Unlocking efficiency: Standalone task automation with AI marketing tools

There’s no denying that AI has become prevalent in numerous industries, and marketing is no different. 

While this has brought many benefits to marketing teams, as basic tasks can now be completed quicker than ever before, not every AI tool is valuable or effective.

Without a proper understanding of what tasks are appropriate to automate through AI, marketing teams could become bogged down in ineffective, time-consuming, and expensive tools that hinder rather than help.

Now, more than ever, is the time to identify which tools are actually effective and which are just noise. Especially as the generative AI market is only in its early stages, with it forecasted to exceed $1 trillion by 2034.

Integrating artificial intelligence into your marketing team’s workflow doesn’t have to be an overly complicated process. Standalone task automation is low-hanging fruit that can improve scalability and reduce human error in data-driven tasks, all while freeing up marketers to focus on strategy.

To truly utilize AI in the best way possible, this article will examine the link between standalone task automation and marketing, helping you avoid over-investing in unnecessarily complex tools. 

What is standalone task automation in marketing?

In marketing, standalone task automation is where technology (such as AI) is used to complete a repetitive task. This is usually implemented to improve productivity, speed, and efficiency within the workforce.

With menial tasks carried out by automation, marketers can focus on the higher-level tasks on their to-do lists that require creativity, judgment, and concentration.

The automation of small, yet repetitive, marketing tasks shouldn’t require much human oversight post-implementation and should be centered around one specific task at a time rather than automating a whole workflow.

In marketing, good candidates for standalone automation could include auto-tagging and categorizing incoming leads, cleaning and formatting data in CRMs, setting up A/B testing, auto-generating UTM parameters, and providing real-time quality assurance (QA) on email renders or web links.

These are perfect examples of how to utilize automation without compromising human skill, especially as none of these jobs require much active thought. When it comes to performing these monotonous tasks, it would be easy to accidentally zone out and make errors that snowball into bigger problems.

With automation, these mistakes don’t happen. The technology carries out its instructions without falling victim to mental boredom or physical fatigue. In these scenarios, AI becomes an accelerator, allowing marketers to scale quickly with fewer errors in key repetitive tasks.

As Microsoft CEO Satya Nadella put it:

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“AI isn’t here to replace human intelligence but to augment it. The real magic happens when we empower people with the right AI tools.”

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Ideal automation use cases in the martech stack

There are multiple use cases for AI automation technology, and these models thrive when applied to repetitive or rule-based tasks.

When used in the right situations, AI automation can be a true time-saver, with expectations that it’ll save professionals 12 hours per week by 2029. When used incorrectly, it can lead to AI fatigue and leave team members eye-rolling as they grapple with figuring out yet another system.

Immediately qualify leads while correctly segmenting contacts

One valuable use case is qualifying leads and ensuring duplicate and low-intent sign-ups are not included in pipelines. The automation can correctly segment the leads and normalize field values.

Dividing a larger group of leads into smaller segments can yield a better understanding of customers, allowing more targeted and personalized messaging that’s more likely to convert. 

The actual segmentation categories are business dependent, but examples could include geographic location, purchase history, usage rate, position in the customer lifecycle, demographics, interests, and more.

Twilio Segment’s feature provides a complete view of the customer.

Successful segmentation is not always easy to do, however, as it requires a large amount of data to be pulled and collated. Each contact must then be correctly assigned, as otherwise an entirely unrelated email or communication could be sent to the wrong person. Not only is this clunky from a business perspective, but it could put off the customer from purchasing as they don’t feel understood by the brand.

Automation, however, can collect the customer data into one place and segment without errors simply by following predefined instructions.

For example, Twilio Segment collects first-party data from every touchpoint, giving teams a full view of customers across different experiences (app, sales, support, payment, messaging, etc.). Information can be added to each person’s profile, including “events” (pages viewed, customer start date, etc.), traits, audiences, and identities.

