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How to bring AI and automation into the enterprise in a way that improves how work gets done and produces results you can measure.

If your organization has already experimented with AI or automation, take a look at how work actually gets done today. Accountants still key in invoice data one document at a time. Sales teams still stitch together reports from dozens of Excel files. Executives still wait days for a summary before they can make a decision.

These examples show that adopting technology is not the same as capturing its value. Many organizations have started using AI and automation, yet turning those investments into measurable business results remains a challenge.

McKinsey’s The State of AI in 2026: On the Road to ROI reports that 89% of respondents say their organizations use AI regularly in at least one business function, and 44% say they are scaling AI across the enterprise, up from 38% a year earlier. Yet only 37% report that AI has contributed positively to enterprise-level earnings before interest and taxes (EBIT).

Adoption is spreading, but turning it into business results remains a challenge. McKinsey also finds that organizations achieving the strongest results from AI are more likely to redesign workflows around AI than to simply add the technology to existing processes.

So how do you move from experimentation to everyday use? Start with these four steps.

1. Start with the Process, Not the Technology

Organizations that achieve meaningful results from AI share an important lesson: they don't just add technology to existing workflows. They rethink and redesign the workflows themselves.

Deloitte’s Tech Trends 2026 highlights end-to-end process redesign as an important part of moving from pilots to production, rather than simply using AI to speed up existing processes that may already be inefficient.

In practice, map each process from start to finish. Which steps are essential? Which are redundant? Which exist only because of limitations in legacy systems?

Remove or simplify what isn't needed, then introduce automation or AI where it can make a meaningful difference.

2. Invest in Your Data Foundation Before Your Models

Process redesign is only half the picture. Data readiness matters just as much because AI performance depends heavily on the quality and availability of the data it uses.

IDC's predictions for Asia/Pacific highlight unclear business objectives and weak data foundations as major barriers to realizing returns from AI investments. Both are areas organizations can address before starting a project.

Focus on the basics:

• Connect scattered data so information can be shared and used across systems.
• Align key definitions so departments interpret important business metrics consistently.
• Set appropriate access rights to protect sensitive information while enabling authorized use.
• Digitize paper documents and PDFs into formats that systems can read and process.

With a stronger data foundation, organizations can use AI more effectively while reducing problems caused by inaccurate or inconsistent information.

3. Pick Measurable Work Before Exciting Work

Once your processes and data are ready, choose use cases where automation or AI can produce measurable business results.

Gartner predicts that more than 40% of agentic AI projects will be canceled by the end of 2027 because of rising costs, unclear business value, and inadequate risk controls.

Screen potential projects against four criteria:

1. Task frequency — The work occurs often enough to make automation worthwhile.
2. Clear steps — The process and its business rules are well defined.
3. Data readiness — The required information is available and reliable.
4. Measurable outcomes — Results can be tracked through indicators such as time saved per transaction, fewer errors, or a shorter financial close.

Good starting candidates include document review, account reconciliation, and answering frequently asked internal questions.

In every case, weigh the expected benefits against the costs of developing, integrating, and maintaining the solution.

4. Scale with Governance and a Team That's Ready

Once a pilot demonstrates its value, clear governance helps organizations expand its use.

That means assigning process owners, identifying where human review or approval is required, and establishing ways to monitor the quality of automated outputs. Employee training is equally important so teams understand what the technology can do and where its limitations are.

Preparing to scale also connects to longer-term business opportunities.

IDC predicts that by 2030, approximately half of the new economic value generated by digital businesses in Asia/Pacific will come from organizations investing in and scaling their AI capabilities today.

This outlook suggests that AI is becoming an increasingly important part of long-term business planning, alongside the processes, data, and people needed to use it effectively.

Key Takeaways

• Redesign the process before buying tools. Automating an inefficient process may make it faster without solving the underlying problems.
• Get your data foundation ready. Connected information, consistent definitions, and documents that systems can read provide a stronger starting point.
• Choose work you can measure. Prioritize high-volume tasks with clear rules, reliable data, and KPIs agreed upon in advance.
• Govern from day one. Process owners, human checkpoints, and quality monitoring help organizations scale responsibly.
• Start small, but start early. One process with measurable results provides a better foundation than several pilots without clear success measures.

Get Started with Automation and AI with MSC

Metro Systems Corporation Public Company Limited (MSC) supports organizations in applying Automation, Data & AI to business processes. This includes connecting data across systems, using automation to reduce repetitive tasks, and applying AI to improve operational efficiency.

MSC considers each organization's existing IT infrastructure, business systems, and operational requirements when recommending solutions, helping ensure that new technologies can work effectively within the existing environment.

If your organization is looking for a place to start, MSC's specialists can help assess your business needs and identify suitable solutions aligned with your goals, with a focus on achieving measurable results from technology investments.

Note: Statistics and research findings are drawn from reports published by McKinsey, Deloitte, IDC, and Gartner. Findings reflect the scope and conditions of each study.