How to Ensure Marketing-Focused AI Initiatives Don’t Fall Flat

Based on our discussions with clients and prospects, it is clear companies are increasingly putting their excitement about AI into action via both small- and large-scale initiatives. In the marketing world, these efforts seem to be smartly aimed at leveraging AI to increase campaign efficiency, improve personalization and uncover new growth opportunities.

However, there’s a risk that companies allow their zealous pursuit of AI-enabled marketing to push them too far too fast, leading to underwhelming results and squandered investment. Companies must pursue quick action and impact – but also take a step back and focus on foundational elements. These foundational elements – from data and technology to change management and agility – are far less exciting than discussions of AI’s potential. But they are crucial enablers of sustainable AI initiatives that generate meaningful ROI.

1. Lay data and technology foundations: To successfully activate an AI-driven growth initiative that spans marketing and sales, it’s crucial to start with organized data and tools. Companies must answer a series of questions: Do we have the right data for AI, or do we need to supplement our owned data? How organized is our customer and product data? Do we have a platform that enables us to see customer behavior across product lines? What AI tools do we have access to today that we aren’t using? How ready are we for increased automation across marketing and sales channels? Answering these questions and then addressing any gaps will allow a company to point AI models at organized and robust datasets, make strategic decisions about its toolset and proceed confidently into an AI-driven future.
2. Document the operating model: Another crucial foundational step is to formalize and document the marketing operating model (i.e., how a marketing team builds campaign strategy and executes campaigns). Often, this operating model lives in scattered documents and team members’ heads. But to get the most out of an AI deployment, a team must thoroughly write out its operating model, from the idea generation phase of a campaign all the way through execution and measurement. When it has this model documented, it can identify the opportunities for AI support. There will be some tasks that marketers can completely automate with AI. However, in most cases (today, at least), AI will play an enrichment role, helping the team improve its ideation, creative output, campaign velocity and measurement capabilities.
3. Act with urgency: While laying this foundation can require a big lift (depending on a company’s starting point), it’s not an excuse to move slowly. After all, it can be challenging for leaders to seek significant investments only to tell their stakeholders to wait a while for the results. The good news is that there’s a middle way between interminable foundation-building and rushed AI deployments.
   
   No matter the verbiage a company uses (e.g., “phases,” “proofs of concepts,” etc.), it’s important to segment the initiative so stakeholders understand when they can expect to see market-facing activity and impact. Additionally, act immediately on opportunities uncovered during the foundational period. So, if during the data and tool assessment, the team sees an opportunity to use generative AI to speed data analysis or content creation for an upcoming campaign, it should jump on it. Layering AI into existing processes can speed outcomes and start the necessary mindset transition from traditional marketing to AI-enabled marketing.
   
   Similarly, the team should move quickly to conduct the initial assessment, build the data and technology foundation, and craft the go-forward marketing and sales plan. Iterations are an inherent piece of an AI deployment. So, once a company has its foundations in place, it can launch and improve over time.
4. Don’t underestimate the organizational change required: No matter how eager an organization seems to be to embrace AI, it will involve fundamental change for those on the front lines of marketing and sales efforts. Marketers who are used to manually creating materials, reviewing data, and revising campaigns will have to get comfortable fine-tuning inputs to the AI engine, overseeing outputs from the AI engine, and being protectors of the company’s brand. They must also add new skills (e.g., prompt engineering) and master new tools. Similarly, salespeople will need to get comfortable handing over a portion of their sales outreach to the AI engine. Additionally, they will need to act on AI-generated insights while feeding their on-the-ground insights back into the model to help it improve.
   
   Successfully navigating these significant changes requires thorough, strategic and advanced planning around internal communications and change management.
5. Manage expectations and be agile: As AI tools continue to improve, it’s easy to envision a world where marketers outsource nearly all aspects of their campaign execution to a single AI tool. While this is certainly conceivable in the not-so-distant future, most marketers today will have to use an amalgamation of tools to manage their operations (e.g., one tool for ideation, another for content development and iterations, and still another for customer data management and sales insights, etc.). As a result, they must become proficient with various tools and agile in navigating across them.
   
   They must also be organized technically and ensure they connect tools with the many channels that are an inherent part of a sophisticated marketing campaign. And working with their data analytics colleagues, it’s crucial that they build and leverage dashboards that consolidate data and information from their various tools and channels – and offer a single source of truth about campaigns.

Companies are feeling pressure to take advantage of AI tools to not only improve efficiency but also drive growth. The data and technology capabilities available today are powerful, and companies have great opportunities in front of them. But they can’t afford to let their eagerness undercut the potential of their AI efforts.

Data, technology and change management fundamentals remain essential to driving success through AI initiatives. Companies that embrace this foundational piece—while also acting with urgency and agility—will position themselves to create sustainable AI operating models that power growth efforts for years to come.

About Our Authors

Kyle Adams is a managing director for The Agency at Sikich and leader of the team’s work with B2B companies. He develops and executes comprehensive marketing programs that help companies raise brand awareness, promote products and services, and showcase expertise.

Rick Young is a senior director, who leads Sikich’s data and AI practice, helping people achieve their business goals through the development of innovative and effective information management, analytics, and intelligent automation strategies. With over 20 years of experience in management consulting, he has delivered enterprise-scale solutions for Fortune 1000 clients across various industries. Rick specializes in data strategy, data governance, master data management, data science and intelligent automation.