Key takeaways
- Winning with agentic AI depends on giving agents reliable, relevant data for the job.
- Original research adds context about your audience that clicks and campaign results cannot provide on their own.
- Start with one useful task, check the results and decide where human approval is needed.
You wouldn’t brief a new colleague with an outdated customer survey, three conflicting spreadsheets and a vague instruction to “improve engagement.” An AI agent needs a better start, too.
When an agent recommends a topic or prepares a distribution plan, the information it uses shapes its decisions. If that information is incomplete, it can carry the same mistake through several steps before anyone notices.
Winning with agentic AI starts with checking what you are giving it to work with.
What is agentic AI?
Agentic AI can carry out several steps toward a goal, using connected tools and working within rules you set.
You might ask an agent to prepare a distribution plan for a research report. It could review approved audience findings, match them to relevant content and suggest channels, then submit the plan for approval.
That sounds useful when your team has a report to launch and a calendar that is already full. But the agent still needs to know who the report is for, what those people care about and which information it is allowed to use.
Give the agent a clear job and the right information
Salesforce’s State of Agentic AI in the Enterprise research surveyed more than 2,000 AI decision-makers. In its accompanying analysis, clean, accessible data and a narrowly defined use case were leading success factors, each cited by 36% of deployed organizations.
Before connecting an agent to your content library, choose the task you want it to handle. “Recommend three relevant assets for finance leaders evaluating a purchase” gives you a clearer starting point than “help with our marketing.”
Then check the information that task requires. Are the audience findings current? Are the assets approved? Can the agent identify which research supports each recommendation?
Dates and definitions matter here. A survey of IT buyers from two years ago does not establish what finance leaders need today. An agent needs enough context to recognize that difference.
Your audience deserves more than an educated guess
Campaign results are useful, but a click leaves plenty unanswered. Someone might open a report because they are researching a purchase, helping a colleague or simply interested in the subject.
Original research lets you ask more direct questions about their priorities, concerns and decisions. Interviews can add detail that a survey response alone misses.
For an agent recommending thought leadership topics, that evidence provides something concrete to work from. It helps your team assess whether a proposed topic addresses an audience need, rather than repeating whichever subjects dominate your content library.
Our Thought Leadership in Practice 2026 report, based on 1,000 practitioners, found that 53% use AI in research. The findings underline how relevant questions about evidence have become to everyday thought leadership work.
B2B AI transformation brings those questions into more workflows. When agents help turn audience information into recommendations, the quality of the research deserves attention before the recommendations reach your campaign plan.
What does your audience need you to understand?
Build a stronger evidence base for your content and marketing decisions with original research.
Start small enough to check the work
You do not have to sort out every database before getting started.
Salesforce reports that organizations integrating data iteratively reached ROI in 8.2 months, compared with 7.3 months for those that fully unified their data first.
A practical starting point is one task, one audience and a set of approved sources. Ask the agent to prepare content recommendations, then have someone review whether they fit the audience and accurately reflect the research.
Include that review time when assessing the result. A draft produced in seconds is less useful if correcting it takes the afternoon.
This gives B2B AI transformation a practical test: does the agent help your team complete a worthwhile task with an acceptable level of checking?
Decide who can approve what
Our Thought Leadership in Practice 2026 research found that 64% of respondents have a formal policy governing AI use in thought leadership.
An agent needs working rules as well. Decide which research it can access, whether it can use confidential information and which actions need approval.
For example, preparing an internal distribution plan and publishing it are separate responsibilities. Set that boundary before the agent starts working.
Winning with agentic AI starts with what you know
Before handing an agent more work, check whether it has the evidence to do that work well. Clear audience insights and reliable data give it a stronger starting point, and give your team a basis for judging its recommendations.
If your audience research needs a refresh, start there. iResearch Services helps you understand decision-maker priorities and turn those findings into informed marketing decisions.
FAQ
Data informs an agent’s decisions and actions. Outdated or poorly labeled information can lead it to make recommendations based on the wrong audience or findings.
Generative AI produces content, such as text or images. Agentic AI can use it alongside tools to complete several steps toward a goal.
Prepare the information needed for your chosen task. Content recommendations might require audience research, approved assets and relevant performance data.
No. Research supplies evidence. Results also depend on the task, how the agent uses that evidence and the judgment of the people reviewing its work.
