Gartner predicts that by 2026, 80% of enterprises will be using GenAI APIs or applications—AI is definitely here to stay. But despite the buzz, both CEOs and CIOs are still questioning its profitability. In fact, 49% of organizations struggle to estimate and prove AI ROI, making it a real challenge to fully embrace. In today’s article, we’ll cover what you need to know about AI value and how to better estimate and boost your ROI.
And don’t miss the chance to hear more about making AI ROI tangible at the ServiceNow World Forum in Dallas. Spencer Beenmiller, ServiceNow’s Director of Executive Innovation Strategy, will be there to break it all down. Plus, we’ll be there too—visit us at booth #10 to chat about the best AI strategy for your business. Reach out to us now or stop by in Dallas to explore how we can help you drive growth!
What Is the Importance of AI ROI In Business?
First let’s see what exactly means by “AI ROI”. This measures the financial benefits gained from investing in AI technologies. It evaluates the cost savings, revenue increases, and efficiency improvements resulting from AI implementations. In this sense, this is essential to help businesses determine the real value and effectiveness of AI investments. In summary, it is what gives you the idea on what and where it is worth investing in AI solutions.
But how does GenAI impacts on ROI? Well, there are multiple ways in which this kind of AI can help enhance revenues. It enhances productivity, speeds up decision-making, and personalizes customer experiences, driving higher engagement and revenue. GenAI also unlocks new income streams through innovation, ultimately leading to cost savings, faster output, and improved customer satisfaction.
What Are the Key Factors to Measure AI ROI?
Now let’s examine what are the key factors to measure AI ROI. While there are many aspects that contribute to calculating the real value of the implementation of AI technologies, these can be summarized to:
- Cost Savings: AI can reduce operational costs through automation and improved efficiency. However, you must always bear in mind that cost savings must not compromise service nor product quality.
- Revenue Generation: Increased sales and new revenue streams from AI-driven products and services.
- Efficiency Improvements: Enhanced productivity and faster decision-making processes.
- Customer Satisfaction: Improved customer experiences leading to higher retention and loyalty. In this sense, the implementation of Agentic AI technologies could be very helpful to maximize ROI.
- Innovation: Development of new business models and opportunities through AI capabilities.
The Importance of Integration to Maximize AI ROI
While understanding those components is essential to better evaluate AI investments, integration is the key to maximizing its value. This is also signaled in MuleSoft research, where 45% of IT leaders point out that this also increases ROI. But how? Well, this can be summarized as it follows:
1 Enhances Data Utilization
AI thrives on information, and integrating your systems ensures that the vast amount of data generated across departments is effectively leveraged. Proper integration means AI has seamless access to both structured and unstructured data, enabling better predictions, customer insights, and process optimizations. Without it, this technology will not have the information needed to perform properly.
2. Improves User Adoption
One of the common reasons AI projects fail is because they don’t mesh well with existing tools or workflows, which means people resist using them. This affects almost 39% of organizations. But integration makes AI feel like a natural extension of the systems people are already used to working with, increasing adoption and engagement.
3. Scales Across Use Cases
Since AI is not a one-size-fits-all solution, ROI comes when it is applied across core business areas. In this sense, integration allows organizations to scale AI across the different core functions without the need for standalone solutions for each.
4. Faster Time to Value
Integrated AI solutions deliver quicker AI ROI because they are easier to implement and scale within the business. You won’t need to start from scratch every time you deploy AI in a new department or use case, leading to faster time-to-value.
In summary, integration makes sure AI isn’t just an add-on—it becomes a core part of your business, enhancing everything you do. At Inclusion Cloud, we’re here to help you find the AI integration that brings real, measurable results. Reach out today, and let’s find the perfect plan for your business.
Over and Underperformers: The Two Faces of AI Implementation
There are basically two types of organizations when it comes to AI ROI performance. On the one hand, we have overperforming enterprises, who can achieve a high return from their AI projects. On the other hand, there are the underperforming businesses, who usually struggle to realize significant ROI from their AI investments. But then, what makes the difference? This can be summarized as follows:
1. Preparation and Foundation
Overperformers invest significantly in preparation. They implement key practices in data management, tracking results, and ensuring security, privacy, and ethics. This strong foundation is crucial for achieving high AI ROI.
2. Experience and Maturity
Companies with more experience in Artificial Intelligence projects tend to see higher AI ROI. Leaders in AI implementation reported an average ROI of 4.3%, compared to just 0.2% for beginners. This experience also translates to shorter payback periods for their investments.
3. Value Realization Areas
Overperformers gain value from AI across multiple areas, including:
- Higher productivity.
- Increased customer satisfaction.
- Improved employee engagement and profitability.
- Development of new products/services.
In addition, overperforming business are also more likely to see strategic benefits from AI implementation, like innovation in new products, rather than just efficiency improvements. This is key, as some of its benefits are intangible and hard to quantify, but not less important for long term ROI improvements.
How Can I Measure AI ROI in Business?
As we mentioned earlier, estimating AI ROI is one of the main difficulties when it comes to implementing this kind of technology. Then, to measure the real value expected of AI implementations is vital for any organization. But this process varies depending on your business particularities. However, it can be summarized in the following steps:
1. Define Clear Objectives
The first thing to do is determine what you aim to achieve with AI implementation (cost reduction, revenue growth, etc.). For this, you must set key performance indicators (KPIs) that align with your objectives. For example, if you aim to growth your revenue, your KPIs can be increasing sales, reducing operational costs, or improving customer retention rates.
