5 AI usage best practices to get more value from every prompt

AI adoption is accelerating across the enterprise. Gartner forecasts worldwide AI spending will reach $2.59 trillion in 2026, up 47% year over year, as organizations expand their use of GenAI models and agents across more workflows.

As that investment grows, so does the pressure to turn AI usage into meaningful business value.

McKinsey’s 2026 State of AI survey found that one in five respondents say AI-related operating costs, including token costs, have constrained their organization’s AI use. At the same time, 60% expect their organizations to increase AI investment over the next year.

For go-to-market (GTM) teams, AI can support everything from understanding buying activity and researching accounts to preparing for customer conversations and identifying where to focus next. But as more people incorporate AI into their work, inconsistent prompting, unnecessary retries, and poorly matched tools can drive up usage without improving the outcome.

Getting more value from AI starts with better habits around how teams use it. That means giving people proven starting points, matching the right tools to the work, and connecting AI usage back to the outcomes that matter.

Here are five ways to start.

1. Give teams a proven starting point for common GTM workflows

Starting with a blank prompt box puts the burden on every employee to figure out what to ask, what context to include, and how to structure the request.

The results can vary widely. One person may get a useful answer on the first try. Another may paste in unnecessary information, rewrite the prompt several times, and still struggle to get something they can use.

Ready-to-use prompts give teams a stronger starting point, with the instructions, context, and structure needed for recurring work while still giving people room to customize the request for the situation.

For GTM teams, that means starting with the questions they already need to answer. Which accounts are showing surging intent? Where are there gaps in buying group coverage? Which opportunities have stalled? What activity tends to precede closed-won deals?

The Demandbase AI Prompt Library organizes ready-to-use prompts around common GTM workflows like these. Teams can browse by workflow, role, or capability, find a prompt that fits the job, customize the inputs, and use it with Demandbase AI. Instead of figuring out how to structure every request from scratch, they can focus on what the answer tells them and what to do next.

2. Match the model to the work

Different AI tasks require different levels of reasoning and computing power.

Synthesizing information from multiple sources or working through a complex strategic question may call for a more capable reasoning model. Summarizing notes, reformatting information, or handling another straightforward task may require less.

That distinction matters as AI usage scales. Gartner expects enterprises to expand their use of GenAI models and AI agents across multiple workflows, with model consumption increasing as those processes become more complex.

Give employees simple guidance about which tools and models are best suited to the work they do most often. Ground that guidance in real workflows so people can make informed choices without needing to understand the technical details behind every model.

More intentional choices help organizations put AI resources where they’ll have the most value.

3. Make better prompting practices easier to follow

Prompt quality affects both the usefulness of the response and the resources required to produce it.

Long inputs, unnecessary attachments, repeated retries, and overly broad requests can all increase AI usage. Cutting too much context can leave the model without the information it needs to provide a useful answer.

The goal is to give AI the right context for the job.

A strong prompt should make the task clear, provide the information needed to complete it, and define what a useful response looks like. For GTM work, that might mean specifying an account, journey stage, time period, segment, or other criteria that help narrow the question.

Reusable prompts make those practices easier to apply consistently. For example, a seller trying to identify stalled opportunities shouldn’t have to remember every signal worth examining. A well-structured prompt can already ask for engagement trends, buying group activity, intent signals, and other relevant context, then use that information to surface a recommended next step.

Teams can customize the inputs for the situation while keeping the structure that makes the prompt useful.

4. Set AI access and usage guidelines around real roles

AI usage looks different across a GTM organization.

A seller preparing for an account conversation has different needs from a marketer analyzing campaign performance or a RevOps team examining pipeline trends. Those differences should inform how organizations think about AI access, tools, and usage guidelines.

Start with the workflows each team relies on most. Look at which roles use AI frequently, where more advanced capabilities add value, and which everyday tasks can be handled with simpler tools or models.

Usage patterns can help leaders see where AI is becoming part of the GTM motion and where additional investment may make sense. If certain workflows consistently require more capacity, that context can guide decisions about where to expand access or resources.

Clear guidelines can also give teams room to experiment while keeping visibility into where usage is growing and what they’re accomplishing with it.

5. Connect AI usage to business outcomes

Knowing what you’re spending on AI is useful. Knowing what that investment is helping your teams accomplish matters even more.

McKinsey’s 2026 research found that 80% of respondents say AI has improved their individual productivity, while 37% report that AI has contributed positively to their organization’s EBIT. AI high performers are also more likely to redesign workflows around AI and have defined processes for measuring the impact of their initiatives.

For GTM teams, that means connecting AI usage to the work and decisions it supports. Look at where AI is helping teams understand account activity, uncover buying group gaps, prioritize opportunities, prepare for customer conversations, or identify the next best action. Then assess whether those workflows are helping teams make better decisions and move opportunities forward.

That same discipline can surface investments that aren’t producing enough value. Emergn’s 2026 Value Gap research found that the average enterprise loses 2.4% of annual revenue to transformation and AI work that should have been stopped sooner.

Reviewing usage alongside outcomes gives leaders a clearer basis for deciding what to expand, refine, or stop.

Build better AI habits as usage grows

AI adoption is moving quickly, but adoption alone doesn’t create business value. The way teams incorporate AI into their work matters.

McKinsey found that nearly three-quarters of AI high performers have fundamentally redesigned workflows because of their AI use, compared with just one-quarter of other respondents. They’re also more likely to have defined processes for measuring the impact of AI initiatives.

For GTM teams, building better AI workflows can start with the work people already do. Create repeatable ways to use AI for common GTM questions, then pay attention to which workflows help people make better decisions and take action faster.

As those practices become repeatable, AI becomes a more useful part of everyday GTM execution.

Start with prompts built for B2B GTM

The right prompt can help your team get from a GTM question to useful insight faster.

The Demandbase AI Prompt Library gives teams ready-to-use prompts for common GTM workflows across the customer journey. Browse by workflow, role, or capability, copy the prompt that fits your goal, and customize it for the job at hand.

Use Demandbase AI to uncover insights, prioritize the right accounts, and move opportunities forward.