ROI with AI: Strategies and Best Practices

A holistic strategy is essential to ensure that AI investments drive value across financial, operational, and strategic dimensions, maximizing overall returns. Evaluating the company’s entire portfolio of AI initiatives is crucial to understanding the broader impact on overall corporate strategy and return on investment. This value materializes across multiple dimensions, including financial benefits, operational efficiency, and strategic advantages, all of which directly impact business growth and sustainability.

– Financial Benefits

AI delivers substantial financial gains by increasing revenue streams and significantly reducing operational expenses. For instance, companies that implement Aisera’s AI solutions can expect to realize payback within 6-9 months, depending on the scale of the implementation and the specific use cases, such as customer support automation or IT service management. Additionally, AI can enhance revenue streams by enabling more effective marketing strategies and personalized customer experiences.

– Operational Efficiency

Automating repetitive tasks and streamlining workflows through AI-driven automation improves productivity across the board. Aisera’s AI platform enables businesses to achieve up to 50-70% cost savings in support and IT service management, significantly reducing overheads while optimizing resource use. This efficiency directly contributes to faster ROI and clear payback in a short span. Moreover, AI-powered customer support systems can provide faster and more accurate responses, leading to higher customer satisfaction and loyalty.

– Strategic Value

Leveraging AI empowers organizations to gain a competitive edge by driving innovation and improving customer experience. Aisera’s AI solutions, with their focus on proactive problem-solving and personalized customer engagement, allow businesses to achieve both short-term wins and long-term strategic gains.

– Innovation ROI

This type of ROI measures the return on investment in AI-driven innovation, such as the development of new products, services, or business models. AI initiatives can drive innovation by uncovering new opportunities and enabling the creation of cutting-edge solutions. For example, generative AI can be used to develop new product designs or optimize existing ones, leading to a more diverse and competitive product portfolio.

To fully realize the benefits of AI investments and ensure a timely return, organizations should prioritize high-impact use cases. Aisera’s AI solutions provide specific pathways for maximizing ROI in key operational areas:

– Customer Support Automation

Aisera’s conversational AI solutions significantly reduce support ticket volumes by automating responses to routine inquiries, leading to 30-50% faster resolution times. Leveraging conversational AI not only improves customer satisfaction but also translates into direct cost savings by minimizing the need for extensive human intervention in repetitive tasks. Generative AI can also be leveraged in AI customer Service to create dynamic responses to complex customer queries, offering a more personalized support experience.

– IT Service Management

By automating incident resolution and removing the dependency on human-driven IT operations, organizations can reduce the average cost per ticket and enhance productivity by reclaiming 1-4 hours per ticket. With these tailored use cases, businesses can achieve clear financial payback within a defined timeline, while also improving service quality.

Organizations that adopt a prescriptive approach—aligning AI investments in ITSM with specific business outcomes, backed by clear success metrics—will be able to realize both quantitative financial gains and qualitative operational improvements, ensuring maximizing ITSM ROI from their AI implementations.

Hard Returns: Tangible Benefits of AI

Hard returns represent quantifiable benefits that have a direct impact on a company’s financial performance. These include:

• Revenue Enhancement and New Product Introductions: AI can drive new revenue streams by enabling faster go-to-market (GTM) strategies, advanced data analytics, and targeted marketing. AI-driven recommendation systems, for instance, can boost sales through personalized product suggestions, while AI enables businesses to launch new products more rapidly and efficiently.

• Reducing Revenue Churn through Better Customer Experience: AI solutions, such as AI-powered chatbots, significantly improve customer engagement and satisfaction by delivering faster, more personalized responses. This better customer experience helps reduce revenue churn by retaining customers, offering premium services, and increasing loyalty.
• Cost Savings:  By automating repetitive tasks, AI dramatically lowers operational costs. For example in the context of conversational AI ROI, AI-powered chatbots and automation platforms can handle customer inquiries and internal IT requests, reducing the need for large customer service teams and cutting down on costs associated with managed service provider (MSP) contracts.
• Mitigating Unplanned Business Outages: AI solutions help mitigate the financial impact of unplanned business outages by predicting potential failures and resolving incidents faster, leading to fewer and shorter outages. This proactive approach ensures business continuity and operational stability, reducing potential revenue losses tied to downtime.
• Reducing Employee Churn and Fatigue: AI-driven automation helps reduce employee fatigue and churn by alleviating the burden of repetitive tasks, allowing staff to focus on higher-value work. This results in improved job satisfaction and reduced employee turnover, which can be costly for businesses.
• Future-Proofing Against Headcount Growth: As AI scales, it enables organizations to manage increasing customer bases without the need to proportionally hire more staff. By leveraging AI to handle larger volumes of requests and incidents, businesses can avoid future headcount increases while maintaining or improving service quality.

