Drives measurable operational and business impact while creating the reliable data foundation organizations need for AI readiness.
Organizations have traditionally invested in data transformation to streamline operations and reduce costs. Today, those same investments are helping enterprises solve one of AI’s biggest challenges: ensuring the data behind AI is accurate, governed, and trustworthy.
Syniti and SAP commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study examining the value organizations realized from using SAP Advanced Data Migration and Management (SAP ADMM) by Syniti. The study quantified measurable outcomes from data transformation. But beyond the financial metrics, the findings also reveal how stronger data readiness can support scalable AI adoption and broader business transformation initiatives.
In this blog post, we’ll explore how the TEI report not only provides a clear ROI and payback framework but also hints at how SAP ADMM helps achieve AI readiness.
TEI studies provide an independent framework for evaluating the financial impact, costs, risks, and potential ROI of a technology solution. The Total Economic Impact™ of SAP Advanced Data Migration and Management by Syniti, a commissioned study conducted by Forrester Consulting on behalf of Syniti and SAP, quantified the business value organizations realized with SAP ADMM, including:
These findings were based on a composite organization representing the experiences of Syniti customers interviewed. Forrester interviewed four decision-makers with hands-on experience using SAP ADMM and combined their insights to model the solution’s impact on a composite organization with 10,000 employees and $5 billion in annual revenue.
The study highlights measurable operational and financial outcomes, but it also tells a broader story about enterprise transformation and AI readiness.
Beyond the quantified benefits, interviewees cited several strategic advantages, including:
Taken together, these outcomes reflect capabilities essential to successful AI initiatives. As organizations invest in AI, the quality, consistency, and governance of enterprise data become critical for generating accurate insights, reducing risk, and scaling innovation responsibly.
SAP ADMM not only helps organizations improve operational efficiency and reduce costs but also helps establish the trusted data foundation needed to support AI adoption at scale.
Before implementing SAP ADMM, interviewed organizations faced significant data challenges, including duplicate records, inconsistent formatting, incomplete information, and disconnected systems. These issues not only created operational inefficiencies and increased risk but also slowed broader transformation efforts.
The same data problems can affect AI efforts. Poor-quality data leads to inaccurate outputs, unreliable recommendations, and reduced confidence in AI-driven decision-making.
By implementing SAP ADMM, organizations significantly enhanced the accuracy, consistency, and reliability of their enterprise data. One customer increased data conformity from approximately 45% to nearly 99% and improved overall data quality by over 40%. Another company achieved 99.3% data accuracy during data migration.
The improvements highlighted in the Forrester TEI study represent operational cleanup and create the trusted data foundation necessary for more reliable AI outcomes. As organizations move from AI experimentation to enterprise-scale deployment, trusted data becomes a competitive advantage.
Many organizations still spend enormous amounts of time manually cleansing, validating, reconciling, and documenting data. The TEI study shows SAP ADMM helped replace fragmented spreadsheets, disconnected tools, and repetitive manual workflows with a unified, automated platform in the interviewed organizations.
With SAP ADMM, time spent on data management tasks is reduced by 30%. Automation streamlined validation, cleansing, governance, and workflow orchestration activities that previously required extensive manual effort.
AI initiatives often stall because organizations spend too much time preparing and correcting data before it can be used effectively. By automating and standardizing data workflows, organizations can quickly move from data preparation to AI deployment.
As organizations expand their use of AI, data governance, transparency, and auditability become increasingly important. Businesses need confidence in the data powering AI, including where the data originated, how it was transformed, and whether governance policies were consistently enforced throughout the process.
SAP ADMM helped organizations strengthen governance by automating workflows, preserving audit trails, and embedding traceability into core data management processes. The platform provided real-time visibility into data quality, lineage, mappings, approvals, and transformation activities.
These capabilities delivered measurable benefits. The TEI study reports that organizations reduced internal audit preparation time by 80%, allowing teams to “prepare proactively and efficiently for audits.”
As AI becomes more embedded in business operations, companies need stronger safeguards around how data is managed and used. The data governance and traceability capabilities demonstrated in the TEI study help establish the transparency, accountability, and control required to support responsible AI adoption.
SAP ADMM helps organizations boost data quality and establish a scalable, long-term data foundation that supports future transformation initiatives, automation efforts, and broader business innovation.
By centralizing, cleansing, and standardizing enterprise data, organizations enhanced reporting, forecasting, and strategic decision-making. Instead of managing fragmented data across disconnected systems, teams gained access to more consistent and trusted information across the enterprise.
The TEI study also shows how repeatable governance models and standardized processes make it easier for organizations to expand analytical capabilities or introduce new automation without repeatedly rebuilding core data pipelines.
These are important as organizations advance their AI maturity. Scalable AI requires a reusable, governed, and well-managed data infrastructure. Those with strong data foundations are more prepared to scale AI initiatives, accelerate innovation, and get long-term business value from their AI investments.
Companies that modernize and govern enterprise data effectively will be better positioned to scale AI initiatives faster, more responsibly, and with greater confidence.
The TEI study demonstrates how SAP ADMM enables organizations to improve operational efficiency and reduce costs. It also strengthens governance and establishes a trusted data foundation, both of which support AI readiness, innovation, and more reliable decision-making across the enterprise.
Read the full study to see the quantified business outcomes, customer insights, and strategic advantages organizations achieved, including how a stronger data foundation drive AI innovation and enterprise modernization.
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