Generative artificial intelligence (AI) is changing how businesses approach sales, marketing, and revenue operations (RevOps). Yet, some organizations struggle to harness its full potential. Concerns about data quality, integration, and skills gaps often slow adoption.
Fortunately, proper implementation can improve data accuracy, seamless integration, and practical adoption across teams to deliver meaningful impact.
Understanding Generative AI in B2B
Generative AI generates new insights, content, and responses in real time. In B2B sales and RevOps, this means moving past static reports and rigid sales playbooks. Generative AI automatically reveals trends, risks, and opportunities so teams can act proactively.
For sales teams, AI-powered insights provide real-time visibility into the health of a pipeline. It assesses emails, calls, customer relationship management (CRM) updates, and historical patterns to flag potential risks and suggest the best next steps. Sales teams no longer need to rely solely on gut instincts or inconsistent data entry they have AI-driven recommendations that guide them toward win rates.
For RevOps leaders, generative AI streamlines revenue forecasting. It allows them to gain data-driven predictions that adjust as new information comes in. They can make strategic decisions with confidence, knowing their forecasts are based on the most accurate insights available.
Identifying Generative AI Challenges
The promise of AI in B2B is exciting, but the reality of implementation presents some hurdles. Organizations may invest in tools only to find that they encounter the following challenges.
AI Implementation
Implementing generative AI requires careful planning, customization, and ongoing management. Many businesses underestimate the level of effort needed to prepare their data, align AI outputs with existing workflows, and ensure teams know how to use the technology.
AI is only valuable when it's seamlessly embedded into daily operations. If sales teams struggle to interpret generated insights or marketing teams don't know how to use content personalization tools, adoption rates plummet, and investments fail to deliver a meaningful return on investment.
Data Privacy and Security
Generative AI thrives on data. However, these details typically include sensitive customer information and confidential sales strategies. For companies in highly regulated industries with strict compliance requirements, this makes adopting the tool even more complex.
The concern is about storing AI data securely and controlling who has access to it and how it's used. Some AI tools operate in black-box environments, making it difficult for businesses to fully understand how their information is being processed. Without clear governance policies and secure models, businesses risk exposing themselves to compliance violations and data leaks.
Integration With Existing Systems
For AI to work, it needs access to data from CRM platforms, email exchanges, and marketing automation tools. However, many businesses operate with data locked in disconnected silos. Without seamless integration, AI-generated insights become unreliable. If a model is pulling incomplete or outdated data from a CRM, its sales forecast will be inaccurate. Companies that fail to address these integration challenges may struggle to see tangible results from AI investments.
Skills Gaps
Without proper training, AI tools can feel intimidating or complex. Sales teams might not know how to interpret deal risk assessments, marketing teams may struggle to optimize content, and RevOps leaders may find it difficult to fine-tune forecasting models.
Without the right training and support, even the most advanced models can become underutilized. A skills gap may exist because of the following:
- AI is perceived as complex
- There's a lack of training and education
- There's a misalignment between AI capabilities and business needs
- The team fears job displacement
It's also important to recognize that AI can find trends but doesn't understand what might be driving those trends. A wary eye is critical to interpreting what AI says and deciding how to use that information going forward. Teams should be trained to interpret AI outputs with skepticism.
Data Quality Issues
Data quality is typically the difference between hitting targets and falling short. However, organizations may struggle with a few of these issues:
- Inconsistent data: When data isn't standardized across sales and marketing platforms, discrepancies arise. The two systems have different jargon for how they describe something, so merging their perspectives becomes necessary. These inconsistencies make it difficult for AI to generate accurate insights.
- Inaccurate or outdated data: If a company's CRM contains outdated customer contact details, outreach may target the wrong people. If old records aren't updated, AI-generated sales predictions won't reflect the latest trends.
- Irrelevant data: If systems are cluttered with unnecessary or duplicate records, AI models may generate misleading conclusions. Without a way to filter and prioritize relevant data, insights lose their strategic value.
How a RevOps Platform Can Help
RevOps platforms like Clari eliminate data roadblocks by providing a unified, AI-powered revenue solution that guarantees accuracy, completeness, and consistency. Here's how it can help teams capture, clean, and validate information to improve decision-making:
- Automated data capture: Clari Capture automatically pulls in data from emails, calls, and meetings to ensure no valuable customer interactions are lost.
- Data inspection and validation: Clari Inspect identifies gaps, inconsistencies, and potential risks. This allows teams to correct inaccuracies before they impact forecasting or sales strategy.
- Integration with existing systems: Clari connects with existing customer relationship management software and other platforms to keep data flowing across systems. This eliminates silos and gives AI access to a comprehensive dataset.
- AI-powered forecasting: By combining real-time data with historical trends, Clari Forecasting provides accurate revenue predictions.
AI requires adoption and trust from teams across an organization. Clari removes these barriers by making AI accessible and intuitive in the following ways:
- Built-in automation: The platform automates repetitive tasks, which eliminates the need for teams to manually interpret complex data. Forecasting generates precise predictions without requiring deep analysis skills.
- Comprehensive training and resources: Clari provides ongoing education through webinars and downloads so teams know how to use AI in their workflows. Additionally, the online community offers peer-to-peer support, best practices, and success stories.
Why Trust Us to Address Challenges With AI?
Clari helps businesses run revenue with precision and predictability. Our platform simplifies AI adoption, data capture and validation, and sales execution so teams can operate with confidence. Leading companies trust us to improve forecast accuracy and drive growth. Our solution cleans and structures your data, automates workflows, and provides real-time, actionable insights.
We also integrate seamlessly with your existing system to eliminate data silos and ensure every team has access to reliable, up-to-date revenue intelligence. Our customer success team provides training resources to make the model work for you. Teams also have access to community support so they can benefit from revenue orchestration, even if they're not AI experts.
Implement AI in Business With Clari
Generative AI is transforming revenue operations, but success depends on the right platform. Clari's all-in-one system helps automate insights and improve forecast accuracy. Request a demo today to see Clari in action.