Make is another automation tool that can help with this use case, as it fully automates lead generation processing. It can add new leads into your chosen CRM, include enrichment to qualify leads, and gain insights about company data from domains.

Monitor paid media campaigns and advertisements

Paid media operations can make excellent use of AI standalone task automation. Syncing creative assets, pausing underperforming ads or campaigns, and adjusting bids are all functions that can be automated.

Timelines for pay-per-click (PPC) campaigns can be extensive, with a lot of data to handle. But it can be made easier with automation.

AI technology can monitor metrics and trigger alerts when something underperforms, allowing for both a reactive and proactive approach.

The time spent on manual campaign reporting across multiple platforms (Google, Meta, Pinterest, etc.) can be significant, and gathering data from these different platforms into a central location and then analyzing it makes for a frustrating, tedious task.

Rather than just monitoring the data and pulling together information, Evolv AI goes one step further as it examines why users aren’t converting. This greatly reduces the time and effort spent on A/B testing, as it uses causal inference to test multiple variations.

The platform then identifies true cause-and-effect relationships of how users interact with the website to create optimizations. This greatly decreases how long it takes to get an underperforming paid media campaign to start delivering results and return on investment.

Manage organic social media campaigns

It’s not just the paid media side of marketing that can use automation assistance with extensive reports and content creation. Organic social media can benefit from AI, as well.

Whether you need end-of-month reports or an audit into a campaign’s overall effectiveness, standalone task automation can provide support by making reporting a much quicker process. In paid marketing, ad platforms are able to relay all of the necessary information, whereas collecting data for organic social usually requires manually pulling the information from different platforms.

This can be especially irritating when the social media channels all have different metrics and statistics. For example, Instagram records views and interactions while TikTok Studio tracks post views and profile views.

Sprout Social is an automation tool that offers social media reporting with embedded AI. In this case, the proprietary AI and automation utilizes more than 10 years of historical social data and is paired with large language model integrations like OpenAI and Claude.

Rather than a tool that simply generates a social media report, Sprout’s AI identifies the most important key learnings from the data. Not only does this allow your team to see what is working and what isn’t, but it also allows you to quickly react to what’s happening in your campaign.

For social media content creation, Zoho Social goes beyond the usual social media management features we’re all familiar with by leveraging OpenAI’s ChatGPT capabilities to increase publishing efficiency. It does this with an AI assistant that can generate and proofread content for you, suggest replies, and even research hashtags. 

Resize, repurpose, and tailor creatives for campaigns with ease

To streamline cumbersome activities, like resizing creatives for multiple different use cases, check out Hunch, an AI- and automation-focused platform targeted at marketers working on paid social. 

Hunch Autopilot can pull information from media plans and create ads for different purposes. We all know the frustrations of needing more creatives to test but never having enough. Even when you have limited internal resources, Hunch helps you accelerate your testing with AI-powered creatives.

It even tackles the issue of tailoring content for a national versus local perspective. Swapping simple contextual inputs for different geographic regions or offers is time-consuming. The process can be sped up with Hunch, helping your teams avoid creative fatigue by quickly tailoring creatives to different preferences and needs. 

Another example is Buffer, which utilizes AI to tailor every post to different channels (LinkedIn, Threads, X, Instagram, etc.) 

With this tool, long gone are the days of having to amend every single post for five different channels that all have their own algorithms, specifications, and writing styles. Now, teams don’t have to spend their time re-working posts to fit different word counts as the smart repurposing feature can do it for you.

Four best practices for implementing standalone task automation

Changes in the workplace can cause uncertainty as people adjust to the new way of working, but you can ensure the process of integrating AI automation into your marketing team’s workflow is carried out as smoothly as possible.

Utilize automation for data-driven tasks

Starting with high-friction, low-complexity tasks is paramount, especially as these data-driven and repetitive activities make it easier to spot successes or failures. Because these tasks tend to be frustrating or inefficient to complete manually, teams are more likely to welcome their automation, increasing team member buy-in.

