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At least 30% of GenAI projects will be abandoned after the proof-of-concept stage due to poor data quality, insufficient risk controls, escalating development and deployment costs, or unclear business value in 2025, according to Gartner. These challenges limit GenAI’s potential to deliver personalized, contextual insights for enterprise users.
The key to personalizing GenAI outputs and maximizing its productivity lies in improving data quality and retrieval methods. High-quality, well-structured data enables AI systems to generate accurate and tailored outputs that meet your organization’s unique and nuanced needs. In this piece, I’ll talk about why that’s the case and discuss ways you can implement these changes at your organization.
Personalized AI Meets Nuanced Needs
As organizations struggle to adopt GenAI, the critical question remains: how can AI tools deliver better outputs that clearly impact productivity?
Once you understand that AI tools should cater to the specific needs of various personas within an organization, you'll start to create stronger value and outputs with AI. For example, a sales team member using an AI tool has vastly different needs than a human resources manager, a finance head, a mergers and acquisitions (M&A) lawyer, or a partner management lead. While these roles might be operating together in the same organization, using the same AI tool, each persona requires tailored information and recommendations that align with their unique roles and objectives.
Enter the specialized AI agent. AI agents are AI systems designed to perform specific tasks or functions, tailoring responses to the roles within an organization or a particular domain or industry, such as customer service, medical diagnosis or financial analysis. When deployed correctly, agents provide more accurate, relevant and targeted information that generalized AI models are unable to deliver. But to be fully effective, agents need a bedrock of well-optimized data.
The Data Difference
When an AI agent draws from well-organized, optimal data, it acts almost like a subject matter expert in a particular field, dispensing highly-personalized and specialized advice to help expert-level professionals work more efficiently. However, if the information powering an agent is mislabelled, irrelevant, outdated or inappropriate for the use case, the agent will sound less like a subject matter expert and more like a generalized chatbot (or worse), dispensing general and non-personalized advice.
Microsoft’s SharePoint agents, for example, draw from a bedrock of enterprise-specific proprietary data. Rather than providing generalized advice, these agents offer recommendations in Word, PowerPoint and other applications at the level of a subject matter expert. Crucially, these agents also draw directly from data in your organization’s SharePoint environment, which means they have the reasoning power and contextual knowledge to provide highly actionable, personalized recommendations.
Imagine that you’re a software sales representative who can always speak directly with a senior software engineer at your company, directly in a SharePoint document — that's the sort of expert-level, contextual advice that well-trained agents are able to provide. But remember that agents trained on poorly-governed data will be much less effective.
As more companies turn to agents to generate more effective, personalized recommendations with AI, they can’t forget the “garbage in, garbage out” rule: GenAI is only as strong as the data that powers it. This is one of the reasons why Forrester identifies data quality as the primary barrier to B2B GenAI adoption, underscoring that poor data quality can lead to inaccurate insights and ineffective AI use.
How to Optimize Data for Successful Deployment of Personalized AI
Follow these three steps to help your organization personalize AI recommendations and improve the productivity of AI tools.
1. Centralize Your Data
A critical early step in GenAI adoption is to consolidate data from legacy sources into a central repository using cloud platforms. A central repository for data ensures AI agents and other AI tools can process and consume your data efficiently. Master data management creates a single version of truth, which helps resolve inconsistencies and enhances data reliability. The result is more personalized recommendations. Centralized storage not only improves data accessibility but also streamlines data governance, making it easier to maintain data quality and integrity.
2. Secure Your Data
AI implementation raises new risks and vulnerabilities. To meet this challenge head-on, craft a data governance strategy to protect your entire data estate from overexposure. When crafting this strategy, use a methodology that validates and enhances data integrity by enforcing content policies and securing access to critical data. It’s also important to remove any workspaces that haven't been accessed in a significant amount of time, confirm all governance details, and validate or remove permissions on data across this entire estate. This allows you to effectively govern and secure your data as you get ready to introduce AI tools, which will significantly limit risks.
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3. Implement Information Lifecycle Management
A final, critical part of the process is to establish a system to remove redundant, obsolete or trivial (ROT) data. Quality data ensures that AI agents and other AI tools generate the most current and valuable insights, and enhances decision-making and improves the output of AI tools. By systematically managing data from creation to disposal, organizations can ensure that their AI systems are always working with the best possible data, leading to more precise and actionable insights.
Sharpen Your Competitive Edge with Personalized AI
Personalized AI is no longer a luxury but a necessity for driving innovation and efficiency. By optimizing data ecosystems, organizations can unleash the full potential of AI to deliver tailored insights that meet unique business needs. As AI continues to evolve, the role of high-quality data will only grow more critical. By prioritizing data quality and governance, businesses can ensure their AI investments yield clear, tangible value, positioning them for success in a future where personalized AI is the competitive edge.
Editor's Note: Read on about related questions around AI in the workplace:
- Big Tech Bets Billions on GenAI, But Adoption Is Slow — Companies like Microsoft, Google and Salesforce are betting the house on generative AI, yet adoption rates lag far behind the investment. Here's why.
- Why Clean Data Is Foundational for Effective AI — The adage garbage in, garbage out is more relevant than ever as we look to train AI models on organizational data. Here's how to take out the trash.
- Artificial Intelligence Trends in the Intranet and Employee Experience Platform Space — Just because an intranet has an AI feature doesn’t mean it’s right for you. Consider how and where you want to use AI, then vet the platforms.