Soft Returns: Tangible Benefits of AI

Soft returns, while not always immediately quantifiable, are crucial for long-term success. These include:

• Boosted Employee Engagement and Productivity: AI automates mundane and repetitive tasks, allowing employees to focus on more strategic and fulfilling work. This not only boosts job satisfaction and employee retention but also improves overall employee productivity. Employees no longer need to wait for troubleshooting or depend on IT intervention for common issues such as account lockouts or software updates.
• Enhanced Customer Experience: AI tools, such as personalized support systems, deliver smoother, more accurate customer interactions, leading to greater satisfaction and customer loyalty. For instance, AI chatbots provide fast, tailored responses to customer inquiries, improving the overall customer experience and helping to reduce churn by building lasting relationships.
• Informed Decision-Making: AI allows business leaders to access valuable insights from large datasets, empowering them to make more informed and data-driven decisions. By automating data analysis, generative AI frees up time for business owners to focus on strategic planning and long-term growth.

For companies that have put AI to work, like Aisera’s clients, the benefits are stacking up fast. With advanced automation crunching away at routine tasks, customer satisfaction is getting a significant boost from personalized support. And as employees get to focus on tasks that truly require their expertise, the organization as a whole starts to hum along more efficiently.

Aisera also plays a crucial role in reducing staff churn by automating repetitive tasks and streamlining workflows, which helps alleviate frustration and fatigue. By handling routine inquiries and incidents with AI-driven solutions, Aisera enables agents and support engineers to focus on more complex and engaging work. This shift not only enhances job satisfaction but also significantly reduces turnover, with an 80-90% reduction in fatigue-induced churn, contributing to a more stable and motivated workforce.

Despite its potential, measuring ROI for AI investments poses several challenges due to the complexity and evolving nature of AI projects. Here are some common hurdles:

Critical Success Factors – Ensure Rapid Time to Value

Defining Clear Metrics: Clearly define the business goals AI investments aim to achieve. Set success criteria and KPIs that align with business outcomes to evaluate the effectiveness of AI projects. Continuous review and refinement of AI strategies based on performance data and evolving business needs are essential for maximizing ROI.

Establish a Clear Roadmap Based on Value: Start by defining specific business goals that AI investments aim to achieve. Create a roadmap that prioritizes high-value use cases, ensuring that the implementation directly aligns with these objectives. By maintaining a clear focus on value, organizations can accelerate their time to value and maximize ROI.

Seamless System Integration: One key to rapid AI implementation is the ability to integrate seamlessly with existing systems, such as ticketing platforms or HR systems, using out-of-the-box (OOTB) connectors. Gaining access to system owners and establishing smooth connections with these platforms allows AI solutions to work without friction, reducing delays and ensuring a faster route to value realization.

Ease of Change Management: A smooth transition to AI-driven solutions requires effective change management. The goal should be to minimize the impact on end users and guide them through the process. Rather than forcing a change in user behavior, AI systems should adapt and reach out to users with intuitive, guided solutions that seamlessly fit into their workflows. This approach ensures a more rapid adoption and faster time to value, as users won’t need to drastically alter their habits or processes.

Long-Term Benefits of AI: Although AI delivers rapid time to value, it’s important to recognize the long-term potential of AI investments. The advantages of AI compound over time, with more accurate predictions, better insights, and continuous process improvements. As AI scales within an organization, its impact on productivity, revenue growth, and innovation becomes more profound.

By leveraging AI and automation, Aisera efficiently handles a larger volume of requests, scaling seamlessly with rising demand. As ticket, request, and incident numbers grow, Aisera automates repetitive tasks and provides advanced self-service options to users, ensuring that service quality remains consistently high. This approach not only maintains operational efficiency but also helps organizations regain 1-5 years of growth coverage without the need for additional human resources.

Addressing the AI Data Dilemma

Data is fundamental to the success of AI, and its quality directly affects the performance of AI systems. Organizations must tackle several data-related challenges:

• Ensuring Data Quality: Accurate, complete, and relevant data is crucial for effective AI operations. When data quality is poor, it can cause misleading insights and ineffective outcomes.
• Data Integration: AI solutions often require integration with diverse data sources. Robust data integration is necessary to provide a holistic evaluation of the AI investment
• Data Security and Privacy: Since there is lot of company and customer data involved, AI systems become vulnerable to data breaches and security concerns. Keeping customer data safe is a top priority, and companies can’t afford to slack off when it comes to protecting privacy – their reputation depends on it.

