What Should Industries Prioritize for AI: Tools, Applications, or Data?

In the last few years year, the corporate world has witnessed a surge in enthusiasm around AI. Organizations across sectors are adopting generic AI tools like chatbots, assistants, content generators, copilots. This interest towards AI tools is largely driven by the pressure to appear “AI-ready.” Leaders want to showcase quick wins, employees want to experiment, and companies want to signal digital maturity.

While this trend reflects healthy curiosity, it also masks a deeper truth: generic AI tools alone will not create sustained business advantage. Tools improve individual productivity, but their impact on transforming operations is limited. How will AI understand company’s operation if it is not created from company’s data and how will it transform if it doesn’t understand well.

Even today, the real differentiator lies not just in using AI tools, but in building customized, context-specific AI applications embedded into core business processes.

The approach to adopt AI applications is very different from simply evaluating a tool, purchasing it, and putting it to use. Unlike generic AI tools, customized AI solutions require long-term collaboration between the user company and the solution provider. This means organizations must develop an understanding of the AI solution-development process and the success factors. This is critical as organization need to provide data and bring their domain knowledge, and operational context. Building effective AI applications is a structured journey, not a plug-and-play exercise. Let’s look at the key steps involved in creating customized AI applications that can truly transform operations.

How Customized AI Applications Are Developed

Unlike generic tools, customized AI applications are built with a clear business objective. The process typically involves:

1. Identifying high-value use cases

Use case prioritization starts with understanding where the business loses the most time, money, or efficiency. This involves analyzing bottlenecks in production, quality, maintenance, supply chain, or energy usage and prioritizing problems that directly affect throughput, yield, cost, or customer experience. The goal is to select use cases where AI can create measurable value, not just interesting experiments. Some of the example are: forecasting demand, optimizing production schedules, predicting equipment failures, minimizing energy consumption, or improving batch consistency. These are problems unique to each plant, workflow, equipment type, and product mix.

2. Understanding the process and capturing domain knowledge

AI in manufacturing is only as good as the domain expertise behind it. This step requires deep engagement with operators, process experts, engineers, and maintenance teams to map how the process actually runs. The goal is to capture real-world constraints, variability and root-cause patterns that AI must learn. This will come from reviewing machine behavior, Process parameters and Operator practices. Generic tools cannot understand this context, we need custom AI to learn this industry specific information

3. Building data pipelines and ensuring the right data foundation

AI applications depend on clean, consistent, and reliable data. This step focuses on integrating sensors, machines, systems, and logs to build a structured data pipeline that captures real-time and historical information. Ensuring data quality, completeness, labeling, and synchronization is critical, because without a strong data foundation, even the best AI models will fail. This is where most organizations struggle as they lack consistent data capture processes

4. Developing models tailored to the operation

Once the data is ready, AI models are built and trained using plant-specific patterns and business rules. These models learn what drives downtime, quality issues, yield variation, cycle times, or energy usage. The output is not generic insights, but precise predictions and recommendation on optimization aligned to the unique behavior of each machine, process, and product. Some of the analysis that gets done are :

• What causes machine downtime
• What parameters drive yield variation
• How to optimize cycle time
• When equipment will fail
• Which batches are likely to deviate

5. Integrating AI into daily workflows

The true value of custom AI emerges only when insights translate into action. This step involves embedding the AI application into operator dashboards, MES/ERP systems, control rooms, or maintenance workflows so that decisions improve in real time. Adoption, training, and change management are essential to ensure the model becomes part of everyday operations

6. Measure Impact and Continuously Improve

AI applications evolve as the business evolves. After deployment, performance needs to be tracked against KPIs such as throughput, quality, downtime, or cost savings, followed by refining models as more data becomes available. Continuous feedback loops ensure the solution remains accurate, relevant, and aligned with operational goals.

Impact of Customized AI Applications

When developed and deployed effectively, AI solutions create deep, measurable, and sustained impact across manufacturing operations. They unlock higher throughput by optimizing production flows and minimizing delays, allowing plants to produce more with the same resources. Yield and quality improve as AI identifies subtle process variations and recommends corrective actions before issues occur. Energy and material waste reduce significantly as AI pinpoints inefficiencies and guides teams toward optimal settings and consumption patterns.

Where to start: The Non-Negotiable Foundation

AI applications simply cannot perform well unless the underlying data is accurate, complete, and time-synchronized across machines, processes, and systems. It must be captured consistently, using disciplined practices rather than irregular or manual logging. Challenge that we see in majority of plant is the scattered data at multiple places like paper logbooks, spreadsheets, ERP and customized applications. The data must be unified with single source of truth, reflecting real operating conditions so that the models can learn true patterns, variations, and anomalies. Without this reliable and representative data layer, even the most advanced AI models will struggle to produce meaningful insights or deliver measurable impact.

Industries often want to jump into AI but overlook basic data readiness. AI amplifies value only when data is trustworthy and auditable, hence before jumping into flow of  AI adoption, first we need to reach a state of data maturity. The right approach is:

1. Start digitizing processes first.
2. Improve data quality and availability.
3. Then build AI on top of a reliable foundation.

A Balanced Strategy

The message is definitely not to avoid AI tools as they have clear benefits of speeding analysis, improving reporting and helping teams learn digital faster. These tools makes us lot more productive in our day to day work, But lets not treat these AI tools as the replacement of the classical or core AI applications. Use cases for AI tools and customized applications are very different and hence it is critical that skill development in organizations covers both aspects. Real competitive advantage will come from a balanced strategy, not just picking one option. Most important, acquiring data digitally should be the starting point followed by leveraging it to build custom AI applications that drive business growth and solve real operational problems.