Everyone has been in these situations: you are waiting on a decision to move forward, the team is stuck on a decision, or, a decision gets made quickly in a room that did not include the right people, and two weeks later everyone is relitigating it.
Either way, the decision-making process failed. The hard truth, the process was not designed to produce a good decision in a reasonable amount of time.
We are in a unique position to improve this. The analytical skills, stakeholder relationships, and process thinking that define good our work are directly applicable to making organizational decision-making faster and better. Most of us are not using these skills deliberately in this way.
AI being used more is accelerating the amount of work and decisions we are working on, creates more decisions to be made, and increases the pressure to make them faster.
Understand the Decision Before the Meeting
The most important work in accelerating a decision happens before anyone gets in a room together, and before a decision is even asked for.
Start with a decision framing: what is the question that needs to be answered, who has the authority to answer it, what information is needed to answer it well, and what is the cost of delay versus the cost of a wrong answer? Getting this framing right is the difference between a productive decision process and a circular or stalled one.
We tend to be good at gathering information but less systematic about clarifying the decision itself or the decision authority. In many organizations, the person who runs the analysis is not the person who makes the decision, and the person who makes the decision is not always clear on what authority they actually have. Taking ten minutes to clarify this before a decision meeting can save two weeks of circular escalation, or stalled progress.
Separate the Types of Decisions
Not all decisions are the same, and treating them the same way is a common source of inefficiency.
Some decisions are consequential and hard to reverse. A major technology platform choice, a significant process redesign, a customer-facing policy change. These decisions warrant thorough analysis, broad stakeholder input, and careful risk assessment. Taking the time to get them right is not a failure of decision velocity. It’s appropriate judgment.
Other decisions are lower stakes or easily reversible. A test approach for a feature, a prioritization call for the next sprint, a draft of a requirements artifact for stakeholder review. These decisions are often delayed far longer than their consequence warrants. The team seeks consensus they do not need, escalates to leadership who should not be involved, and waits for certainty that is not available.
When we can help a team calibrate the level of rigor appropriate for a given decision, we are providing significant value. Not every decision needs a week of analysis and a steering committee review.
Get The Decision Defined Well
One of the most common failure points in the decision making process is that the decision itself is poorly defined. When it is poorly defined, a decision maker may not feel the urgency to take action and actually make a decision or may not see it as their decision to make. So, they may not make a decision at all, and may not even be aware you or the team are waiting on them.
In group settings vague or poorly defined decisions become circular conversations and opinions as the group struggles to focus on what the decision is actually all about, but this issue is rarely surfaced early enough and difficult dynamics set in making the process painful.
Build Evidence Packages, Not Data Dumps
One of the most common ways that we slow down decisions is by presenting too much information rather than the right information.
A thorough analysis is not the same as a useful evidence package. An evidence package is specifically designed to enable a decision: it presents the question being answered, the options considered, the relevant evidence for each option, a recommendation with rationale, and the risks of each path. It is calibrated to the decision-maker’s level of familiarity with the topic and contains what they need to decide, nothing more.
Building an evidence package well requires us to have done the analytical work to form a view. It is not a neutral data presentation. It is a recommendation supported by evidence. Many analysts are uncomfortable with this level of directness. In a fast-moving environment, the unwillingness to form and communicate a recommendation is itself a source of delay.
Facilitate Alignment, Not Consensus
Alignment and consensus are different things, and conflating them is a major source of slow decision-making.
Consensus means everyone agrees. For most significant decisions, consensus is not achievable and the pursuit of it creates delay and drift. Someone will always have a different perspective, a different risk tolerance, or a different reading of the evidence.
Alignment means everyone understands the decision, understands why it was made, and is prepared to act on it even if they would have decided differently. Alignment is achievable and sufficient. It requires that the people who need to act on the decision trust that the process was fair, the key concerns were heard, and the decision-maker was the right person to make the call.
We can facilitate alignment effectively by making sure concerns are surfaced and addressed before the decision meeting, by being transparent about the trade-offs in the recommendation, and by clearly communicating who has the decision authority so that the people who do not have it feel heard rather than overruled.
Use AI to Accelerate the Analysis, Not Replace It
AI tools can significantly compress the time required to build an evidence package. Synthesizing background information, generating option lists, modeling implications, drafting stakeholder communications, all of these tasks can move faster with AI assistance.
The caution is the same one that applies everywhere AI is used in our work: the analytical judgment that gives an evidence package its value cannot be automated. We still need to evaluate whether the AI-generated options are the right ones, whether the evidence is reliable and complete, and whether the recommendation is defensible given the organizational context.
AI-assisted evidence packages are faster to produce. They are only better if we apply rigorous analytical judgment to what the AI produces.
If you want to develop your facilitation and decision-making skills alongside your AI capabilities, my Maven courses cover all of it. We work on the practical skills that make BAs more effective in fast-moving, AI-accelerated environments. Join me at maven.com/angela-wick.