AI Examples in Manufacturing: One Innovation at a Time

5 Effective AI Strategies to Secure Long-Term Business Value 

65% of organizations implement AI solutions in at least one business function. However, adoption alone doesn’t guarantee success due to the previously discussed barriers. 

Here are 5 strategies to define ROI, integrate AI activities with company objectives, and deliver long-term value for sustainable growth. 

1. Define AI vision 

AI can’t solve everything on its own. While machine learning and AI capabilities can optimize processes and improve efficiency, they’re not a catch-all solution. This is why business goals (not the latest tech advancements) should always be the starting point for any AI adoption strategy. Vision-oriented organizations are 1.5 times more likely to achieve desired outcomes. 

Thus, brainstorm with your team: What are the organization’s primary objectives, and how can AI contribute to achieving these? Gartner reported that over 600 long-term AI adopters measure success by focusing on business metrics, such as: 

• Business growth (cross-selling potential, price increases, demand estimation, new asset monetization) 

• Customer success (customer retention and satisfaction rates) 

• Cost-efficiency (inventory reduction, production costs, employee productivity, asset optimization) 

Gartner also noted that these organizations identify metrics early and measure AI use case success consistently. AI projects with direct ties to business objectives have a clear vision that helps maximize success. 

2. Assess AI readiness 

44% of companies find gathering relevant, consistent data an increasing AI adoption barrier, causing data silos that can severely limit AI performance. 

44% of companies find gathering relevant, consistent data an increasing AI adoption barrier, causing data silos that can severely limit AI performance. A solution  is for AI teams to evaluate data availability and quality across different departments, systems, and processes.  

They must integrate these data sets into a central system using an Enterprise Data Warehouse (EDW).

An EDW is a centralized repository that stores data from multiple sources across an organization. It consolidates, cleanses, and standardizes data, making it readily available for AI systems to access and analyze. By having a single, reliable data source, organizations can minimize discrepancies that can skew analyses and predictions, ensuring that AI models are built on a solid foundation of high-quality, consistent data. 

3. Identify high-impact use cases 

Searce reported that 97% of organizations have at least one GenAI use case already up and running. The more high-impact these use cases, the more value they bring. 

Infoverity encourages pilot projects to build confidence in potential use cases. Pilot projects offer a controlled environment to assess feasibility, performance, and ROI before scaling AI efforts. 

In fact, 59% of AI leaders pilot AI initiatives to validate ROI to overcome barriers. To conduct this, observe recurring inefficiencies or issues in your operations. These pain points represent resource-intensive workflows AI can automate or optimize. 

Tie each use case to financial metrics to measure AI’s tangible benefits. Suppose the pain point is excessive customer service wait times. Assess the AI chatbot’s performance by tracking: 

• Reduced operational costs (fewer staff hours required) 

• Improved customer satisfaction (better retention rates) 

• Increased customer throughput (higher volume of queries handled per day) 

In a nutshell, your use cases should be where AI delivers substantial value to ensure ROI.  

4. Develop a strategy roadmap 

A well-structured AI strategy roadmap helps organizations manage risks, track progress, and scale AI applications. It ensures they achieve short-term wins while keeping an eye on long-term scalability and integration with other systems. Start by sorting the use cases by their potential to achieve corporate objectives (e.g., deliver competitive advantage or improve supply chain efficiency). Next, set attainable milestones to ensure timely execution. 

Finally, map out the resources, technology, and infrastructure required for each phase to support smooth implementation. AI teams can undertake these tasks themselves or seek providers like Infoverity to facilitate the process.  

Infoverity tailors a tactical AI/ML roadmap with model recommendations and gap analysis to identify priorities and resources upfront. Organizations can fast-track their AI adoption process through enhanced models and reduced time to market. 

5. Quantify Return on Investment (ROI) 

Quantifiable ROI estimates allow stakeholders—both technical teams and executive leadership—to see AI investment’s direct impact on business performance. 

To get these figures, teams must track key performance indicators (KPIs) related to business outcomes. Below are some industry-specific KPIs related to AI initiatives: 

Customer service 

• First contact resolution rate – how fast AI resolves issues on the first contact 

• Cost per interaction – cost savings from automating customer inquiries 

Manufacturing 

• Downtime reduction – how AI-driven maintenance minimizes machine downtime 

• Production efficiency – how much AI helps increase output and reduce waste 

Retail 

• Sales conversion rate – increase in sales due to AI-driven personalized recommendations 

• Inventory turnover – AI’s accuracy in predicting demand 

Finance 

• Fraud detection accuracy – how effective AI detects real-time fraudulent transactions 

• Operational efficiency – how AI-automated processes lower operational costs 

Healthcare/Pharma 

• Diagnosis speed and accuracy – improvement in diagnostic speed and accuracy with AI 

• Drug development time – reduction in time-to-market for new drugs through AI-driven R&D 

Insurance 

• Claims processing time – how fast AI speeds up claims approvals 

• Risk assessment accuracy – how AI improves underwriting decisions and pricing accuracy 

Monitoring progress monthly or quarterly can help verify if AI functions as planned. Although rapid deployment initiatives (e.g., fraud detection) may benefit more from frequent tracking—weekly, daily, or even in real-time—to allow for immediate adjustments.