Can AI Really Be Trusted?

If you’ve ever asked a question of Alexa or Siri, you’re familiar with artificial intelligence (AI), whether you realize it or not. AI has become almost ubiquitous in our personal lives, and its usefulness hasn’t been overlooked by the business world either. In fact, AI is being used by a wide variety of businesses and business professionals in a wide array of roles to automate rote and repetitive tasks and to make predictions based on insights gleaned from massive amounts of data.

These two capabilities make AI an obvious business solution for marketers. The difference between AI and traditional marketing automation is that in the past people had to tell systems what to do through human programming. AI is based on the concept of machine learning—the AI itself makes decisions and becomes more “knowledgeable” with every interaction it has.

“AI-driven data analysis has the potential to provide marketing decision makers with strategic information about their target audiences in record time,” says Paul Herrera, chief operating officer and cofounder of Maven Road, a business intelligence firm.

These capabilities are helping marketers in a variety of ways.

The Marketing AI Institute’s “2021 State of Marketing AI Report” offers some insights into how marketers are currently using AI and how they might use it in the future, based on responses from 400 marketers. The majority of those respondents (71 percent) say that AI is already either “very” (37 percent) or “somewhat” (34 percent) important to their success. Those already using AI say that they’re using it for the following:

  • Accelerating revenue growth/improving performance (41 percent).
  • Getting more actionable insights from marketing data (40 percent).
  • Creating personalized consumer experiences at scale (38 percent).
  • Reducing time spent on repetitive, data-driven tasks (35 percent).
  • Generating greater ROI on campaigns (34 percent).
  • Driving costs down/increasing efficiency (33 percent).
  • Unlocking greater value from marketing technologies (32 percent).
  • Predicting consumer needs and behaviors with greater accuracy (29 percent).
  • Increasing qualified pipeline (26 percent).
  • Shortening the sales cycle (21 percent).

Their highest priority was to accelerate revenue (42 percent).

Interestingly, while AI has typically been approached with a certain amount of trepidation among many, with rumblings about the potential for robots to “take over” and eliminate jobs, respondents to this survey were more optimistic. The majority of respondents felt AI would have primarily a positive impact—with 56 percent saying that it is likely to create more jobs.

“Through data analysis—driven by artificial intelligence—it is possible to find relationships between consumers of different products that could go unnoticed in consumer-facing strategies,” Herrera says.

Ryan Stewart, a marketing entrepreneur specializing in growing leads for B2B companies, points to some specific ways that marketers are using AI, including the following:

  • data collection and filtering;
  • sending personalized or tailored messages;
  • content creation;
  • user interface customization; and
  • advertising.

He points to three commonly used AI marketing tools and shares the pros and cons of each:, an AI software for content optimization and user behavior prediction. Pros: It seamlessly integrates with large content management system platforms and is cost-effective. Cons: It lacks a graphic map to keep track of conversations, performance costs, and technical understanding for maximum utilization.

Brand24, a tool used to track the brand prevalence and brand monitoring. Pros: It offers fast and professional customer services. Cons: The application is too costly for small businesses.

NetBase Quid, a popular AI tool that allows real-time interaction between companies and customers. Pros: It is top-rated among the global companies, known for efficiency and an attractive user interface. Cons: Its manual setup requires technical know-how. It is only recommended for experts.

The potential of AI for use in marketing is significant. But how reliable is AI currently, and to what extent can marketing and sales professionals rely on its insights?


AI offers big benefits to marketers and others, says Ingrid Burton, chief marketing officer of Quantcast, a provider of AI-driven real-time advertising and audience insights and measurement.

“AI can help marketers in a variety of different ways, but one of the greatest benefits of AI/machine learning technology is its ability to help marketers identify patterns in behavior that the human eye can’t always see, which can help shape how they reach audiences and then tailor content and offers,” she explains. “Without AI and machine learning, marketers are largely left with their gut instinct to predict customer behavior, and despite what many marketers might tell you, AI has the ability to make stronger predictions than humans.”

Still, for AI to be effective and reliable for use by marketers, they must be able to provide the right data and enough of it, and they must ask the right questions. Marketers shouldn’t simply assume that because advanced technology is in play, the results they receive are reliable and can be trusted.

Marketers need to be aware of some potential drawbacks as they turn to AI-driven tools to help them make informed marketing decisions.