Over time, and as people become accustomed to the technology, new standalone task automations can be added to your workflows as your team’s confidence in using AI tools increases.

Examples of high-friction, low-complexity tasks include repurposing content for different platforms, transcribing webinars and interviews, creating ad copy variants for A/B testing, resizing ads and graphics, and segmenting emails and audiences.

Determine metrics to assess effectiveness

To truly determine whether an AI tool is making a difference (or is secretly becoming a hindrance) quantitative metrics should be used. These should be based on speed, accuracy, and consistency.

Depending on the task at hand, consider using time-tracking software during the trial phase to document how long an activity takes, both pre-AI and while using the tool.

To gauge how effective your automation has been at reducing execution time, compare the average time the task took to complete manually to the average time the task takes to complete with automation. This is how much time your team has freed up to spend on higher-level tasks.

The process can then be replicated to gauge the standalone automation’s impact on several other performance metrics:

• Error rate
• Expenses
• Overall team productivity
• Compliance rates

Ensure the use of AI is transparent

When integrating AI automation into your team’s workflows, transparency is key. The automation and its output should be easy to both explain and understand. Otherwise, you can expect a great deal of confusion, consternation, and difficulty understanding where things have gone wrong.

Internal documentation should be created to measure, document, and store the results of the AI. This should include playbooks that focus on the use of the automation.

Playbooks should include clear steps on how the AI automation should be used, along with any necessary prompts and criteria. This ensures that everyone is using the same method. This reduces confusion and guesswork, while reducing the risk of incorrect use and errors. 

Involve everyone from the start

When testing the standalone task automation, end users need to be included from the very beginning. This includes the people who will be using the automation, like social media managers or email marketers, and any decision-makers who will need to sign-off on the new addition and its output.

The legal and compliance team should also be contacted, as the privacy and data security risks of the tool will need to be assessed. If a tool is dev-heavy, IT may need to be included in the initial discussions, as well.

There’s nothing worse than implementing a change and then discovering bottlenecks near the end of the workflow. This will cause delays, frustrations, and low team morale as extra roadblocks must now be worked through.

Red flags in standalone task automation: What to watch for

Utilizing a new tool doesn’t automatically yield benefits, especially when the implementation isn’t properly planned or all of the impacts considered. That’s when a feedback loop becomes essential, with a focus on improvement and error correction. The entire team should be involved, with feedback surveys completed after the automation is complete. 

Depending on the results and team feedback, action should be taken to adjust the automation so it’s as effective and correct as possible.

For example, say a marketing team intends to use a new AI tool to automate adapting social media posts to different platforms. Once the task has been automated, the social media manager should provide feedback on how the tool is performing. This could touch on the accuracy of the tool, along with speed and tone of voice.

Once every two weeks, the feedback should be assessed and prompts or templates updated to improve the output. This should be an ongoing process, with the goal of fine-tuning the automation to the needs of the company and team.

Don’t over-automate tasks that require a human touch

To prevent self-inflicted crises and problems, a key red flag to watch for is the over-automation of edge cases that require human nuance. Once mistakenly automated, a deeper look at these edge cases can expose underlying issues flying under the radar.

For example, while automating the resizing of different creatives is a great timesaver and a perfect task for AI, the actual creation of ads or social media posts requires human involvement. 

While AI ad creation can greatly speed things up, it can cause more harm than good if an AI-generated ad misses the mark by including sensitive information or failing to appeal to the target audience.

Learning the hard way

We all saw the backlash Coca-Cola faced when the brand unleashed its AI-generated ad. While the video was intended to pay homage to the classic 1995 Coca-Cola commercial, audiences branded it “soulless.”

Approximately 45% of consumers feel AI-generated content lacks authenticity, so although it can be a timesaver for brainstorming and developing an idea, a human touch is still needed to complete the finished product.

This is the same for approving influencer or user-generated content. An image may get approved from a technology standpoint, but AI can miss an inappropriate item, political symbol, or something that doesn’t align with the brand.

