How To Evaluate GenAI Impact On Your Business?!

According to recent reports, a significant majority of data, analytics, and IT executives have indicated that they are experiencing positive returns on investment (ROI) from the Generative AI (GenAI) projects currently deployed in their operations. This optimistic outlook reflects a growing confidence in the capabilities of GenAI to drive value and innovation across various sectors. However, when these executives are probed about the specific methods they employ to measure the success and ROI of their GenAI initiatives, the responses reveal a more complex and somewhat fragmented picture. Some indicated that their assessment of success is predominantly qualitative, relying on subjective evaluations and anecdotal evidence, while a larger portion reported that their assessment is mostly quantitative, based on measurable data and metrics. This disparity highlights the diverse approaches organizations are taking to gauge the effectiveness of their GenAI efforts.

Among the key challenges faced by these executives is the inherent difficulty in isolating the specific impact of GenAI from other technological advancements or initiatives that may also be contributing to overall business performance. This challenge is compounded by a notable absence of clear metrics or industry benchmarks that can serve as reliable indicators of success in the realm of Generative AI. Consequently, the task of quantifying results has become increasingly complex and nuanced, particularly as the landscape of AI continues to evolve. The anticipated rise of AI agents, which are designed to operate autonomously and integrate seamlessly into existing workflows, is expected to further complicate these measurement efforts. As these AI agents interact with other technologies and systems in unpredictable manners, the boundaries between GenAI and other technological solutions will become increasingly blurred, making it even more challenging to ascertain the distinct contributions of GenAI initiatives.

In light of the significant investments being made in GenAI, stakeholders in the boardroom are understandably eager to see tangible returns. Business leaders are therefore urged to transcend traditional qualitative measures of success and instead adopt a more structured approach with clearly defined metrics and frameworks that focus on defining specific, concrete financial and operational outcomes that can be directly attributed to their GenAI initiatives. This shift in focus is essential for demonstrating the value of these projects to stakeholders and justifying ongoing investment in AI technologies. To maintain a competitive edge and stay ahead of the curve in this rapidly evolving landscape, organizations must develop and implement new frameworks for evaluating the impact of GenAI. These frameworks should prioritize quantifiable results, such as productivity gains, cost savings, accelerated time-to-market for new products and services, and other measurable outcomes that can clearly illustrate the benefits of GenAI deployments. Here are several frameworks and key considerations for evaluating GenAI impact on productivity gains, cost savings, time-to-market acceleration, and other quantifiable results:

1. Define Clear Objectives and KPIs

Before implementing any GenAI initiative, establish specific, measurable, achievable, relevant, and time-bound (SMART) objectives. For each objective, define Key Performance Indicators (KPIs) that will track progress and success. Examples include:

• Productivity Gains:

  • Reduction in task completion time (e.g., content creation, code generation, data analysis).

  • Increase in output volume with the same or fewer resources.

  • Automation rate of specific tasks or processes.

  • Improved employee efficiency metrics (e.g., more strategic work done per employee).

  • Reduction in errors or rework due to AI assistance.

    
    

• Cost Savings:

  • Reduction in labor costs through automation.

  • Lower operational expenses (e.g., reduced processing fees, optimized resource allocation).

  • Savings from improved efficiency (e.g., faster customer service reducing support costs).

  • Cost avoidance (e.g., preventing errors that would have been costly to fix).

    
    

• Time-to-Market Acceleration:

  • Reduction in product development cycles.

  • Faster content creation and deployment timelines.

  • Quicker turnaround times for analysis and reporting.

  • Increased frequency of product releases or updates.

    
    

• Other Quantifiable Results:

  • Increased sales or revenue attributed to AI-powered personalization or recommendations.

  • Improved customer satisfaction scores (CSAT, NPS) linked to AI-enhanced experiences.

  • Higher conversion rates due to AI-driven insights.

  • Reduced churn through AI-powered customer retention efforts.

  • Improved accuracy in forecasting or decision-making.

