{"id":138165,"date":"2026-08-05T16:20:07","date_gmt":"2026-08-05T16:20:07","guid":{"rendered":"https:\/\/mailinvest.blog\/index.php\/2026\/08\/05\/how-to-prioritize-ai-use-cases-in-b2b-marketing\/"},"modified":"2026-08-05T16:22:31","modified_gmt":"2026-08-05T16:22:31","slug":"how-to-prioritize-ai-use-cases-in-b2b-marketing","status":"publish","type":"post","link":"https:\/\/mailinvest.blog\/index.php\/2026\/08\/05\/how-to-prioritize-ai-use-cases-in-b2b-marketing\/","title":{"rendered":"How to Prioritize AI Use Cases in B2B Marketing"},"content":{"rendered":"

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Abstract<\/h2>\n

Use a 3-part framework to guage use instances for AI in B2B advertising, after which prioritize based mostly on the place they fall in that spectrum. The three areas to overview are leverage (how typically are these duties repeated), threat (what occurs if issues go improper), and chainability (how does this match right into a workflow).<\/p>\n<\/div>\n<\/section>\n

By Tom Swanson, Senior Engagement Manager at Heinz Marketing<\/a><\/p>\n

Final month I wrote about using SWOTs to identify use cases for AI in B2B marketing workflows<\/a>.\u00a0 In the present day, I’m writing about evaluating these use instances.<\/p>\n

I’ve labored with quite a few groups on bettering their advertising orchestration.\u00a0 For the final 1.5 years that has meant \u201chow do we integrate AI?<\/a>\u201d.\u00a0 Clearly step one is how do you determine use instances that will be significant, after which need to prioritize them.<\/p>\n

\"newsletter\"newsletter<\/a><\/p>\n

This framework is how I construction my pondering for groups of all sizes to deal with doing only a few issues at a time, very well.<\/p>\n

The framework is easy: leverage \u2013 threat \u2013 chainability.<\/p>\n

So first, slightly little bit of definition, and in line with the easy theme:<\/p>\n

Leverage = how typically is the duty repeated?<\/p>\n

Threat = how unhealthy will it’s if this will get tousled?<\/p>\n

Chainability = does this activity produce an enter for one more activity?<\/p>\n

It’s fairly simple how these 3 match collectively.\u00a0 If the duty will save plenty of time\/repetition, isn\u2019t tough to repair if issues go awry, and produces an enter for one more related activity, then it’s a nice candidate for AI.<\/p>\n

If, like me, you take pleasure in a easy visible, then lay these out on a cartesian aircraft.\u00a0 Like this:<\/p>\n

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Chainability is extra of a sure\/no on whether or not or not the output will go AS-IS into one other activity.\u00a0 If the output will have to be reviewed\/modified then it isn’t chainable.\u00a0 This may differ by crew, so if you need people to overview\/approve your marketing campaign technique earlier than the machine generates a tactical plan, then it isn’t chainable.\u00a0 Different groups may not care a lot.<\/p>\n

Working it this fashion offers you some flexibility in the way you rank issues alongside the x and y axes, with chainability as a Boolean gate or no less than as a big consideration.\u00a0 Every little thing is a spectrum.<\/p>\n

There are plenty of nuances in doing this.\u00a0 A lot of it’s cultural.\u00a0 The danger of a phrase being improper is likely to be big for one agency or business and small for one more.\u00a0 You\u2019ll have to do that subjectively for most of the use-cases.<\/p>\n

Anyway, let\u2019s get into some examples.<\/p>\n

Leverage: How typically is that this activity repeated?<\/h3>\n

Rote, repeated duties are the very best match for AI, significantly early on if you are nonetheless studying to construct.\u00a0 Extra complicated duties are simpler to interrupt down later, after you perceive how the machine thinks.<\/p>\n

When evaluating leverage, I like to take a look at the scenario (learn: use case), the frequency that the duty is repeated, the price of doing the duty (normally: time), how the workflow differs from run to run, and eventually how uniform the output is every time in comparison with earlier runs.<\/p>\n

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Here’s a actual consumer instance:<\/p>\n

Native Knowledge Miner<\/strong><\/p>\n

State of affairs: Regional reps wanted to remain on high of recent native companies as potential prospects.<\/p>\n

Frequency: Weekly.<\/p>\n

Value: \u00bd day.<\/p>\n

Workflow: uniform, the identical each time.<\/p>\n

Output construction: uniform, the identical each time.<\/p>\n

Finally, this was a terrific candidate.\u00a0 It has a daily frequency that may be mechanically triggered, and it prices a good period of time that could possibly be spent promoting.\u00a0 The automation requires little upkeep as a result of the workflow and the outputs are the identical each time.\u00a0 Modifications ought to be rare and additive.<\/p>\n

Because it occurred, the danger profile and chainability had been additionally stable for this instrument.\u00a0 A knockout!<\/p>\n

Threat: What occurs whether it is tousled?<\/h3>\n

There are numerous, some ways issues can go improper.\u00a0 Dangers are task-specific, have excessive variance between duties, and are weighted otherwise by organizations, groups, and people. \u00a0A crew with a excessive threat tolerance resulting from AI-automation mandates goes to simply accept a monetary threat quicker than a crew whose govt management is skeptical and measured about AI adoption.<\/p>\n

I discover it most helpful to categorize dangers by sort after which basically do a low\/mid\/excessive to outline the danger inherent to the duty.<\/p>\n

Right here is one other actual instance:<\/p>\n

Automated Marketing campaign Technique with Human Assessment<\/strong><\/p>\n

Use case context: the consumer wished to automate their marketing campaign technique drafting with a crew of brokers that will pull CRM knowledge, net analysis, and buyer knowledge to develop marketing campaign methods that will expedite time to market.\u00a0 You will need to notice that this is able to produce a deck for strategic overview by leaders, not push something into market itself.<\/p>\n

Monetary threat: Low. Token-cost solely.<\/p>\n

Rework threat: Excessive. Strategic drift doubtless.<\/p>\n

TCO threat: Mid. Documentation must be up-to-date.<\/p>\n

Model threat: Low. Offset by management overview.<\/p>\n

Adoption threat: Mid. Crew would require coaxing to make use of it.<\/p>\n

Accountability threat: Low. Offset by management overview.<\/p>\n

I’ve but to codify a particular set of dangers that have to be assessed on a task-by-task foundation.\u00a0 I feel monetary, model, and TCO are in all probability the large 3, however there are others.\u00a0 Content material bots could have a distinct profile of dangers than a win\/loss evaluation bot.<\/p>\n

Chainability: How does this hook up with different duties?<\/h3>\n
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For a fan of workflows, similar to myself, that is essentially the most attention-grabbing consideration.\u00a0 How a bot matches right into a workflow is fairly simple.\u00a0 It’s triggered both by a person or by one other bot, supplied and enter, after which it runs its activity.<\/p>\n

The large query to ask is: when would you like a human overview?\u00a0 Maximally chained bots take an enter from the earlier bot within the chain, do their factor, then cross the output to the subsequent bot.\u00a0 This comes with some tradeoffs.<\/p>\n

On the professionals:<\/p>\n