Similarly, while AIcan be used to generate basic messages to respond to customers on social media, automations need to be employed carefully, as technology can misunderstand tone. 

To put it simply, while AI automation can speed up simple tasks, it cannot replace human thinking and originality. AI automation is best used to handle scale rather than substance.

Poor change management: Getting buy-in from your team

When implementing AI task automation, it’s critical to get your entire team on board with integrating the tool into the workflow. Failing to do so could lead to incomplete adoption of the automation (i.e., only some team members use it) or distrust regarding the accuracy of the AI’s output.

Here are the team members you need to include:

• End users (social media managers, email marketers, content managers, data analysts, etc.)
• Brand manager
• Marketing decision-makers, such as the VP of Marketing
• Legal and compliance
• IT
• Sales or customer support (if the automation is customer-facing)

It’s important to be respectful of your team members during this process. Some may feel that their position is threatened by the AI, while others might simply distrust the AI’s accuracy. Addressing these concerns is critical, however, as satisfying your team’s concerns is the only way to get them on board.

To avoid incomplete adoption of task automation, be open about the benefits of the technology (e.g., increased speed and efficiency, freedom to focus on higher-level work and less monotonous tasks) with the team. Try a trial run where the team audits the AI’s work and verifies the accuracy of its output.

Listen to any concerns your team has, be respectful, and find ways to address roadblocks.

Lack of integration with broader orchestration tools or data pipelines

Verify that the AI tool has been fully integrated with the current martech stack ahead of rolling out a new AI automation process. Having the relevant people on board from the beginning means they can ensure the automation works from their side, with the full process ready to be tested.

Verification, at a minimum, will likely require involving the following team members:

• Data analysts to verify that the AI’s outputs are accurate
• IT to verify that the AI tool is communicating with your team’s other applications and technology
• Project managers to verify that the AI is successfully handling all aspects of its tasks

If the tool doesn’t prove compatible, it could be siloed or ignored as it can’t be merged into the overall day-to-day. This lack of integration can be irritating, as it can result in workflow delays and may sour your team on future AI automations.

This results in wasted budget as the software isn’t fully usable. 

Don’t get locked into one specific vendor

You may believe you’ve found the perfect solution, but it’s important to only onboard a new tool that doesn’t lock you into a single vendor. 

Otherwise, your marketing team could become too dependent on this lone vendor, which limits flexibility and hinders future innovation and compatibility with future technologies. If the tool goes down or the price increases to an infeasible amount, your team’s left with a big hole in its workflow.

Don’t accept a tool that isn’t easily explained

Make certain the AI doesn’t feature a black-box model, either, as this lack of transparency leads to an inability to explain outcomes. 

Not only does this prevent total trust in the tool’s outputs, but it also means team members are unable to adapt to edge cases. Instead, focus on automation that uses transparent model behavior. Team members should be able to understand the results and the reasoning behind it.

Finally, be aware that your company is legally responsible for what your AI does. A black box model means you can’t fully predict what actions the AI will take. And if the AI does something legally questionable, such as violating the privacy of your customers, a legal fiasco could follow.

Tool evaluation: What to look for in AI task automation vendors

With the number of AI tools available, selecting a vendor can feel overwhelming, with decision fatigue kicking in at crunch time. To avoid this, here are some green flags in AI task automation vendors to watch for.

API accessibility

An accessible API is a priority with AI marketing tools because the API is often the key to ensuring that the AI can integrate with all of the applications and data sources in the current martech stack. Without this, your team most likely won’t be able to fully utilize the AI tool, which could lead to inaccurate outputs, data fragmentation, and manual data transfers (defeating the purpose of automation).

Plus, an AI tool that lacks an accessible API also means you are missing out on customization opportunities and can’t fully tailor the tool to your team’s unique workflows without relying on execution by the vendor.

Audit logs and version history

The vendor should be able to provide a time-stamped record of events, allowing all actions to be traced to the responsible user, as well as when and where.

