Data accessibility is emerging as the biggest roadblock to AI adoption in B2B marketing, according to a recent Hightouch report. While businesses are heavily investing in artificial intelligence to help them better target, personalize, and run more efficient campaigns, fragmented and inaccessible data is preventing AI from providing the full benefit it can.
As organizations are increasingly realizing the significance of AI adoption in B2B marketing, they must address data accessibility to fully leverage the potential of AI.
Tejas Manohar, Co-CEO and Co-Founder at Hightouch commented “AI is only as good as the data that feeds it”. Many marketers are excited about what AI can do, but as they find, without access to actionable data that is easy to understand, even the most progressive AI tools will not yield meaningful results.”
Data Accessibility: The Hidden Barrier to AI Adoption
Without proper data accessibility, AI adoption in B2B marketing remains a challenge that many organizations face.
Marketers must recognize that AI adoption in B2B marketing cannot succeed without reliable data sources.
Hightouch’s research, Has MarTech Failed Marketers? polled 384 marketing leaders and discovered that 75% of the challenges attributed to marketing tools have been due to poor data accessibility. Only 10% of marketers said they were successfully using AI in their campaigns, suggesting a significant gap between aspirations on AI and reality.
Key findings include:
This gap in data accessibility is a significant hurdle to AI adoption in B2B marketing.
- Less than 2% of marketers are successful in implementing one-to-one personalization at scale.
- Fewer than 1% have full AI-driven individual customer engagement.
- 95% of marketers report difficulties in identifying and targeting audiences due to fragmented data sources.
“Marketers always put the blame on MarTech stacks for underperformance,” Manohar said. “The real thing that is an issue is the quality and accessibility of the data that goes into these tools.”
Why B2B Marketers are Struggling Despite Advanced Tools
Now the B2B marketing tech landscape has more than 15,000 platforms in it. While the sophistication of the AI tools has grown, marketers report that disconnected datasets, dependence on engineering teams, and outdated systems are preventing effective use of AI.
The importance of data accessibility is crucial for successful AI adoption in B2B marketing.
In complex B2B environments, where sales cycles are long and many decision-makers are involved, the risk of having fragmented data is that you may end up with inaccurate segmentation, ineffective targeting, and revenue opportunities are missed.
AI adoption in B2B marketing is not just about technology; it’s about having the right data.
Organizations that prioritize data accessibility will see enhanced AI adoption in B2B marketing strategies.
“Speed and scale are meaningless unless they have got the data with what you can target campaigns to the right people,” Manohar added.
Impact on Campaigns, Future Target Personalization and ROI
The effects of poor accessibility of data can be felt:
- Campaigns are less targeted and yield lower engagement.
- Personalized marketing at scale remains elusive.
- ROI from AI-driven initiatives is hard to quantify.
This unified data approach is vital for effective AI adoption in B2B marketing efforts.
To maximize AI adoption in B2B marketing, brands must ensure their data infrastructures are robust.
Improving data accessibility will facilitate smoother AI adoption in B2B marketing campaigns.
Hightouch’s report reveals that marketers are pumping money into AI tools without connecting them to accessible, unified data sets and initiatives have stalled, or results have underwhelmed.
For B2B marketers, predictive targeting, insights into customer journeys and hyper-personalized campaigns can all be a promise of AI – but this can only happen if the data infrastructure is sound.
Nuts and Bolts of How You Can Harness the Power of AI for B2B Marketing
Hightouch advises practical ways of addressing these barriers:
- Audit Data Accessibility – Make sure marketing teams have access to relevant data – CRM, intent signals, account AND product usage – all these key data should be accessible to marketing teams without excessive IT support.
- Prioritize Data Activation Over Tools – To begin with, NIF should focus on unifying and cleaning of existing data sets and then look to add in new AI platforms.
- Align AI Use-Cases with Data Readiness – Implement AI solutions based on the data behind, in order to support the personalization and predictive models.
- Establish Cross-Functional Governance – Marketing, sales and IT must get on the same page with regards to data definitions, ownership and workflows.
- Measure Outcomes, Not Tools – Rather than adopting technology as tools (i.e. number of tool installations) measure pipeline growth, conversions and customer engagement.
“AI won’t be able to succeed in a vacuum,” Manohar stressed. “Organizations who aim to focus on data activation before implementing AI will dominate the market.”
The Indian B2B Marketing Scenario
In India, the issue with having fragmented data is exacerbated by regional diversity, multiple languages and multi-tiered sales cycles. Despite great interest in the adoption of AI, many Indian B2B organizations have reported that their data infrastructure isn’t ready for AI-fuelled campaigns.
In summary, the success of AI adoption in B2B marketing hinges on effective data management.
As the market evolves, AI adoption in B2B marketing will become increasingly reliant on data accessibility.
According to the surveys of the industry:
- Only 15% of Indian B2B marketers feel fully equipped to deploy AI effectively.
- 70% cite fragmented datasets as the key barrier to personalized outreach.
- Adoption of data activation platforms like CDPs and reverse ETL tools is growing but still limited to early adopters.
Brands that invest in data readiness are better positioned to take advantage of AI and to get a competitive edge with unified access and action within their organisations.
Future Outlook: Data Ready Artificial Intelligence for B2B Marketers
Looking forward, AI adoption in B2B marketing will increasingly depend on data readiness, governance, and activation rather than tool sophistication alone.
Emerging trends include:
- First-party, third-party and CRM data integration into centralized data warehouses.
- Use of reverse ETL platforms to activate data across marketing and sales channels.
- Self-service access for marketing teams to reduce dependency on IT.
- Outcome-focused metrics linking AI-driven personalization to pipeline, revenue, and retention.
Data readiness is essential for effective AI adoption in B2B marketing moving forward.
Manohar concluded “The organizations focusing on accessibility of data will be the leaders of AI driven marketing tomorrow.” It’s not about software more, it’s about software less It’s about making your data work.
Key Takeaway
AI adoption in B2B marketing is constrained by data accessibility. Hightouch research indicates that fragmented data makes it impossible to personalize, accurately target, or measure ROI. B2B marketers who unify their data sets, put data into action for AI, and establish data governance will achieve tangible business outcomes and gain a competitive edge in the market. The lesson to be taken from these changes is that investing in data readiness has a bigger impact than merely pursuing the latest AI tools.
Ultimately, the pathway to successful AI adoption in B2B marketing is paved with accessible data.