Editorial
The Gist
-
Enhanced CX monitoring. AIOps tools provide marketers with real-time insights into customer journeys and help optimize experiences.
-
Data-driven decisions. Observability metrics in AIOps highlight data quality, which allows marketing teams to make informed decisions about personalization and workflows.
-
Smarter automation. AIOps drives predictive maintenance and helps marketers resolve issues and maintain consistent, high-performance digital ecosystems.
With all the excitement of AI hitting marketing today, most marketers think of AI as an assistant for creating content. However, AI offers more than the latest video snippet. AI is being deployed to understand operations, and it plays a role in many infrastructure elements that marketers use for programmatic marketing.
Marketing managers do not need to be an operations expert with AI. But understanding some elements of ops relative to AI (AIOps) can help marketers appreciate what can be done with the programmatic options within their team. Moreover, that understanding improves decisions on the people and processes that execute those options to deliver intriguing customer experiences.
Table of Contents
- The Benefits of AIOps for Modern Marketing Teams
- Starting Points for Marketers Exploring AIOps
- Navigating the Complex Digital Landscape
- Observability Drives Smarter AI Insights for Marketers
- Key AIOps Workflows Marketers Need to Know
- Core Questions Around AIOps
The Benefits of AIOps for Modern Marketing Teams
AIOps stands for artificial intelligence for IT operations. Traditionally it is defined as a process that uses artificial intelligence and machine learning technologies to enhance and automate IT operations management.
Marketers should care because AIOps is increasingly occurring with systems meant to deliver customer experiences. Prime examples are initiatives among retailers, especially restaurant chains. For example, Yum! Brands, the owner of Taco Bell, Pizza Hut and KFC restaurants, has been using AI-driven marketing campaigns that personalize offer messaging for customers. Similarly, Taco Bell has announced an expansion of an automated drive-thru system that uses voice AI to accept customer orders.
The interest in AI has enticed competitors to explore opportunities to improve their customer experiences through using AIOps. Fast-food giant McDonald's recently ended a two-year test of an automated drive-thru order system that involved more than 100 restaurants. Developed in partnership with IBM, the voice AI system is set for further refinement as McDonald's plans its next phase of development.
A key benefit from marketers using AIOps is that they’re better able to understand how personalization through AI can best be deployed.
Related Article: AIOps for Customer Experience: Amazon Just Walk Out Lessons
Starting Points for Marketers Exploring AIOps
AIOps is planned by analyzing applications or services within a defined environment. A single application or service will generate traces. A trace is a specific, detailed record of a single request's journey through your system.
Traces are a means to capture a digital journey. A trace represents an elementary action within your application, such as an API call to another application, or a retrieval operation for data.
In addition to traces, an environment also produces runs. A run is a broader execution of a process or workflow, which encompasses multiple traces in an instance. A single application can contain several traces initiating a collection of runs.
As a manager overseeing an app that supports customer purchases or services, you will find that AIOps can help you track customer journeys through your app by gathering the details of a digital journey and identifying downstream impacts on supporting software systems.
AIOps uses traces and runs to paint a comprehensive data storytelling mechanism that explains what happens when a customer makes a single request or transaction. When a customer clicks a button or submits a form, a trace captures the exact path the request takes, how long each step takes, which services or microservices are involved and any potential bottlenecks or errors encountered.
Traces are like detailed travel logs that capture every single interaction and touchpoint from customer interactions with a given system. Runs represent requests that move through your application's infrastructure.
Navigating the Complex Digital Landscape
AIOps reflect how applications have been supported as businesses operate more as digital platforms. Apps are more distributed across devices and require more synchronization to make sure the end users have the same feature access and experience regardless of their device. Using the cloud, microservices are often involved within the environment.
Moreover, data is increasingly being kept in non-traditional data storage like data lakes and data warehouses. Analysts are realizing that they need data access but are often conducting frequent data requests to a variety of data types that can create overly complex and expensive queries. Alternative data repositories provide storage capabilities that keep data organized and can handle high volume requests for updating dashboard metrics and other data products.
Because of these trends, IT and marketing managers must pay attention to the performance of an environment of applications rather than just the performance of a singular software. The environment that ultimately serves customers consists of several apps rather than just one.
When marketers consider AIOps, they are looking at how to best manage a suite of applications designed to operate as a system to deliver valuable results. Most management tasks involve customer data collection. Applications share and collect data from various devices and infrastructure sources. As a result, it’s important that marketers continually refine and optimize workflows to keep the system efficient and effective.