    
    

2. Establish Baselines and Control Groups

To isolate the impact of GenAI, it's crucial to establish baselines before implementation, such as how many calls are tagged with a particular issue. Measure the relevant KPIs for the processes or tasks you intend to augment with GenAI. Where possible, use control groups that do not have access to the GenAI tools to compare results and attribute changes directly to the technology.

It is vitally important to have a plan in place to deal with any insights and

recommendations that the analytics process may create. Solution providers and consultants are in full agreement of the necessity to have a ‘project champion’: someone of sufficient crossdepartmental seniority, vision and gravitas to carry out necessary change. Such a person is probably already in high demand, yet businesses that want to gain the most from analytics need to find a way to get that person involved.

Unlike some other technology implementations, analytics requires more face-to-face time with solution providers, and although some implementation times might be a matter of weeks, the reality is that the most successful projects will require considerable amounts of planning effort on both sides in order to understand what is achievable, and measure its success.

3. Implement Robust Tracking and Measurement Systems

To be successful, analytics must be integrated into the existing systems, processes and structure. Embedding it within the overall culture of the wider business is perhaps the surest way of ensuring success. At a contact centre level, connecting analytics output with the quality management process means that the operation can find a place for analytics within their world, which will encourage them to consider it for business intelligence purposes later on. Businesses may also wish to consider solutions where analytics output is shown automatically across the organisation, sharing dynamic reports and graphics on a regular or exceptional basis to business owners elsewhere in the enterprise.

So bussnisses should invest in tools and processes to accurately track the defined KPIs. This might involve:

• Integrating GenAI tools with existing data analytics platforms.

• Developing custom dashboards to visualize key metrics.

• Implementing data collection mechanisms to capture relevant information (e.g., time spent on tasks, output volume, error rates).

• Utilizing A/B testing methodologies, or ‘control and experiment’ approach, to compare GenAI-enhanced workflows with traditional ones, for example, one sales team carries on as they were, while the other may have their scripts changed or receive tailored training based on analytical insights.

  
  

4. Employ Frameworks for Impact Evaluation

Consider these frameworks to structure your evaluation:

• Return on Investment (ROI) Framework:

  • Calculate the financial gains resulting from the GenAI initiative (e.g., increased revenue, cost savings).

    
    

    Variables to be considered for ROI measurements for Revenue Increase include:

    • Increase in sales conversion rates and values based on dissemination of best practice across agents, monitored by script compliance

    • Increase in promise-to-pay ratios (debt collection)

    • Optimised marketing messages through instant customer evaluation

    • Reduced customer churn through dynamic screen-pop and real-time analytics

    • Quicker response to new competitor and pricing information

    • Increase sales revenue by automating manual, non-revenue generating activity by identifying and improving self-service options

    • Some businesses assign a revenue value to an improvement in customer satisfaction ratings or Net Promoter Score®

    • Understand and correlate call outcomes, using metadata and call analysis to see what works and what doesn’t.

    
    

    Variables to be considered for ROI measurements for cost savings include:

    • Reduction in headcount from automation of call monitoring and compliance checking

    • Understanding and minimising the parts of the call which do not add value

    • Avoidance of fines and damages for non-compliance

    • Reduction in cost of unnecessary callbacks after improving first-call resolution rates through root cause analysis

    • Avoidance of live calls that can be handled by better IVR or website self-service

    • Reduced cost of QA and QM

    • Understand customer intent. For example, an insurance company received a lot of calls after customers had bought policies from their website. Analysis was able to show that customers were ringing for reassurance that the policy had been started, meaning the company could immediately send an email to new customers with their policy details on it, avoiding the majority of these calls

    • Lower cost per call through shortened handle times and fewer transfers

    • Lower new staff attrition rates and recruitment costs through early identification of specific training requirements

    • Identifying non-optimised business processes (e.g. a confusing website or a high number of callers ringing about delivery) and fix these, avoiding calls and improving revenue.

    • etc.

