The Single Secret to Agentforce Success
By Jenna Trott | 5 Minute Read

At A Glance
01. Discover how Salesforce Agentforce is transforming business automation across sales, service, and marketing.
02. Learn why high-quality, unified data is the cornerstone of Agentforce’s effectiveness.
03. Understand the challenges posed by unstructured data, which makes up 80% of organizational information.
04. Explore the critical role of robust data engineering in unlocking Agentforce’s full potential.
05. Find out how to ensure advanced AI systems deliver actionable insights and personalized experiences.
Products Highlighted
Agentforce
Data Cloud
MuleSoft
Salesforce Flow
Slack
Atlas Reasoning Generation (RAG)
PeerIslands
The innovation of humankind knows no bounds; spanning a millennia and touching nearly every aspect of life, we’ve accomplished some pretty remarkable feats. From the first general-purpose electronic computer to the dawn of the internet–and beyond–humans have and continue to journey beyond what was previously known to be possible. Such is the case for the minds at Salesforce, who, for the last 25 years have pushed the envelope of innovation to give us technological advancements like Salesforce Flow, Data Cloud, and the name that needs no introduction–Agentforce.
Agentforce represents a fundamental shift from how we’ve historically conducted business. Teams can now leverage autonomous agents capable of independently navigating complex tasks across sales, service, and marketing functions. These AI agents go beyond simply following scripts—they make decisions, take initiative, and seamlessly integrate with Salesforce’s powerful ecosystem, including Data Cloud, MuleSoft, and Slack.
For organizations looking to go beyond traditional automation, Agentforce offers unmatched opportunities. It frees human talent to focus on strategic initiatives while maintaining consistent, high-quality customer interactions across every touchpoint. However, like any advanced AI system, Agentforce’s effectiveness is fundamentally linked to the quality of its foundational data. Organizations must implement rigorous data management practices to ensure their information is clean, reliable, and actionable—something even advanced tools like Data Cloud need to operate effectively.
The Data Challenge
In the same way that wheels are necessary to propel a car forward, data is integral for Agentforce to work effectively. At its core, the platform leverages Retrieval Augmented Generation (RAG) to enhance its responses by pulling real-time data from multiple sources, including CRM systems and external data lakes. The Atlas Reasoning Engine serves as the platform’s brain, processing this integrated data to formulate contextually relevant responses and execute autonomous actions. Through a continuous feedback loop and deep integration with business data, Agentforce can understand customer history, access relevant information, and provide personalized responses.
However, the modern enterprise faces a significant data challenge: we simply have too much. And the vast majority of this information—approximately 80%—is unstructured, buried within PDFs, articles, case notes, presentations, and other formats that are difficult to activate. Further compounding this issue is the disconnected nature of enterprise systems. On average, organizations juggle 1,026 applications, with 71% of them failing to communicate effectively. While humans can laboriously parse unstructured data to some extent, these AI agents will fall short without proper integration; and while we may chuckle at the occasional AI hallucinations in other settings, it’s no laughing matter when it comes to your clients.
Unifying fragmented customer data is crucial not just for technical efficiency, but for enabling AI systems to retrieve, analyze, and generate deeper insights into customer preferences and sentiment—ultimately delivering truly personalized experiences.
Tackling Salesforce Data Challenges
Salesforce Data Cloud stands as a powerful tool in today’s business landscape, offering robust capabilities for unifying customer data and delivering actionable insights. However, like any sophisticated platform, its success hinges on the quality of its foundation. While Data Cloud excels at processing and analyzing information, businesses often face underlying challenges with data quality, system integration, and organizational silos that can limit its effectiveness. These challenges include:
Poorly maintained or incomplete data that limit the effectiveness of advanced tools.
Complexity in integrating legacy systems with Salesforce.
Organizational silos that require customized solutions beyond out-of-the-box features.
That’s where data engineering plays a pivotal role—ensuring that your foundation is as robust as the solutions you build upon it. By providing essential services like data cleansing, pipeline development, and governance frameworks, data engineering bridges the gap between Salesforce’s capabilities and business utilization to create a comprehensive solution that drives sustainable success.
DI-WHY: Don’t Do Data Alone
While you may be tempted to lead data cleansing efforts yourself, one-off data cleanups are like bailing water from a leaky boat in the middle of the sea. You tirelessly scoop out water cup by cup, only to watch the waves pour right back in through the cracks. The effort is endless, and the progress–temporary. Treating data maintenance as an isolated task is much the same. You might scoop out the bad data for a while, but without fixing the underlying leaks in your processes, you’ll always be one wave away from being overwhelmed.
Yet, many organizations, in an effort to control costs and maintain data sovereignty, still attempt to handle their data maintenance and cleanup initiatives in-house. This do-it-yourself approach often proves to be a costly misstep that can cascade into long-term technical debt and compromised AI performance. One significant cost is the inefficiency arising from human error, where incorrect or incomplete data cleaning amplifies errors across systems, requiring additional time and resources for rectification. A study on data management costs found that errors in handling data fields disproportionately increase operational expenses, with actual costs often exceeding projections by 1.6 to 4.86 times.
