Turn competing AI ideas into a clear roadmap.

The concrete outcome

Agreed outcome: a prioritised roadmap rather than a tool collection. lol.marketing delivers target state, use-case portfolio, decision criteria and roadmap in a form that can be reviewed, continued and operated internally.

Acceptance is not based on presentation impact but on agreed cases, quality criteria and control paths.

How the engagement works

• Record baseline, goal and owners
• Review data, permissions and edge cases
• Build and test with realistic cases
• Accept, document and hand over into operations

Price and boundaries

The lol.marketing fee is agreed individually after scope confirmation. Provider subscriptions, API charges, third parties, additional legal review and work outside the agreed scope are excluded.

Turn competing AI ideas into a reasoned decision

You want to use AI in marketing, but the choice of tools, demonstrations and potential projects makes prioritisation difficult. Your team may already use individual assistants without a shared process. Or you may first want to know whether an automation can be operated sensibly in your business.

Our AI consulting provides a concrete basis for that decision. We examine tasks, information sources and handovers, then assess applications by value, effort and prerequisites. The output is an actionable recommendation with visible boundaries rather than a general presentation about technology trends.

When consulting is useful

The service suits business owners and marketing leaders selecting an initial pilot, connecting isolated experiments or evaluating a proposed project. It is particularly useful where marketing and sales use several systems and responsibility between them is unclear.

You do not need to choose a preferred model or platform beforehand. We begin with the task and consider the systems you already have. If a simpler improvement without AI fits the problem better, that option belongs in the assessment.

What you receive

We produce a clear view of the processes examined and suitable use cases. For the recommended priority, we describe the objective, required input, intended working output, subject-matter ownership and technical dependencies. We also define quality criteria and questions that need resolution before implementation.

The agreed scope can include stakeholder conversations, review of approved working examples and a joint findings session. You leave with a concrete next step for a pilot, or a reasoned recommendation to defer an application until its prerequisites are in place.

From current process to pilot plan

We first identify recurring tasks and bottlenecks. We then consider frequency, handling effort and the consequences of incorrect output. Representative examples make requirements tangible. Those findings support comparison of a small number of viable approaches and the final recommendation.

In an explicitly hypothetical case, a company wants to automate its entire sales operation. Review identifies enquiry preparation as a more bounded initial project. The pilot plan then describes that process, the CRM information required and approval of response drafts. Further capabilities remain documented as possible later work.

Inputs and pricing factors

A useful assessment requires access to responsible people and suitable descriptions or examples of the work. Production-system access is not necessary for every initial review. We agree which information is relevant beforehand.

Cost depends on the number and complexity of processes, participating teams, existing documentation and depth of technical analysis. The proposal defines the scope. Implementation, software subscriptions and ongoing operation are separate unless explicitly included.

Can we begin with a single process?

Yes. A named bottleneck supports a concrete assessment and a manageable pilot. It often reveals missing information and ownership more clearly than a broad exploration of every possible AI application.

Will you recommend particular tools?

Once requirements are clear enough, we compare suitable options. The recommendation considers existing systems and operating needs. Selecting a well-known tool is not the project objective in itself.

Can you implement the recommendation?

Implementation can be offered as a separately defined project. AI automation covers connected workflows. An AI workshop can complement the assessment where the team needs practical experience first.