Not only is this helpful if incidents and errors occur, but it can also provide a level of security in anomaly detection. This level of accountability should meet regulatory requirements, allowing IT teams to go directly to the source, if needed.

When considering different automation tools, consider those that are granular at user, system, and data levels, with API access to logs.

Ease of use for non-technical MOPS roles

For complete adoption within your organization, the AI tool must be easy to use for everyone—including those who don’t have technical roles. When an AI tool is accessible to even the inexperienced, all members of the team can use it and widespread adoption will be quicker.

Look out for:

• User-friendly interfaces (drag-and-drop user interface rather than coding)
• Prebuilt templates
• Clear documentation
• Support for set-up 

Even when instructions have been provided, an internal onboarding process could address any questions and show the team exactly how and when the tool should be used.

Low-latency processing and alerts

In the ever-changing world of digital marketing, speed is more important than ever, which is why the chosen AI automation tool should quickly process information and data.

Depending on the use case, latency could make a major difference in real-time decision-making, as well as customer experience.

Governance controls: Role-based access controls

With role-based access controls (RBAC), users are only allowed to see the data that directly correlates to their position.

This differs from attribute-based access controls (ABAC) which evaluate multiple attributes to determine access rights. Rather than focusing on a person’s role, access can instead be based on the resource (data type or sensitivity) or the environment (location of the user) amongst other possible attributes.

Another commonly used model is mandatory access control (MAC), which is the strictest and most complex model to set up. MAC requires a user to have clearance to view data labeled “secret” or “confidential,” for example. The model is enforced across all of the company’s systems and isn’t based on personal user settings.

While RBAC sounds like it could cause potential blockages and bottlenecks, it actually protects the data from being maliciously or accidentally changed.

For example, a trainee wouldn’t be able to mistakenly amend data if they’re exploring the system and accidentally click on the wrong button. Similarly, an intern or volunteer wouldn’t be able to view sensitive data. With an RBAC-based system, the information can remain in the right hands, which drastically reduces security concerns.

This model works well in companies interested in integrating standalone task automation, as long as employees have clearly defined roles. It’s easier to manage than MAC and ABAC, with agility and speed being two benefits of RBAC.

Utilizing sandbox environments

Enabling a sandbox environment means team members are using a fully curated tool, as the testing can be completed ahead of it going into active use. This provides time to adjust campaigns or systems early in the process.

The department heads and decision makers can ensure the tool is effective and test it before it becomes part of the normal workflow. The automation can be fed the necessary documentation and instructions, allowing it to be used to its full potential from day one.

When this isn’t the case, team members will receive an untested tool, which could lead to negative first impressions of the automation should any issues need to be worked out, preventing full team buy-in.

A sandbox environment is best used by mid-sized to large marketing teams looking to add marketing automation tools while contending with strict compliance requirements. 

Sandbox environments are ill-suited to small teams who aren’t yet using a lot of automation in their day-to-day workflows. In this case, a manual review process or utilizing draft modes could instead be the most beneficial.

Next steps: Adding standalone task automation into the marketing workflow

Integrating anything new can be overwhelming, but this doesn’t have to be the case with AI task automation.

Start by documenting the basic and repetitive tasks that appear in the day-to-day of your marketing team. You’re looking for something that is rule- or sequence-based and doesn’t require human judgment. 

Consider people in different departments and write down who completes these tasks and at which stage. To gain better understanding, ask for full accountability from the team and time how long these specific actions take to complete.

Once you’ve identified a low-risk use case currently clogging up your marketers’ time, map out how automation will solve this problem and run without human intervention.

From here, you can begin searching for the AI standalone automation tool that will solve your problem. Be sure to consider all the vetting tips included in this article, and try to involve the team members who will ultimately use the automation. If they help with selecting the tool, you’ll be more likely to get their full buy-in.

Ready to learn more about marketing activities that can be improved with AI? Check out “3 marketing tasks genAI can help with – no copywriting involved.”