The potential of AI for marketing is limitless: it helps marketers create hyper-personalized content, increase customer engagement by identifying the optimal times to deliver messages, and can even recommend solutions before the customer searches for them. Yet despite the numerous benefits, marketers have been slow to embrace deep learning, the most advanced form of AI, in today’s digital media landscape.

From the significant time investment in managing large volumes of data, to data and privacy regulations, along with the added leg work involved in understanding and differentiating vendors before launching a partnership, marketers continue to face challenges as they begin to embrace this evolution.

According to Gartner, by the end of 2024, 75% of enterprises will move from piloting to operationalizing AI. While deep learning and AI may sound intimidating, it is an invaluable tool for marketers to embrace that empowers them to make more informed, efficient decisioning, to reach the right consumers in a meaningful and impactful manner. It also reduces waste, and gives them time back to focus on increased creativity and strategic business matters that drive company growth.

Here are three of the most common hurdles that brand marketers are facing when beginning to embrace AI, and suggested solutions for navigating them successfully:

Cost and Implementation:

Even “a small AI team can cost a business upwards of $320,000 per year in technology development costs alone.” Deep learning, in particular, requires investment, both in terms of time and resources. It takes several months to build and train a neural network until it begins to show its optimal value. Most companies might also feel they lack the resources needed to bring on data scientists and engineers with the skills necessary to build custom algorithms. However, this mindset is a profoundly limiting one. Investing in deep learning now is the bold, forward-looking strategy that separates industry leaders in the evolving marketing landscape.

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Data Quality and Quantity:

According to research from Gartner, poor data quality remains one of the top reasons that marketing analytics aren’t reliable for business decisions. Access to high quality, clean, and diverse data is essential to the performance of AI algorithms. AI models highly depend on high quality and large scale data to train and learn from, while insufficient or low quality data (inaccurate, biased, or incomplete data) yields poor results and flawed outcomes.

Companies who are constantly assessing how they can further improve their data collection, use, and management are the ones winning today.. In addition, being more transparent by improving cybersecurity procedures and being upfront about data collection can help in addressing overarching ethical concerns surrounding AI.

Rapidly Evolving KPIs:

1. Does your KPI still serve the goals of your campaign and even larger to think and understand = will this serve the goals of the organization? Or should we be looking at a more sophisticated approach to understanding the real value we are getting from our media partners?
2. Accountability for the role that a partner actually plays in identifying, messaging and ultimately driving or influencing conversion – accountability of the various partners and the roles that that play in every stage needs to be revisited to really reset expectations and understand the value that one partner brings which can ultimately be in multiple arenas.

Finding Solutions and Reaping the Benefits

All too often, companies choose to rely on a series of short-term solutions that may provide rapid boosts but leave the underlying problems intact. Most customer acquisition strategies are quick fixes that leave marketers perpetually stranded within a hamster wheel, struggling to get ahead without any long-term progression while their competitors forge ahead.

Today, adopting an AI solution is frictionless and flexible. There’s no need to reinvent your marketing pipeline. In fact, as with a self-driving car, this is about things getting simpler rather than more intricate or complex. You can even keep much of your existing ecosystem in place. AI can add value within your current stack while delivering relevant, personalized ads at scale.

1. Precision and personalization allowing for advertisements to reach a qualified audience in a meaningful manner
2. Utilizing your CRM in an impactful way; allowing what you know about your consumers to indicate and identify net-new consumers to grow your business, unlocking new opportunities for consumer engagement and understanding – this is how you will grow your business in a meaningful and precise manner

A defining element of almost every kind of marketing is driving more sales, and finding ways to constantly gain an edge is fundamental to that. Part of marketing practice is about taking on the responsibility of being open-minded to the opportunities and enabling your clients to take advantage of the benefits the future brings.

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