To ensure that AI investments deliver the highest value, organizations should adopt a strategic, phased approach and implement best practices.

Proven Concepts for Scaling AI

Invest in Scalable Infrastructure: Building a scalable infrastructure is key to supporting expanding AI needs over time. What once seemed like large, daunting AI projects are now easier to grow, thanks to cloud-based solutions and advanced computing. A phased approach allows you to make each phase of AI implementation self-funded, with the outcomes and ROI from phase one acting as the investment for subsequent phases. This ensures continuous value realization without overwhelming upfront costs.

Work with a Vendor that Prescribes a Multi-Year Strategy: Choose a vendor who can prescribe a multi-year AI strategy that aligns with your specific business outcomes. By collaborating with a partner who understands the need for long-term, outcome-driven solutions, your AI journey will stay on course, delivering ongoing ROI across all facets of your business.

Encourage Cross-Functional Collaboration: Foster close collaboration between technology and business teams to ensure that business objectives are clearly reflected in AI initiatives. A phased approach also allows various teams to adjust to AI solutions over time, gradually transforming how they work. By aligning AI initiatives with measurable business metrics, each phase becomes a building block toward achieving greater organizational impact.

Aisera’s solutions not only scale seamlessly with growing demand but also empower employees to handle tasks more effectively and without disruption. By providing efficient self-service and swift resolutions to common issues, Aisera enhances productivity, saving 1-4 hours of employee time per productivity-impact request.

To accurately measure the ROI of AI initiatives, it’s crucial to choose the right key performance indicators (KPIs). Here are some tips for selecting the most relevant KPIs:

1. Align KPIs with business objectives: Choose KPIs that align with the overall business objectives and goals of the AI project. For example, if the goal is to improve customer satisfaction, relevant KPIs might include customer satisfaction scores or the average response time for customer inquiries.
2. Focus on outcomes, not outputs: Instead of measuring the number of AI models developed or the amount of data processed, focus on the outcomes generated by the AI project, such as cost savings or revenue growth. This approach ensures that the KPIs reflect the actual business impact of the AI initiatives.
3. Use a mix of quantitative and qualitative KPIs: Combine quantitative KPIs, such as financial metrics, with qualitative customer service KPIs, such as customer satisfaction or employee engagement. This balanced approach provides a more comprehensive view of the ROI of AI investments.
4. Consider the entire portfolio: When evaluating the ROI of AI initiatives, consider the company’s entire portfolio of AI projects, not just individual projects. This holistic view helps identify synergies and ensures that all AI investments contribute to the overall business strategy.
5. Monitor and adjust KPIs: Continuously monitor and adjust KPIs as needed to ensure they remain relevant and effective in measuring the ROI of AI initiatives. As business needs and market conditions evolve, it’s important to update KPIs to reflect these changes and maintain alignment with strategic goals.

By choosing the right KPIs, business leaders can accurately measure the ROI of their AI investments and make informed decisions about future investments. This strategic approach ensures that AI initiatives deliver tangible business value and support long-term growth and success.

Effective risk management is crucial for successful AI implementations. Rather than rushing into AI projects without proper planning, organizations should identify and address potential stumbling blocks upfront. Consider these strategies to ensure a smoother process:

• Conduct Comprehensive Risk Assessments: Proactively identify potential risks associated with AI projects, such as technical challenges, data privacy issues, and regulatory compliance concerns. Having backup plans in place to mitigate these risks is essential to safeguard the project’s success.
• Perform Pilot Testing: Implement pilot tests to evaluate AI solutions on a smaller scale before full deployment. Pilot testing helps uncover potential issues and validate the effectiveness of AI technologies in real-world scenarios.
• Implement Robust Change Management Practices: Support the adoption of AI with a structured change management approach that minimizes disruption. A smooth transition involves preparing teams for the change and ensuring that AI integration is aligned with user workflows.

Aisera’s comprehensive risk management methodology—featuring risk assessments, pilot programs, and change management strategies—ensures that AI implementations are thoroughly tested and validated before full-scale rollout. This proactive approach effectively minimizes potential challenges, increasing the likelihood of successful AI initiatives while safeguarding long-term business value.

