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6 Issues Marketers Need to Consider for Successful AI Implementations

8 minute read
David Roe avatar
As marketing departments start automating certain tasks to improve customer experiences, there are a number of things they need to take into account first.

Despite the many unanswered questions that remain about the use of artificial intelligence (AI) in the workplace and in customer-facing and servicing departments, the growth of AI appears unstoppable. Even as early as two years ago, research from the UK-based digital marketing agency Big Rock found after interviewing 100 senior marketers globally, that AI applications, even at that stage had become one of the marketing departments mainstays. The interviews showed — again at that stage — that 55% of companies were either currently implementing or actively investigating some form of AI initiative within their marketing practices. Meaning, AI was already shaking things up in the industry.

Unsurprisingly, the research read, this inevitable rise of AI technologies in marketing is causing a major shift in the way companies work. With more and more companies looking to adopt a data-centric approach to meet the demand for more effective digital marketing.

The Role Of AI

To be clear, AI-driven marketing is a method of using technology to improve the customer journey. It can also be used to boost the return on investment (ROI) of marketing campaigns. Investment in it doesn’t look like its going to slow down soon either. According to Gartner, by next year, artificial intelligence (AI) augmentation will create $2.9 trillion of business value and 6.2 billion hours of worker productivity globally, according to Gartner.

Augmented intelligence reduces mistakes while delivering customer convenience and personalization at scale, democratizing what was previously available to the select few. “The goal is to be more efficient with automation, while complementing it with a human touch and common sense to manage the risks of decision automation,” said Svetlana Sicular, research vice president at Gartner, in a statement.

Nonetheless, there needs to be a clear consensus on the extent of the changes that AI will bring to marketing. This leaves many companies and marketers in the dark about the benefits of AI integration, and the potential gap opening between those who are adopting AI wherever possible and those who are entirely late to the game.

Aristotelis Kostopoulos is vice-president of AI product solutions at Waltham, Mass.-based Lionbridge. He pointed out that while many marketing departments, or marketing driven businesses are interested in using AI, there are a lot of factors involved in shifting a successful AI pilot project to enterprise-wide production, not least of which is getting people on board. “In my opinion, the most important is people: Get your team onboard and prepare them for the changes AI will bring. It's natural for people to resist change, so there has to be proper communication and cultural support from business leaders,” he said.Organizational leaders should bring everyone onboard by properly communicating AI's benefits and by involving them in development at different stages according to their skillsets and expertise. Then the system can successfully rollout and using it will feel like a natural next step.

Related Article: 7 Ways Artificial Intelligence is Reinventing Human Resources

Implementing KPIs

One of the big problems with introducing new technologies into an enterprise is assessing their successful implementation and AI is nodifferent. Surprisingly, businesses often fail to determine their needs and desired KPIs (key performance indicators) prior to evaluating new technologies, during the pilot or proof-of-concept (PoC) phase, said Alexey Sapozhnikov, co-founder and CTO of Israel-based prooV website. “Analyzing key features, performance, compatibility, interoperability, and industry compliance of new AI technology can be time-consuming and difficult when goals aren’t initially set,” he said.

However, understanding how the new solution adds value to your business is the first critical step towards adoption. After a successful PoC, the business should create and communicate its implementation plan with relevant stakeholders (IT staff, clients, users), which should include a timeline to prepare for subsequent training, development, and support resources vital to the rollout process.

Working closely with company leadership and securing executive buy-in is also essential, or the project can become stalled by internal politics. Often times, responsibilities and roles will shift with new technologies, which makes it crucial to examine and discuss the new changes to employees duties. Having a plan and timeline to account for all these tasks will mitigate problems, manage expectations and ensure everyone understands the impact of the new technology.

Related Article: 8 Examples of Artificial Intelligence (AI) in the Workplace

Brand Impact

So where does this leave marketers? Dean Abbott, SmarterHQ's chief data scientist and co-founder points out that while 78% of brands have said they are or are planning to implement AI this year, the problem is that if not approached or used properly, it can end up hurting instead of helping a brand's business goals, personalization efforts, and customer experiences. He says that are three priorities marketers should tackle to make their AI initiatives more effective in the coming years.

Data Accessibility

Having access to the right data is key to making any sort of machine learning capabilities work at all, let alone be more effective. But there's a reason why data collection, unification, and integration is a recurring priority and work in progress for most marketers; it's hard to do across internal teams, data streams, and interaction channels. And those who say they've already prioritized this most likely cannot do it at the level of scale or accuracy they need to just yet.

Learning Opportunities

Customer Intent

Many marketing decisions are based on average shopper behavior, but when you lump everyone together, you often miss out on key patterns of behavior that deviate from the average. For example, one might find that shoppers with nine or more visits in the past week have a 10x increased likelihood to transact online in the week following — but the marketing message will be different depending on which nine products the shopper viewed, or whether the shopper transacts regularly or if these visits are their first experience ever on the site. Make it a point to prioritize a more accurate and holistic understanding of customer intent in order to send the message or offer that makes the most sense to each individual based on their specific behavior, where they are in the customer journey, and engagement level.

Historical View

Tracking real-time behavior is important, but so is incorporating the customer's past interactions and purchases. Make it a priority to focus on more than just what the customer is doing right now. Pay attention to what the customer also did last week, last month, and last year when trying to interpret what the action they're taking now means.

Automation In Marketing

Mahi de Silva is chairman and CEO for Palo Alto, Calif.-based He said that the most impactful use of AI that they’ve seen is in adding automation into a marketing process that has previously required the following.

  • The customer to "figure out" if a brand’s products were a good fit for them
  • And/or a customer engagement process (e.g. order form, application review, customer service request) which required human intervention somewhere along the customer journey.

AI can help here too. De Silva cites three more things that marketers need to consider in this respect.


AI Assistants, using Natural Language Processing and Machine Learning, deploy a combination of questions, pre-canned choices or free-form text/picture input — to pace a would-be customer through a journey that helps a brand understand the customer, their needs and what products/solution are most appropriate for them. This level of personalization has proven to deliver better conversion rates, retention and lifetime value.

Social Media Engagement

Driven by organic traffic or paid media, brands get a lot of feedback, comments and reactions (text and/or emojis) on social media. Social listening platforms can help, but today, they require some human (typically a customer service professional or social media specialist) to react to this input. Only about 20% of these interactions get a response today.

AI assistants can automate this process, acknowledging 100% of the interactions and automating a response where it makes sense and escalating them into a one-on-one interaction with a specialist (when a specialist is ready to engage) has proven to deliver higher customer satisfaction ratings and much more positive sentiment in their social media account.


Many enterprises are shocked by the price tag associated with AI platforms like IBM Watson or a BPO shop. If that same enterprise went to IBM to build them an e-commerce site, or their customer service portal, the price tag would be equally shocking. This is why companies like Shopify or Zendesk thrive, because their SaaS platformshave a much friendlier price point and the cloud-based, pay-as-you-gosolutions, allow thousands of enterprises to use these platforms, tailoring them for the specific needs of an enterprise without a lot of effort.