Decision intelligence brings data, models, business rules, and human judgment into a decision process. It starts with a practical question: what choice needs to be made, and what evidence would help?
The output may be a recommendation, a forecast, a comparison of options, or a question for further investigation. The useful result is a better-supported decision, with a way to review what happened afterward.
How it relates to business intelligence
| What to compare | Reporting and BI capabilities | Decision-support capabilities |
|---|---|---|
| Starting question | What happened, and where should we investigate? | What should we do, given the evidence and constraints? |
| Possible output | Report, alert, diagnostic analysis, or recommendation | Options, recommendation, assumptions, and review steps |
| Illustrative campaign review | Cost per lead increased during the selected period | Check tracking, conversion delay, creative, and auction changes before choosing an action |
| Human responsibility | Validate definitions and interpret the analysis | Review evidence, authorize the action, and assess the outcome |
| What to verify | Source coverage, refresh schedule, and diagnostic features | Evidence quality, uncertainty, permissions, and follow-through |
Business intelligence helps teams report, explore, and understand performance. Many BI products also offer alerts, diagnostics, and recommendations. Decision intelligence emphasizes the process around a choice: the evidence, options, constraints, owner, and outcome.
These capabilities overlap. A product label does not prove that one tool explains causes while another only draws charts. Compare what each system actually does with your data and how a person reviews its output.
Start with the decision
Consider a campaign whose cost per lead has increased. The team needs to decide whether to investigate, change the creative, adjust targeting, or wait for more results. The cost change alone does not determine the right action.
A useful review would include the campaign objective, reporting period, conversion delay, and relevant changes to the account. It would separate observed facts from possible explanations and show which questions remain open.
The recommendation might be to test a different opening after reviewing the creative and audience response. It might also be to fix tracking or gather more data. Further investigation can be a valid next step.
Bring together the evidence the decision needs
Identify the relevant records and their owners. Check definitions, timestamps, permissions, and missing fields before combining numbers from different sources. More connected systems do not automatically produce a more reliable answer.
Use historical examples carefully. An earlier campaign or deal can suggest a pattern worth exploring, but differences in audience, timing, price, or market conditions can change its relevance.
Keep prediction separate from causation
A model may predict an outcome or find variables that move together. That does not establish why the outcome occurred. A causal explanation needs evidence and a method appropriate to the question.
Ask which alternatives were considered and what would disprove the explanation. Where an experiment is feasible, define the comparison and success measure before testing. Where it is not, make the limits of the evidence clear.
Make the recommendation reviewable
Show what is proposed, why it is proposed, and what could go wrong. Include the evidence needed to challenge the recommendation. Give the decision to someone with the authority and relevant knowledge to assess it.
If the workflow can change a connected system, confirm the permissions and approval process. Read access, a prepared draft, and authorization to execute are separate things. The product should make those boundaries clear.
Learn from the outcome
Record the original decision and its assumptions. After the action, compare the result with what the team expected. Account for other changes that may have influenced the outcome.
This record can help later reviews, especially when a similar question returns or a different person takes ownership. It should remain open to correction as the business learns more.
What to ask before choosing a tool
Ask the vendor to work through a question like yours. Check source coverage, data freshness, explanations, permissions, and exception handling. Include the work needed to maintain the setup when accounts or definitions change.
Assess effort and decision quality together. A faster recommendation is useful only if your team can understand it, check it, and act responsibly. Compare the workflow with your current process before claiming an improvement.
How Alfred applies this approach
Alfred brings campaign and receivables information into analysis, briefs, and recommendations for human review. Marketing has published plans. Finance begins with a consultation about your process and tools. Sales is in development.
Start with a campaign question or an overdue account. Review the evidence Alfred uses, the recommendation it prepares, and the work it saves your team. That provides a concrete basis for deciding whether it fits.