Top Three AI Marketing Challenges to Overcome

    Top-Three-AI-Marketing-Challenges-to-Overcome
    Top-Three-AI-Marketing-Challenges-to-Overcome

    Every firm must embrace the new marketing opportunities that Artificial Intelligence (AI) delivers in order to remain competitive in 2019 and beyond. But just because AI-powered marketing platforms are increasingly widespread and user-friendly doesn’t imply there are not any pitfalls associated with employing AI in marketing.

    It is clearly documented that implementing AI opens up novel and intriguing potential for the marketing sector. Because every company is currently battling to use AI to remain ahead of the competition. Having said that, it doesn’t follow that things can’t go wrong occasionally or that marketers can’t make mistakes.

    According to the State of Artificial Intelligence for Enterprises by Teradata, 32% of enterprise-level firms were already integrating AI in their marketing strategies. Over 90% of respondents also said they expected significant obstacles to full adoption and integration.

    Here are some challenges that AI in marketing will encounter.

    Inadequate IT infrastructure

    A solid IT infrastructure is required for an AI-driven marketing method to be successful. Large volumes of information are created by AI technology. For this, powerful hardware is required. These computers can be extremely costly to install and maintain. Additionally, they will probably need regular updates and upkeep to keep functioning properly. This can be a major obstacle, particularly for smaller firms with shrinking IT costs.

    Also Read: Shifting B2B Marketing Focus from Solution Based to Customer Centric Marketing Approach with Personas

    Fortunately, there is a different approach to circumvent this problem. While big businesses might decide to create and manage their own AI marketing software, smaller companies might go for cloud-based options. Cloud software providers provide the entire IT infrastructure and personnel required to deploy AI software for a reasonable monthly or yearly price. These cloud services are the apparent answer for companies without adequate IT infrastructure to develop internal solutions.

    Insufficient or nonexistent data

    The foundation of Artificial Intelligence (AI) is high-quality data. Any AI system will produce subpar results if businesses give it inadequate or subpar data.

    Enterprises are collecting enormous volumes of data as the big data world evolves daily. However, this information is not always accurate. Supporting a successful AI marketing plan is either poor or insufficiently effective. These data-related problems in AI marketing restrict firms from utilizing big data to their advantage.

    Businesses should constantly ensure that the data they get is accurate and of high quality. Otherwise, organizations would get poor AI outcomes, which will hurt the general effectiveness of their AI-driven marketing strategies.

    Talent shortage

    There is now a skills gap in AI, which greatly impacts businesses that need to develop internal AI marketing solutions. The severity of this issue is predicted to worsen as the number of AI technology companies and job prospects increases.

    The fact is that there are not enough qualified AI candidates available to fill these open roles at the current rate of growth.

    In fact, even companies using pre-made AI marketing tools and solutions must ensure that their staff members are sufficiently skilled and trained to install, manage, and interpret the results. While there are occasions when the skills gap can be closed by training current personnel, certain enterprises may need to set aside funds to attract AI professionals with attractive compensation packages.

    Also Read: Four Essential Techniques for Managing Teams in Hybrid Marketing

    This puts additional pressure on already-strained financial budgets or necessitates convincing corporate management to invest large sums in AI. They may be hesitant to do this if returns are not yet evident. Machine learning (ML) research, which focuses on creating better algorithms and is the domain of data scientists, differs from applied machine learning, which uses algorithms to shed light on business problems and is the responsibility of marketers.

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