      
      

  • Determine the total cost of implementing and maintaining the GenAI solution (including infrastructure, software, training, and personnel). costs to consider include:

    • Licence fees or cost per call analysed

    • IT costs to implement (internal and external)

    • Upgrade to call recording environment if required

    • Bandwidth if hosted offsite: the recording of calls is usually done on a customer's site, so if the speech analytics solution is to be hosted, it will involve of lot of bandwidth, which will be an additional cost, especially when considering any redundancy

    • Maintenance and support agreements, which may be 15-20% annually of the original licencing cost

    • Additional users - headcount cost - decide who will own and use it, do you need a speech analyst, etc.

    • Extra hardware e.g. servers

    • Ongoing and additional training costs if not included

    • May need extra software to extract data from the call recording production environment.

      
      

  • Use the formula: ROI=((Financial Gains−Total Cost)/Total Cost​)×100%

    
    

• Efficiency and Productivity Framework:

  • Measure the change in output per unit of input (e.g., content created per hour, lines of code generated per developer-day).

  • Track the reduction in manual effort and the reallocation of employee time to more strategic activities.

  • Analyze the impact on process cycle times.

    
    

• Value Stream Mapping:

  • Map the processes before and after GenAI implementation to identify bottlenecks and areas of improvement in terms of time, cost, and quality.

  • Quantify the reductions in lead time and the elimination of waste.

    
    

• Balanced Scorecard Approach:

  • While primarily for strategic management, a balanced scorecard can be adapted to include GenAI-specific metrics across different perspectives:

    • Financial: Cost savings, revenue growth.

    • Customer: Satisfaction, retention.

    • Internal Processes: Efficiency, time-to-market.

    • Learning and Growth: Employee skills development, adoption rates.

      
      

• Technology Adoption Model (TAM):

  • While not directly a financial framework, TAM can help understand user adoption and perceived usefulness of GenAI tools, which are leading indicators of potential ROI. Measure usage rates, user satisfaction with the tools, and perceived productivity gains.

    
    

5. Address the Challenge of Isolating GenAI's Impact

This is a significant hurdle. Consider these strategies:

• Phased Implementation: Introduce GenAI to specific teams or processes incrementally to allow for focused measurement.

• Controlled Experiments: As mentioned earlier, use control groups to compare outcomes with and without GenAI.

• Time-Series Analysis: Analyze trends in KPIs before and after GenAI implementation, looking for significant shifts that correlate with the introduction of the technology. Be mindful of other factors that might influence these trends.

• User Surveys and Feedback: While aiming for quantitative data, gather qualitative feedback from users about the perceived impact of GenAI on their productivity and efficiency. This can provide context and support quantitative findings.

• Focus on Specific Use Cases: Instead of trying to measure the overall impact of "GenAI," focus on evaluating the ROI and impact of individual, well-defined GenAI applications (e.g., an AI-powered content creation tool for marketing, an AI assistant for customer support).

  
  

6. Establish Benchmarks

Identify industry benchmarks or internal targets for the KPIs you are tracking. This will help you understand if your GenAI initiatives are performing well compared to expectations and the broader landscape.

7. Iterate and Refine

Evaluation should be an ongoing process. Continuously monitor your KPIs, gather feedback, and refine your GenAI strategies and measurement frameworks based on the results. Be prepared to adjust your approach as you learn more about the actual impact of GenAI within your organization.

By implementing these frameworks and focusing on quantifiable metrics, business leaders can move beyond qualitative assessments and gain a clearer understanding of the true success and ROI of their GenAI initiatives. Remember that the specific frameworks and KPIs you choose will depend on your industry, business goals, and the specific GenAI applications you are deploying.

Summary

Implementing analytics tools is one of the most important stages to guarantee that companies take advantage of the full spectrum of accessible insights. The first step is to define objectives aligned with the company's mission and vision. Setting up measurable KPIs that will contribute to the overall objectives of the organization will help to identify what and how analytics platforms should be leveraged. The objectives set should be taken in incremental waves according to their priority, as should analytics capabilities, so that contact centre stakeholders can focus on what matters the most.

Another important factor that needs to be taken into account is having the right expertise and communication channels to train and inform stakeholders on the defined objectives and KPIs and how to use the new analytics tools to generate insights.

After implementing, it’s important to keep fine-tuning. By understanding each stakeholder’s needs, organizations will be able to keep adapting their analytics and the way they make decisions.