Optimizing AI models involves enhancing their performance and accuracy through various techniques to deliver results faster and with minimal friction:

• Fine-Tune Hyperparameters: Adjust hyperparameters to improve model performance. Techniques such as grid search and random search can help identify the optimal hyperparameter values, enabling the AI model to perform more efficiently, and contributing to faster time to value.
• Evaluate and Validate Models: Continuous evaluation of AI models using performance metrics and validation techniques ensures they remain effective and accurate. Regular assessments help organizations quickly refine models, driving faster results with minimal downtime.
• Enhance Feature Engineering: Improve model performance by selecting and engineering relevant features. Feature engineering creates or transforms features to enhance model accuracy, which speeds up the delivery of insights and solutions. Models powered by generative AI, with their ability to create new data patterns, can further optimize predictions and improve outcomes in complex use cases.
• Leveraging Aisera’s Hyperflows for Frictionless Time to Value: Aisera’s Hyperflows system, with its intentless capabilities, allows users to operate the system with minimal learning curves, making it accessible and usable by a wide range of employees in a short amount of time. Hyperflows ensure rapid adoption and efficiency, allowing businesses to start realizing tangible benefits almost immediately.

Aisera further enhances AI model optimization by accelerating MTTR (Mean Time to Resolution) on incidents. Through the Aisera Assist platform, support engineers gain rapid access to the latest SOPs and knowledge sources, enabling lower tiers of support to resolve issues more efficiently.

By providing up-to-date, relevant information, Aisera reduces the time spent searching for solutions, significantly improving overall service performance and achieving a 30-50% faster MTTR.

AI-driven IT automation can streamline operations, reduce manual processes, and boost efficiency. Focus on these areas:

• Automate Incident Management: Implement AI solutions for automated incident detection, resolution, and reporting. This reduces response times and improves IT service management.
• Use Predictive Maintenance: Employ AI to predict and prevent IT system failures by analyzing historical data and identifying potential issues before they occur.
• Optimize Resource Allocation: Utilize AI-driven insights to manage server loads, network traffic, and storage requirements more effectively.

Aisera reduces business outages by equipping support engineers with rapid access to the latest SOPs and knowledge sources through its Assist platform. This proactive approach leverages generative AI enabling faster and more accurate incident resolution, preventing critical issues from escalating, and reducing the likelihood of unplanned outages by 30-50%.

As artificial intelligence continues to advance, the future of AI offers exciting opportunities and challenges.

Generative AI Copilot:

AI copilots represent an emerging trend in AI applications, offering advanced capabilities for task automation, data analysis, and decision support. Key aspects include:

• Enhanced Integration: An enterprise AI copilot integrates with multiple systems and applications, providing a unified platform for managing tasks and information. This integration improves productivity and streamlines workflows.
• Contextual Understanding: AI copilots utilize large language models (LLMs) to comprehend context and deliver relevant insights. This capability enhances decision-making and supports complex use cases.
• Improved User Experience: AI copilots offer intuitive interfaces and conversational interactions, making it easier for users to access information and perform tasks efficiently.

By equipping lower tiers of support with AI copilots, Aisera enables it to efficiently handle routine issues, reducing the need for experienced engineers to engage in repetitive tasks. This strategic allocation allows senior engineers to concentrate on complex, high-impact projects that drive innovation and strategic growth. As a result, organizations can maximize their engineering resources, reallocating 30-50% of engineer time to more critical and valuable work.

Organizations face a critical decision regarding their LLM strategy as the foundation of AI models: whether to build custom AI solutions or purchase off-the-shelf products. Key considerations include:

• Cost Analysis: Compare the costs associated with developing custom AI solutions versus buying existing products. Consider factors such as development time, expertise, and ongoing maintenance.
• Complexity Assessment: Evaluate the complexity of building and maintaining custom AI solutions. Off-the-shelf products may offer quicker implementation and reduced complexity.
• Alignment with Needs: Determine whether custom solutions or pre-built products better meet your specific requirements. Custom solutions may offer tailored features, while off-the-shelf products provide established functionality.

One central hub, and thousands of automated processes – that’s the secret to smoothing out even the most sluggish operations. The numbers add up fast: bolstered efficiency, lower costs, and a generally slicker workflow.

Maximizing ROI from AI investments requires a strategic approach that balances both tangible and intangible benefits. By understanding the nuances of AI ROI, addressing challenges in measurement, and implementing best practices for optimization, organizations can unlock the full potential of their AI initiatives.

Embracing an outcomes-based approach, managing risks effectively, and leveraging advancements such as AI copilots will further enhance the value derived from AI investments.

As businesses look to maximize ROI in their AI investments, Aisera’s comprehensive AI solutions offer the perfect blend of innovation and efficiency. By strategically implementing Aisera’s AI solutions, organizations are well-equipped to navigate the complexities of AI and achieve sustained success in the future.

To experience the power of Aisera’s generative AI book a custom AI demo for your enterprise today!