2. Collect Baseline Data
Once you establish your objectives, gather data on current performance metrics before AI implementation. This includes financial information, operational efficiency, and customer feedback. Then, compare with industry standards to understand your starting point.
3. Implement AI Solutions
With solid information about your situation, you can start implementing AI solutions. Usually, the best is to go for small-scale pilot projects to test AI solutions and gather initial results. Then, once pilots show positive outcomes, scale up the AI implementation across the organization.
4. Monitor and Measure Performance
It is essential to continuously monitor the KPIs set in the first step to measure the real impact of AI implementation. For this, you can use analytics tools, like Now Assist from ServiceNow Platform, to assess the performance data and identify trends, improvements, or areas needing adjustment.
5. Calculate ROI
After a certain period, you can calculate AI ROI by comparing the costs of its implementation (including development, deployment, and maintenance) with the financial benefits achieved. However, keep in mind that you should wait for a prudent time to make these measurements. Calculating ROI shortly after AI deployment fails to account for potential performance deterioration over time.
Keep in mind that some key aspects mentioned above may vary according to your business characteristics. That’s why the best is to consult with industry professionals. In this sense, it’s essential to take advantage of major events within the sector since they are an excellent opportunity to hear both experts tips and industry leaders and peers experience with AI technology. Follow us on LinkedIn and stay updated with the latest industry events and trends!
Top Areas for AI Returns
While many companies see a positive outcome from their AI implementation, there are some key areas in which this kind of technology seems to be more productive. In this sense, the sectors with a best AI ROI are the following:
- Customer service and experience (74%).
- IT operations and infrastructure (69%).
- Planning and decision-making (66%).
Who Is Responsible for Demonstrating AI Value in a Business?
To demonstrate the real value of AI in business is a shared responsibility among various roles. Of course, this depends on the enterprise size and organization, but usually his assignment usually falls on one of these five roles:
- Executive Leadership (C-Suite): CEOs, CIOs, and CTOs set the strategic direction for AI, ensuring it aligns with business goals and driving investment in AI initiatives.
- CDOs and CDAOs: They must show how to contribute to successful AI projects, competing in some cases with other executives for ownership of internal AI efforts.
- Product Managers: These are responsible for aligning AI initiatives with business priorities and justifying the business value of investing in data science. In addition, they must communicate where data science initiatives are making strategic and operational impacts.
- Business Unit Leaders: They identify AI use cases within their departments and help measure AI’s impact, such as increased revenue or efficiency in operations.
- Data Science and AI Teams: They oversee the development of AI models and solutions, translating technical capabilities into practical, measurable business benefits.
GenAI: The Future for Retail
As it importance continuos to growth, GenAI for retail deserves particular treatment. According to Gartners survey, this is the most frequently deployed AI solution, with 29% of CIOs applying it to their operations. The main reason behind this fact is its unprecedented ease of use. Everyone who can ask a question of a search engine can use common language to interact with a GenAI chatbot or virtual agent.
Use cases of GenAI: How can enhance ROI?
As we have said, calculating GenAI ROI for retail is one of the most common barriers in AI implementation. This is mostly due to GenAI is still in its early stages, making it difficult to predict long-term impacts on revenue, some of its benefits are hard to quantify. That’s why we will analyze some punctual use cases of this technology in the following sections.
1. Dynamic Pricing Optimization
GenAI can analyze market trends and suggest optimal pricing strategies, leading to profit margin growth. According to McKinsey research, it is estimated that AI-driven pricing strategies can increase revenue margins by 2-4%, a significant boost in competitive industries.
2. Inventory Management
AI-driven demand forecasting improves inventory management, reducing excess stock and minimizing stockouts. According to McKinsey’s research, AI-enabled supply-chain management boosts logistics costs by 15%, inventory levels by 35%, and service by 65%. Besides, it reduces inventory carrying costs by up to 30%.
3. Retail Efficiency & Personalization
In retail, according to McKinsey’s report, AI-driven automation can reduce costs by 15-30%, primarily through personalized marketing and customer service automation. Besides, 60% of retailers utilizing AI reported significant improvements in customer engagement and operational efficiency.
4. Productivity Improvements in IT & Software Management
GenAI can boost developer productivity by 20-40% in code generation and refactoring tasks. McKinsey’s research highlights that it helps reduce time spent on repetitive tasks like code documentation and auto-completion, freeing developers to tackle more complex challenges. In addition, AI-assisted software development has improved completion rates of complex tasks by 25-30%, accelerating product delivery while maintaining quality.
Find What You Need to Know at the ServiceNow World Forum 2024 Dallas
Both AI and GenAI are proving their value with real-world applications that are transforming work as we know it. But figuring out the ROI of AI can be a real challenge, which is why it’s so important to understand its true value and potential returns before diving in. And there’s no better way to do that than by learning from industry experts. That’s why both attending to key events and hear expert tips are essential.
But, for this, is necessary to be updated with the latest industry news and trends. Without this, you will probably let pass many chances to get valuable insights. But don’t worry. At Inclusion Cloud we got you covered. Follow us on LinkedIn and don’t miss the key industry trends and news! And if have an integration project you like to discuss, meet with us and let’s see the best way to carry on according to your business needs.