The first wave of generative AI helped marketing teams create. The next wave is beginning to do the work around those answers — and the useful question for an SMB is which repeatable workflows can be improved now, without handing over decisions that still require human judgement.
Andrew Dixon, Co-Founder—15+ years in senior digital media strategy. About DBC Solutions · Last reviewed 27 August 2026
An AI agent can be given a marketing objective, use approved tools, follow a sequence of steps, check progress and return with a completed output. Depending on its permissions, that might mean researching competitors, checking campaign inputs, updating a spreadsheet, preparing a weekly report, sorting enquiries or moving information between systems.
This shift from answer engine to execution engine is the central idea behind agentic AI. It is also why the opportunity and the risk are both greater than they were with a standalone chatbot. AI marketing automation for small businesses is most valuable when it removes repetitive administration while leaving strategy, interpretation and high-consequence decisions with an experienced person.
A standard AI interaction is usually reactive: you ask for an output and the model responds. An agent works through a goal over multiple steps, combining four elements: instructions (the goal and definition of success), context (relevant documents or business information), tools (authorised ways to search, calculate, edit or update a system), and a control loop (the ability to assess progress and stop or escalate when required).
This does not mean the system thinks like a person or should be trusted like an experienced employee. It means software can now coordinate more of the steps between a request and an outcome.
Large organisations have long been able to assign repetitive work to specialist teams. Smaller businesses usually rely on a few people carrying a wide range of operational tasks. Agents can narrow that capacity gap by handling clearly defined, repeatable work at speed — giving skilled people back the time currently lost to gathering, formatting, checking and transferring information.
Klarna’s 2024 customer-service deployment is an early large-scale example: its AI assistant handled 2.3 million conversations in its first month, performed work equivalent to 700 full-time agents, and reduced average resolution time from 11 minutes to less than two. Those figures should not be treated as a benchmark for a small business, but they demonstrate the economic potential of automating a high-volume, well-defined workflow. The more transferable lesson is to measure quality and customer experience alongside time and cost.
Gathering information from approved sources, organising it into a consistent structure and flagging material changes. Human input remains essential when deciding what the information means.
Assembling performance data, comparing it with targets, highlighting unusual movements and preparing a first draft of commentary — directing senior attention to what needs a decision.
Turning an approved strategy into repeatable production steps. This should support expert content rather than create a volume machine — Google warns that scaled, low-value content can breach its spam policies.
Checking campaign names, links, dates, budgets, tracking parameters and disclaimers against a launch checklist, flagging discrepancies before they become expensive mistakes.
Classifying incoming enquiries, enriching business details and routing leads to the correct person. Speed matters, but so do privacy, tone and escalation.
The wrong first use case gives an agent broad access, unclear instructions and a high-consequence decision. Avoid starting with workflows that allow an untested system to make large advertising budget changes, publish claims without factual or legal review, send bulk customer communications autonomously, enter sensitive personal information into an unapproved public tool, approve refunds, pricing or contracts without limits, or make employment, credit, medical or other high-impact decisions.
The Office of the Australian Information Commissioner advises businesses to understand how commercially available AI products handle personal information and clearly identify public-facing AI tools such as chatbots. Australian Government adoption guidance recommends clear accountability, risk assessment, testing, monitoring and disclosure.
When a chatbot makes an error, a person may notice before anything happens. When an agent has tools, the same error can change a file, contact a customer or update a system — permissions and approvals matter as much as model quality. Sensitive data can also move outside intended boundaries, automation can hide a broken process rather than fix it, and teams can lose important judgement if every first draft or response is automated.
Select a task that is repetitive, time-consuming and easy to verify. Document the current steps, owner and success measure.
Define what the agent can access, what it must never do, and where human approval is required. Use the minimum permissions needed.
Let the agent prepare outputs without taking final actions. Compare its work with the existing process and record accuracy and time saved.
Only after the draft stage is reliable should selected low-risk actions be enabled. Keep approval gates for customer-facing or irreversible steps.
Track accuracy, rework, customer satisfaction, escalations and the effect on commercial performance — not just hours saved.
Automation can be an excellent way to improve efficiency and productivity, but it should not be given free rein over strategic thinking or implementation. AI still has limits. It needs an experienced strategist to sense-check issues, verify its research and add context the system does not have. In my view, a well-built automation process works best when an experienced person remains behind it and accountable for the result.
DBC Solutions brings senior marketing expertise to conversations about AI automation. We can advise brands on which marketing processes may be suitable for automation, which controls are required and where human judgement must remain non-negotiable.
Understanding AI automation does not mean handing client strategy, campaign management, SEO, content or reporting over to it. At DBC Solutions, a co-founder builds the strategy, directs implementation, reviews performance and remains accountable for the result. See how this connects with our approach to media and marketing strategy and to paid search in the AI era.
No. A chatbot primarily responds to prompts. An agent can work towards a goal over several steps and may use tools or connected systems to complete defined actions.
Not for every use case. Many business platforms now provide configurable agent or automation features. More complex workflows, sensitive data and custom system connections may require technical support and formal governance.
A repetitive, low-risk task with clear inputs, an objective success measure and an easy human review step. Marketing reporting preparation, campaign quality checks and structured research are often safer starting points than autonomous customer or financial decisions.
There is no universal list, but access should follow the principle of least privilege. Sensitive personal information, credentials, financial controls and irreversible actions require especially strong justification, protection and oversight.
DBC Solutions can help brands assess the marketing case for AI automation while keeping strategy, implementation and accountability firmly human-led. If you want a senior view on what should be automated — and what should not — apply for a free strategy session.
Apply for a free strategy sessionRelated reading: SEO visibility beyond rankings · Google Ads agencies and AI search · All articles
Sources and further reading: Original topic source: Jeff Bullas, “The $199 Billion Agentic AI Revolution Nobody Is Ready For” · OpenAI, “A practical guide to building agents” · OpenAI, “Introducing ChatGPT agent” · Klarna, “AI assistant handles two-thirds of customer service chats in its first month” · Australian Government Department of Industry, “Guidance for AI adoption” · OAIC, “Guidance on privacy and the use of commercially available AI products” · Google Search Central, “Spam policies for Google web search”