Agentic commerce is not only transforming the retail industry by automating operations, personalizing shopping journeys, and enabling real-time decisions, but consumer adoption and engagement are soaring.
Best practices for building a successful AI foundation
Fueling Agentic Commerce with Clean Data
YoY traffic increase to US retail sites from GenAI browsers and chat services1
Yet artificial intelligence (AI) performs only as well as its data foundation.
While agentic commerce can empower the consumer experience (CX), AI models will want to provide answers even if they are inaccurate.
For AI agents to autonomously find, compare and purchase products for consumers, they need to be able to pull from a foundation of high-quality, API (or machine-readable) data.
To do this, retailers must anchor their AI initiatives with a clean data foundation.
more time on site1
Many enterprises tapping into AI are consistently running into data challenges as the primary obstacle as they work to train, deploy, scale, and determine the ROI of their AI initiatives. In fact, Gartner reports that 50% of generative AI projects were abandoned after proof of concept due to poor data quality.2
The failure rate of AI projects is high, with industry estimates that are upwards of 95% for generative AI projects. Bad data creates:
Prevent biased results by training and fixing metadata at the source.
Cleanse and update records—verify identity, name, address, email, and phone in real time, or batch update inaccurate or outdated information. Parse and structure data into a usable format.
AI-powered business applications offer tangible value to consumers and business operations alike, but effective AI outcomes require rich, accurate, unbiased data.
In a world driven by data, accuracy is more than important—it’s everything.
The Pitfalls of Poor Data
The Impact of Getting it Wrong
Best Practices for AI-Ready Data
The Keys to Building the Data Foundation
Winning With AI-Ready Data
Build an AI-Ready Future With Melissa
Failed pilots/delayed rollout and delayed ROI
Unreliable automation
Weak/wrong personalization
Inaccurate recommendations
Low conversions/ higher cart abandonment
Autonomous checkouts and purchases fail/break
Increased manual fixes
and higher operational costs/costly reworks
Eroded consumer trust and more complaints/refunds
Outdated or stale data, and duplicate records
45% of respondents indicate concern about data accuracy or bias4
Poorly structured or non-machine-readable data
CEOs and CTOs can do their parts by getting their data houses in order, empowering teams to innovate safely, and monitoring all AI deployments for signs of bias or misinformation.
Use real-time training and semantic flagging to correct errors.
Use well-labeled data to ensure accuracy across mission-critical AI applications.
Melissa Open APIs
Enrich with demographics, firmographics, geographics, social media, property attributes, and missing email and phone information to support analytics, personalization, and omnichannel marketing efforts.
Match and merge duplicate records to create a single, accurate consumer profile you can trust.
Monitor your data across the entire data lifecycle to prevent bad data from entering your database and keep it clean over time.
For 40 years, Melissa has been solving data-related issues to ensure that organisations have a good understanding of what those issues are and how they affect computing systems.
Through its Data Quality Assessment Methodology, Melissa provides a “snapshot” of data issues and identifies ways to begin correcting them.
Plan
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lower bounce rate1
Why Does This Matter?
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Sources:
1. BCG, “Agentic Commerce Is Redefining Retail—Here’s How to Respond”; 2. Arun Chandrasekaran, “Why 50% of GenAI Projects Fail — And How to Beat the Odds,” Gartner, January 26, 2026; 3. Strategy, “2026 Data, AI & Analytics Trends Survey Report + Podcast Series”; 4. Deloitte, “The 5 biggest AI adoption challenges for 2025”; 5. Coupa, “85% of CFOs Say AI Is Central to Their Strategy, Yet 92% Fear They Can't Execute”; 6. IDC via Epsilon, “Improve data quality to support quality AI outcomes”; 7. McKinsey, “The agentic commerce opportunity: How AI agents are ushering in a new era for consumers and merchants”; 8. Google, “Introduction to structured data markup in Google Searchinteraction”
McKinsey7
Investing in agent-ready infrastructure—encompassing APIs, data interoperability, trust frameworks, and governance—is essential to staying competitive in a rapidly evolving landscape.
McKinsey 7
Lynne Schneider, IDC 6
If the underlying data is inaccurate, incomplete, or outdated, AI models may misidentify customer preferences, target the wrong segments, or deliver irrelevant content—ultimately leading to wasted resources and missed opportunities.
Machine-readable descriptions of API endpoints.
Flexible API access and plug-in for time savings.
Empowers AI agents to operate and call APIs with less programming.
99% of leaders struggle with defining consistent business metrics across tools and departments3
Nearly 80% of data teams spend more than half their time on data preparation rather than insight generation3
Inconsistent or incomplete data
CFOs report losing an average of 26 hours per month—over 3 working days—manually reconciling data5
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32%
10%
27%
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CLEANSE
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ENRICH
4,700
%
Legacy, fragmented, or siloed data
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400% growth in clicks
SAP
843% increase in clicks in 9 months
Sharp Healthcare
35% increase in visits
Food Network
82% higher CTR
Nestlé
25% higher CTR
Rotten Tomatoes
Some examples of structured data SEO (or rich results) wins include these examples:
SIDEBAR: SUCCESS STORIES
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CLEANSE & AUGMENT
EXPERT SUPERVISION
AUTOMATE & SCALE
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Use a narrow, agentic, traditional AI aligned with qualified data and business rules.
AVOID BROAD “GEN AI”
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