Related Article: 9 Principles to Improve Your Customer Data Management
Observability Drives Smarter AI Insights for Marketers
To be successful with AIOps, marketers must turn to observability. Observability is a process in which AI model metrics monitor the data quality being used by applications. Observability metrics address the quality of AI results. AI models rely on embeddings, a high-dimensional data vector that contains information translated into a numerical form so that the model can process. Embeddings capture the semantic elements of data, including data type and metadata that further describe the data.
To measure the performance of the environment, marketing managers must stay involved in the analytics meant for their systems. Doing so helps them recognize what analytics best serve the questions that marketing teams need frequently answered. This means understanding the objective key results (OKR) framework and the OKRs that were assigned. An OKR framework is where an overall objective is declared and then the key result from the objective is identified. The key result is an action that is measurable for monitoring progress. Observability metrics fall within an OKR framework. Observability metrics often highlight data, consistency and recent days since a given action
One tool example is LangSmith, an open-source observability tool that has seen widespread adoption among technologists managing their large language models. LangSmith is usually integrated into an AI assistant to log system traces and runs in the system. LangSmith highlights runtimes and user responses, indicating if the model is performing well. AIOps tools like LangSmith provide visibility into actual user experiences, highlight performance bottlenecks, enable precise troubleshooting and offer insights into how different services interact.
The quality of model responses ultimately impacts customer experience metrics such as net promoter score. A customer using an AI customer service assistant, for example, perceives the poor responses as a reflection of poor performance. Then they will indicate the negatives within a net promoter score or another metric representing an influence on brand performance.
Key AIOps Workflows Marketers Need to Know
Marketers must appreciate the workflow change that comes with an AIOps environment. The constant workflow monitoring means AIOps methodologies are not a “set-it-and-forget” automation that instantly solves problems. AIOps systems work best when providing indicators for predictive system maintenance (i.e., knowing when a model that supports an Al assistant needs to be examined). The AI algorithms in AIOps offer proactive problem resolution and forecast potential system failures, performance bottlenecks and security vulnerabilities before they become critical issues.
Overall AIOps can be a marketer managers’ best friend when they are faced with CX campaigns that have programmatic complexity. Marketers should spend time understanding how AIOps concepts fit into their game plan. Doing so prioritizes the best combination of people and processes that deliver amazing customer experiences.
Core Questions Around AIOps
Editor's note: Here are two important questions to ask around AIOps:
Webinar
Sep
25
CMS Briefing: A Live Look at What’s Next in AI-Driven Platforms
Learn how leading organizations are using AI‑driven tools to publish faster, personalize smarter and stay secure.
Register
Webinar
On demand
Ready or Not: How Data-First Organizations Are Unlocking Agentforce Potential
Learn how to cut through the noise, activate Agentforce and build a Salesforce AI strategy that actually delivers.
Watch Now
Webinar
On demand
AI in Customer Service: Faster Resolutions, Happier Customers
Don’t let rising demand burn out your team. See how to build a smarter, more resilient support org.
Watch Now
Webinar
On demand
From Hype to High-Impact CX Strategies That Actually Scale
Turn buzzworthy AI and outsourcing trends into measurable CX wins with fresh 2025 data.
Watch Now
Webinar
On demand
Insights to Action Rethinking the Contact Center for Real Business Impact
Join our exclusive webinar to hear CX executives share their innovative strategies for transforming service delivery.
Watch Now
Webinar
On demand
Accelerating Healthcare Ops with AI
Sign up to see how your peers are putting AI to work — and making it stick.
Watch Now
What is AIOps, and why is it important for marketing?
AIOps uses AI and machine learning to enhance IT management. For marketers, AIOps streamlines digital operations, allows seamless customer journeys and optimizes personalization efforts by monitoring and improving the performance of interconnected systems. By integrating AIOps, marketing teams can improve CX strategies and stay agile.
How can marketers get started with AIOps in their organizations?
Marketers should analyze the applications and workflows involved in their customer experience delivery. Focus on traces (detailed digital records) and runs (broader workflows) to identify bottlenecks or inefficiencies. Collaborating with IT teams to implement observability tools helps track AI system performance and data quality, which makes it easier to improve CX initiatives and align processes across the organization.
Learn how you can join our contributor community.
