biggest advantages of AI is that it speeds up processes. Setting specific KPIs that quantify the amount of time saved through AI automation and how much it has increased the volume of work outputs are the first steps towards measuring the real business impact of your AI investments.

That is especially important in operational-heavy roles, such as customer service, marketing, and manufacturing, where AI has significantly improved efficiency by saving time and enhancing time-to-value.

Financial ROI: Revenue vs. AI Implementation Costs

Calculating the ROI of AI initiatives requires a strategic approach. Businesses must weigh the potential revenue gains from AI against its implementation costs.

As we have learned, AI creates both tangible and intangible benefits that often are long-term and will pay off in the future, showing the importance of calculating the true effectiveness of your AI investments and making informed decisions about future initiatives.

AI Accuracy and Precision in Core Tasks

AI accuracy and precision are other two critical KPIs. When AI performs tasks accurately and precisely, businesses save time by minimising manual reviews, leading to quicker decision-making. This efficiency shortens AI’s time to value, ultimately enhancing overall productivity and effectiveness.

Customer Engagement Metrics: CSAT, NPS and Churn

Do your AI investments deliver on vital customer engagement metrics like Customer Satisfaction Score (CSAT), Net Promoter Score (NPS), and churn rate? These KPIs, which are directly related to quality, time saving, and time to value, will provide you with a clear picture. They enable you to proactively address issues and create effective retention strategies to mitigate customer attrition and boost time to value.

Time to Value (TTV) for AI Deployments

AI not only saves time by automating tasks but also accelerates the path to realising value from those efficiencies. Some AI solutions can be deployed quickly and deliver results almost immediately. It makes businesses more agile, competitive, and capable of driving innovation.

Time to Value (TTV) is your go-to measurement for setting realistic stakeholder expectations and improving project planning. Demonstrating and achieving a shorter TTV enhances confidence in AI projects and motivates business leaders to push for further innovation and exploration of AI solutions.

Conclusion

To effectively measure the impact of AI on your business, you don’t need a complex AI strategy to begin with. Adopt a straightforward, holistic approach by positioning AI as a strategic asset rather than just another investment or IT project.

Rather than playing with different experiments, identify a simple, high-impact use case that includes relevant KPIs that validate AI effectiveness and align with business goals.

Create and refine a comprehensive roadmap that’s flexible enough to adapt to any change that may happen along the way and considers continuous improvement.

At Acora, we can help you build a comprehensive approach to AI adoption by starting with an Art of the Possible Session. By analysing the key challenges within your business, your next big milestone event and specific use cases, we will align business objectives with tangible results. Talk to a data and AI expert now.