AI can reduce the effort behind a B2B campaign while leaving the customer’s sales team uncertain about whom to approach. For intelligence providers, that creates a commercial problem: cheaper execution makes output easier to reproduce, but clients still need help deciding where to focus. The business opportunity lies in connecting data, delivery, and sales feedback into a system that improves those decisions.
Anand founded XDBS in 2012 and expanded it to more than 2,500 people, operating across the Middle East, India, the United States, South Africa, and Costa Rica. He says clients increasingly expect a visible link between purchased services and commercial outcomes. At that scale, meeting the expectation requires feedback to reach the teams responsible for targeting and delivery.
Connect Campaign Delivery to the Sales Workflow
A correct contact record establishes that a person can be reached. It doesn’t establish a relevant need, purchasing authority, or suitable timing. Anand distinguishes verified information from intelligence that helps a client recognize a worthwhile opportunity. Providers need to build their workflow around that difference.
Campaign delivery therefore needs a return path. Anand says the difficult operational shift is understanding what customers do after receiving the work, which requires closer feedback from their sales organizations. Without that connection, a provider can confirm completion while learning little about whether its targeting helped.
That feedback also needs enough detail to guide a correction. An unsuccessful approach might reflect poor audience selection or interest that proved weaker than expected. Anand argues that teams should investigate disappointing campaigns and apply what they learn. Increasing activity before identifying the failure risks repeating the same expense.
Responsibility has limits. A provider can own the standard of its intelligence, but it cannot set the customer’s prices or guarantee effective follow-up. Anand says those boundaries should be explicit without allowing them to become a reason to disregard weak results. The practical requirement is shared visibility into what happened after handover.
| Operating question | What the provider needs to establish |
|---|---|
| Was the audience relevant? | Whether targeting matched a genuine customer need |
| Did the intelligence support action? | How the sales team used the information |
| Where did progress stop? | Whether the issue involved delivered quality or factors outside the provider’s control |
| What should change? | Which findings can improve the next assignment |
The table describes a review discipline, rather than a guarantee of conversion. Its value is making the next decision more informed. For a business selling intelligence, that learning cycle can strengthen the client relationship beyond a campaign’s delivery commitment.
Build Differentiation Into the Information System
Buying AI tools does not settle the question of competitive advantage. Anand expects competitors to gain access to many of the same capabilities. Faster production may improve an operation, but speed alone offers limited protection when another supplier can purchase similar technology.
He places more weight on the information supporting those tools and on understanding the customer’s situation. In an account-assessment workflow, the useful question is whether a recommendation reflects a relevant problem and a credible reason to engage. A role and employer provide context; they do not complete the commercial judgment.
Proprietary platforms face the same test. Anand says ownership only becomes meaningful when customers experience better work, such as stronger audience selection or more dependable delivery. A demonstration can show a feature. Continued use must show why the feature improves the assignment.
That puts integration ahead of a standalone technology purchase. The operating model should connect customer context, campaign findings, and subsequent corrections so that learning can influence future work. Anand’s competitive test is whether clients would still find the provider hard to replace once rivals had comparable AI access.
AI may also contribute to judgment, including assessing accounts or suggesting contacts to approach. Human interpretation cannot simply be treated as a permanent advantage. Providers need to test whether the combined workflow produces better decisions after review and correction are included.
Measure the Full Workload Before Expanding a Pilot
Anand starts technology discussions with the business problem: where employees spend effort, which failures carry a high cost, and who must resolve them. That baseline gives a pilot something useful to beat. Measuring generation speed alone leaves too much of the assignment outside the calculation.
A report drafted quickly may still require source checks. An automated recommendation can create investigative work before a salesperson is ready to act. Anand warns that apparent savings shrink when a rapid output leads to substantial verification. The relevant unit of performance is the usable result, including the effort needed to get there.
A practical evaluation follows the work through deployment:
- Establish the baseline. Identify existing effort, expensive errors, and responsibility for correcting them.
- Test normal operating conditions. Include difficult assignments and count review, corrections, and implementation costs.
- Assess consistent use. Include the effort required to make the system part of everyday work.
- Assign continuing ownership. Name the person responsible for explaining results and maintaining the improvement.
Anand calls for a designated operational owner once implementation ends. That requirement closes a common gap between installing a capability and sustaining its value. Someone needs authority to address failures and responsibility for checking whether the improvement holds.
For an intelligence provider, deployment should therefore be judged across the full assignment. A tool that accelerates drafting but shifts work into checking may still be useful. Its business case simply needs to reflect the actual workload rather than the speed of the first output.
Preserve Decision Context as the Business Grows
Expansion makes the same ownership problem harder. Anand notes that early teams often retain the reasons behind commitments and exceptions in their heads. As customers and employees increase, more work passes to people who did not participate in those decisions.
Those handovers need accessible context. Otherwise, employees must reconstruct agreements or correct misunderstandings before they can deliver. Anand argues that adding staff does not resolve vague responsibility and can multiply the opportunities for confusion.
Across markets, process documentation alone is insufficient. Teams also need to know who can settle an exception and when escalation is required. Without that clarity, additional capacity can leave more employees waiting on the same small group of decision-makers.
The operational implication is straightforward: growth needs a way to preserve the reasoning behind commitments, alongside the commitments themselves. That makes delivery less dependent on individual memory and helps new teams act within understood boundaries.
Keep Employee Learning Inside the Automated Workflow
Automating preparation changes how junior employees gain experience. Anand says removing routine assignments does not automatically equip people for harder responsibilities. Development needs deliberate design, particularly when software takes over work that previously exposed staff to evidence and customer requirements.
Consider his report-writing example. AI can assist with a draft, but the employee still needs to understand the figures and spot explanations that conflict with the evidence. Reviewing only the completed document can hide weaknesses in the reasoning that produced it.
Managers therefore need to discuss assumptions and show how to investigate discrepancies. Exposure to customer conversations also helps employees understand the decisions their work supports. Anand stresses that this requires management time; technology does not eliminate the teaching responsibility.
He would assess development through the responsibility an employee can manage: recognizing a problem, supporting a recommendation, and knowing when help is necessary. For clients, those capabilities affect whether intelligence arrives with a usable explanation and an honest account of uncertainty.
As AI makes routine execution easier to reproduce, B2B providers need a stronger connection between their technology stack and the customer’s next action. Feedback, accountable deployment, and trained judgment make that connection practical. Investing in those operating capabilities now gives providers a clearer basis for improving decisions and demonstrating value beyond completed output.
