How should a manufacturer prioritize digital transformation investments?

A manufacturer should prioritize digital transformation investments based on the operational outcomes their company must achieve to stay competitive in the market. Because,

90% of manufacturers say that they need digital transformation to stay competitive.
Source: Rockwell Automation (State of Smart Manufacturing Report, 2025)

Now, staying competitive can mean different things for different companies. For one manufacturer, that could be reducing defects by catching them early in the process. For another, it could mean connecting all the data silos because there is no single source of truth.

Therefore, what staying competitive means for you will mostly depend on your factory’s performance metrics, like the ones where

• Below-target results keep recurring in the reports
• The gap between the performance metric and industry benchmark is too wide
• Common customer complaints keep surfacing

You can identify these challenges internally or talk to a digital transformation consultant who specializes in the manufacturing industry. To help you with a direction, we have listed common areas that we recommend prioritizing. It is based on our industry research and real conversations that we’ve had with our manufacturing existing and prospective clients.

Invest in increasing process efficiency

Because in a time when building digital solutions is easier (thanks to AI), their execution to improve processes will make all the difference. In a conversation earlier this year, our president, Viral Hirpara, shared his experience. After talking to many manufacturing leaders, he noticed that up until last year, companies were looking for technology solutions. Most leaders wanted to build a tech stack that solved their exact problems.

However, the direction of these conversations has changed drastically. While the digital solutions are still important for manufacturers, AI has made easier to build (develop) them. Therefore, the focus is shifting to the next question in order: How to improve existing processes. It includes process automation, process efficiency, and overall productivity.

Manufacturers and their digital solution partners should discuss how their business processes actually run today, where inefficiencies exist, how the right technology can improve them, and which tools can help. The goal is to improve the processes and achieve the business outcomes faster.

Prioritize investments that increase flexibility, intelligence, and resilience across operations

In our research, we found that quality, cost, and risk reduction are the core drivers behind manufacturing choosing to bring digital transformation. And it remains the same across industries and regions. Year over year, the primary outcomes manufacturers are aiming to achieve have remained the same, which proves that the underlying reasons for transformation have remained stable.

In fact, according to the State of Smart Manufacturing Report by Rockwell Automation, the top outcomes targeted by transformation efforts are:

• Improve quality, 46%
• Reduce cost, 40%
• Reduce risk, 36%

To achieve the above, manufacturers are investing in expanding automation, autonomous material movement, simulation, and AI-driven capabilities. The goal is to scale decision-making while ensuring accuracy, adapting to variability, and reducing operational exposure.

We also found that large-scale manufacturing companies like Siemens and GE have achieved the following.

Real-world scenario: Siemens uses real-time quality analytics

The company’s Amberg Electronics Factory uses real-time quality analytics. They are fed by thousands of line-side sensors to keep first-pass yield at 99.99885%. It virtually eliminates scrap.

Real-world scenario: GE uses IoT data lakes for cost efficiency

“Brilliant Factory” sites use plantwide IoT data lakes to optimize throughput and energy. They have reduced unplanned downtime by 10% to 20% and lowered power consumption.

Invest in preparing your data for AI

Most manufacturing companies have run successful AI pilots. However, the same and better outcomes have not continued in production.

The ability to manage and integrate data effectively is a priority, with 52 percent implementing cross-platform data integration or intelligent data fabrics, and 74 percent making significant use of AI-powered data platforms.

Source: Intelligent manufacturing: A blueprint for creating value through AI-driven transformation, 2025, KPMG

Two common reasons are as follows.

Industrial data is in silos or not production-ready yet

There are ERPs that are connected with legacy PLCs, which hamper smooth data flow. On the other hand, SCADA systems, MES platforms, and spreadsheets operate differently. It creates a siloed environment, wherein AI implementation is not possible. For a successful pilot and then production, manufacturers must break the data silos and ensure that their data is collected consistently, updated, and governed.

Early results show model accuracy and not the business impact

It is one thing to achieve 95% prediction accuracy, and completely another to see the impact in reduced overhead and increased throughput.

When an AI pilot use case is successful, it proves that the technical process and solutions are in place. However, converting that automation or efficiency into a solid business impact needs a more comprehensive and strategic approach. That’s where simpler but more important solutions and processes come in.

For example, one of our semiconductor clients wanted to prepare for AI at scale. Instead of jumping into solving operational problems with AI and even running a pilot, we built a CMS to capture and store their e-commerce data. We helped them define what should be the primary goal, how they should architect the solution, and how to ensure compliance. Their CMS will function as their data foundation (data source) for future AI and data projects.

When the data is organized, structured, and accessible, scaling AI can deliver meaningful and reliable outcomes.

Prioritize AI investments based on your business capabilities

Every AI use case does not need the same level of investment. In fact, there is a process to choose your use case carefully so that you can get maximum return on your investment.

1. Identify business objectives based on your business strategy
Choose three broader objectives. They can be based on your performance metrics. You can prioritize any like creating product value, ensuring supply chain, driving revenue, satisfying customers, etc. Then, find its application.

For example, if you want to increase your revenue, improve your customer experience. But if you want to keep your assets running, work on predictive maintenance.

2. Identify responsible stakeholders for each business objective

Manufacturers often treat AI implementation as an IT responsibility, whereas it has to be a business responsibility. Therefore, assign a responsible stakeholder for every business objective you have chosen. It could be Chief Product Officer to create product value and Plant-level Operations Leader to optimize production output.

While your digital solution partner and IT department will orchestrate the technology, your stakeholders will define what success looks like, ensure their teams adopt the change, and get the expected returns.

Following the business strategy-led approach will help you choose the right use case and ensure that your AI investments are not scattered across departments chasing different goals. And you invest only in sequenced and owned objectives which are tied to outcomes that matter for your company.

Next: Take the first step

You have understood where to invest and where to pause. Plus, what to prioritize and how to approach your digital transformation strategies.

First, take an honest look at where your operations stand today, which metrics are underperforming, where your data is disconnected, which business objectives lack the modern solutions for better results.

Then, you can talk to a digital transformation consultant who specializes in manufacturing. They can build a roadmap specific to your company so that you can achieve your business goals.