The $30 Billion AI Opportunity

As a business leader, you probably hear about AI at every turn. But here's what many executives miss: according to HubSpot’s sponsored IDC report, AI's transformative potential "hinges on access to clean, unified data. 

Simply put, if your customer data is fragmented, siloed, or poor quality, even the most sophisticated AI initiative will fall flat.

The Data Challenges Holding Your Business Back

Many organisations struggle with four fundamental data issues that stall AI projects before they can deliver real value:

• Data silos: Key information often gets trapped in separate systems for sales, marketing, and service. When data is fragmented, it’s impossible to maintain a reliable “single source of truth” (reference: IDC Whitepaper). AI then lacks a holistic view, leading to flawed or biased predictions.
• Poor data quality: If your CRM is filled with duplicates, missing fields, or inconsistent formatting, even the best AI tool won’t generate useful insights. As IDC’s analysis of HubSpot’s ecosystem report points out, “operational tasks like data cleansing and deduplication are critical prerequisites for successful AI implementation.” In other words, bad data inevitably leads to bad and incomplete results.
• Integration problems: Having a modern CRM doesn’t guarantee it’s properly connected to your ERP, marketing automation, or support systems. This fragmentation is one of the top barriers to moving AI projects from pilot to production. It’s hard for AI to add value when data lives in multiple places, which is why over a third of companies in IDC’s recent survey cited integration challenges as a major obstacle.
• Unstructured data blindness: More than 80% of customer data is unstructured found in emails, call transcripts, and support chat logs. Traditional CRMs capture mostly structured data (names, dates, click events) and ignore these richer insights. If you’re not leveraging unstructured data, you’re overlooking the majority of your customer interactions. Modern AI can analyse text or voice to detect sentiment or intent, but only if you capture and integrate this data into your systems.

Five Practical Steps to Build Your AI-Ready Data Foundation

The good news? You can address these challenges with strategic data management:

1. Centralize your customer data: Establish a single source of truth where all customer information flows, eliminating duplicates and inconsistencies. Ensure sales, marketing, support, and operations data flow into one CRM or data lake. When all your teams rely on the same up-to-date data, you eliminate data chaos. AI analytics and reports will draw from a complete customer view rather than a fragmented one.
   If you don’t have an effective CRM in place, now is the time to change to a smart, AI-powered platform with a flexible API infrastructure. HubSpot excels over other CRMs in this space. It’s AI-powered, unified customer data platform offers businesses a powerful and efficient solution—making it a compelling choice for those looking to streamline their operations. 
2. Break down system silos: Invest in connecting your CRM with other key systems (ERP, marketing automation, customer service platforms, etc.). By integrating core systems, you ensure that data changes in one system are reflected everywhere. As the IDC whitepaper emphasizes, "tightly integrated applications and core systems are indispensable for AI success." You might need some upfront effort from IT or partners to set up these integrations, but it pays off by giving your AI consistent data across the board.
3. Implement continuous data governance: Don't wait for an AI project to clean your data. Make it routine with standardized data entry rules and regular audits to maintain quality. The investment in internal resources or an outsourced partner to take care of this for you consistently, will pay dividends in the long term.
   You’d be surprised at how many companies see this as a negative cost and neglect it, yet the competitive advantage that clean data brings is a return much larger than the cost to keep it in check. High-quality data is the bedrock of machine learning. By ensuring accuracy and consistency, you make any AI analysis far more reliable.
4. Leverage unstructured data with AI tools: Make it a goal to capture and use unstructured data. Modern CRMs, like HubSpot, or AI add-ons can log emails, record calls, and store support tickets attached to customer profiles.
   As an example, you could transcribe sales calls and leverage the text into your CRM. This way, when you apply AI, it can “hear” what customers are saying, not just see what they clicked. HubSpot’s recent moves (like acquiring an AI company to analyze support conversations) underline how valuable unstructured data is becoming.
   By tapping into hidden insights such as the tone of an email or the content of a service call, your AI predictions (like identifying an unhappy customer) become much sharper.
5. Align AI projects with clear ROI goals: A final tactical tip goes beyond data itself – but it’s important for getting support. One big challenge companies face is demonstrating ROI for AI initiatives (reference: IDC Whitepaper).
   To avoid this, define early what business outcome you expect (e.g. “reduce customer churn by 10% in one year through AI-driven predictive alerts”). Use your cleaned, integrated data to create baseline metrics, then track improvements once AI is in play.
   When executives and teams see concrete results tied to dollars or efficiency, they’re more likely to support further investments in data infrastructure. In short, use your data to prove AI’s value.

Real Results from Unified Data 

Consider this example:  

An industrial equipment supplier is struggling with disconnected systems—sales use a CRM while maintenance services operate from a separate ERP or field service system. By integrating service logs into the CRM and cleansing duplicate records, they created a unified customer view that powered an AI model for churn prediction.

They cleansed the data (removing duplicate entries for the same client’s multiple sites) and fed all of it into an AI model for churn prediction.  

The result?  

Sales representatives received automated alerts when high-value clients had multiple unresolved service issues—a reliable predictor of customer churn. This simple fix of integrating and cleaning data led to a 15% improvement in renewal rates within just one year. All because AI was working with complete information.

The Competitive Advantage of AI-Ready Data 

Every B2B organization today has access to the same raw ingredient that powers giants like Amazon and Google: data. The difference is how well you refine that ingredient.

Remember: AI is only as good as the data behind it. By cleaning your data, connecting your systems, and taking a strategic approach to data management, you transform data challenges into actionable business intelligence and gain a significant competitive edge. 

Many companies struggle with fragmented customer data. It’s common to have sales, marketing, and service each using different tools where one team uses a different spreadsheet, and the other uses another data source. IDC’s analysis shows data cleansing, data pipeline automation and Agent/assistant design as the top three challenges in getting AI ready. 

Ready to Act? 

Start by evaluating your CRM and data integration gaps. Prioritize one fix at a time, maybe beginning with merging duplicate contact records or linking your CRM with your support ticket system.

The sooner you turn disparate data into a cohesive asset, the sooner you’ll harness AI to drive smarter decisions. For personalized guidance on creating an AI-ready data foundation that delivers real business value, explore Fileroom's integrated data management solutions.

The companies that optimize their data today will be the ones leading with AI tomorrow.