Data engineering isn’t just about running cleanup scripts or standardizing formats–it requires deep expertise in data architecture, governance frameworks, and integration patterns developed through years of hands-on experience across diverse business scenarios. Internal teams, already stretched thin with daily operations, typically lack the specialized tools, proven methodologies, and cross-industry insights that professional data engineers bring to the table.
Furthermore, what might seem like simple data inconsistencies can mask complex underlying issues that require sophisticated solutions to address properly. The cost of getting it wrong–whether through incomplete cleansing, improper standardization, or inadequate integration–can far exceed the investment in professional data services, especially when considering the downstream impact on AI-driven tools like Agentforce that depend on pristine data to function effectively. In the end, attempting to DIY your data infrastructure may work temporarily, but the risks and potential long-term consequences far outweigh any initial savings.
PeerIslands & Access Global Group
In today’s AI-driven business landscape, PeerIslands and Access Global Group (AGG) offer a comprehensive solution for organizations looking to utilize Salesforce’s Agentforce accurately and effectively. By combining AGG’s deep Salesforce expertise with Peer Islands’ data-driven software solutions, the partnership addresses the critical challenge of unlocking siloed and unstructured data that often limits AI effectiveness. This solution includes:
Data lake architecture design that can handle diverse data types, from customer service transcripts to IoT sensor data, while maintaining seamless integration with Salesforce.
Custom ETL pipelines that automatically cleanse and standardize data from legacy systems, such as converting inconsistent date formats or normalizing product codes across platforms.
Automated data quality frameworks that continuously monitor and correct issues like duplicate records, incomplete fields, or outdated customer information.
Integration solutions that connect siloed systems, such as bridging on-premise ERP data with cloud-based customer service platforms.
The AGG and PeerIslands partnership approach begins with facilitated workshops to understand organizational needs on a deep level, followed by detailed data architecture mapping that optimizes your Salesforce CRM environment. Through expert data structuring and implementation services, they build and migrate data to a streamlined environment, ensuring seamless integration with Salesforce through advanced iPaaS and ETL solutions. This foundation enables organizations to fully leverage Data Cloud’s capabilities and Agentforce’s AI-powered customer engagement features, transforming raw data into actionable insights.
Unlike any other AI solution we’ve seen, Salesforce’s Agentforce represents a historic leap in how businesses can leverage intelligent technology to deliver personalized customer experiences, anticipate needs, and execute complex tasks autonomously. However, the saying “garbage in, garbage out” has never been more relevant as Agentforce’s ability to deliver meaningful insights and actions hinges completely on the quality and organization of its underlying data.
This is where strategic data engineering will be the catalyst for success. Professional data engineering ensures that your data architecture is optimized from the ground up–cleansing historical records, standardizing data formats, establishing robust pipelines for real-time updates, and creating seamless connections between disparate systems. Without this critical foundation, even the most sophisticated AI agents may stumble, potentially providing incomplete or inaccurate responses that could impact customer satisfaction and business operations.
By investing in comprehensive data engineering, organizations can ensure their autonomous agents have access to the high-quality, real-time data they need to make informed decisions and provide exceptional service consistently.
Keep Updated On Everything Salesforce, Data & AI
Subscribe to get the latest Salesforce blogs, guides, industry reports, events, and all things Salesforce related!
IN THE Spotlight
Cutting Churn Costs with Agentforce: A New Era for Insurance Retention.
Read Blog
Cracking the Code: What is Generative AI?
Read Blog
Einstein GPT vs ChatGPT. Read Blog
Bringing Trusted AI to Banks with Salesforce. Read Blog
How AI is Reshaping the Insurance Industry. Read Blog
Financial Forecast: Data Cloud Potential within The Financial Services Industry.
Read Blog
Exploring 5 Ways AI Empowers Financial Advisors, and the Game-Changing Impact of Salesforce’s Einstein GPT. Read Blog
Unlocking Sales Potential: The Power of Salesforce and Data Integration.
Read Blog
SALESFORCE RESOURCES
Farmers Insurance Improved Their Sales Understanding. Read Story
MetLife Increases Sales Production & Reduces Manual Work. Read Story
Igniting Innovation: Guidewire + Salesforce Integration for Insurance.
Read Guide
A Match Made in Banking: Accelerating ROI with nCino and Salesforce.
Read Guide
Navigating the Salesforce Customer Journey with an Expert Salesforce Summit Partner. View Datasheet
Salesforce for Insurance: Customer 360, Integrating with your AMS. Watch Webinar
Access Commissions
A Salesforce commissions solution for customized commission plans to best suit your organizational goals.
Access QuickBooks
A Salesforce QuickBooks solution for a seamless connection between Salesforce and QuickBooks.
Access Docs
A Salesforce document solution for streamlining work processes and efficiencies with a premier document generation features.