“AI systems need guidance, whether it is training on information such as customers’ behaviors and needs, tuning to marketing and business goals, or operating within campaign rules and targeted outcomes,” says John Nash, chief marketing and strategy officer of Redpoint Global. This takes time and requires that marketers have access to and can provide high-quality data—both historical data and real-time data, he says.

“AI is only as good as the data that it is utilizing, and it is only effective when you can ensure accurate, timely, and representative data,” Nash cautions. “This means you also need technology that will bridge data silos and provide you with a holistic real-time view of your customer data.”

Ideally, Nash says, AI platforms “should be able to ingest and then display data from across first-, second-, and third-party data in real time from all internal and external customer data sources.”

Only when they have the right data feeding AI systems can marketers confidently automate key tactics while leaving themselves time and energy for more strategic endeavors, Nash says.

“AI will never be able to perform all the tasks that a human can,” says Muhammad Fahad Alam, a data science intern with Data Science Dojo, provider of a platform for training in data science, data analytics, and machine learning. “It is incapable of independent thought,” he says. “If a crisis occurs, for example, scheduled messaging may need to be changed. In this scenario, AI would fail.”

Because AI can’t always be trusted, Alam cautions that marketers “still need to be involved in every aspect of their campaigns to ensure the raw data that the AI works from is still relevant.”

AI-based tools, he advises, should be evaluated based on marketers’ “unique needs and preferences, whether that means completely trusting the algorithms, A/B testing every step of the way, or only using machine learning after becoming fully educated on how it works.”


AI tools range from the simple (think Grammarly) to the very complex (think IBM’s Watson). They’re designed to take on many of the mundane tasks to give humans more time to focus on more value-add activities or tasks requiring the kind of creativity and innovation that (at least for now) only humans can provide.

Since much of the current AI is based on the premise that these tools get “smarter and smarter” the more experience they have based on the data available to them and their interactions with human users, they are continually evolving.

“The pandemic has brought enormous challenges to global markets, and over the next few years, we can expect accelerated consumer and market changes,” Herrera says. “This requires leaders and stakeholders who can rely on real-time information to make quick and accurate decisions.”

AI tools are evolving to do just that.

These tools have become much more sophisticated over the past few years, says Heather Davis Lam, founder and CEO of Revenue Ops. “With the prevalence of Big Data and the ability to capture more data points than ever, we rely on AI to help us process the trends that are collected through our sales and marketing activities,” Lam says. For example:

  • Within marketing automation platforms like Pardot, Lam uses the B2B Marketing Analytics tool to analyze the best-performing emails and campaigns to decide which messaging resonates with prospects. They use AI to run A/B tests through their email marketing campaigns. Upon distribution, data is analyzed, and the messaging is adjusted in real time to optimize performance while in flight.
  • On the sales side, Lam points to Salesforce’s Einstein, an AI tool that analyzes records and scores their chance of lead conversion or a deal closing based on historical data. Einstein also offers recommendations about actions that the sales rep can take to increase the likelihood of success with certain prospects. That intelligence, she says, reduces the time employees would spend determining the next best steps. In addition, sales teams can maximize their productivity by focusing on high-priority leads, cases, and campaigns.

There is an increasing array of AI-powered tools and technology to aid a wide range of marketing needs. The sheer volume of these tools can make it challenging for marketers to decide which are likely to best meet their needs.


When considering AI tools, Stewart suggests keeping these needs top of mind:

  • The tool should be easy to incorporate into your existing setup.
  • You can manage the results created by partially automated services.
  • There are no hidden charges.

Selecting AI tools is a balancing act, says Sophie Dionnet, general manager of business solutions at data science platform provider Dataiku. To boost the odds of achieving sustainable, cost-efficient, and impactful results from AI, she suggests the following:

  • Picking use-specific applications to build a collection of use cases.
  • Consider the potential for bias and the need for full transparency. “Between regulatory changes and the need to build trust, organizations must ensure no bias and complete explainability,” Dionnet says.
  • Look for solutions that are flexible and allow for ease of adaptation. “As we’ve seen during COVID, consumer trends change quickly, and marketing—and their platforms—must adapt by recalibrating AI applications quickly and accurately,” she says.
  • Consider how these solutions will interact with other systems. “Integration is key, especially with a growing range of data sources and moving ecosystems, such as the cloud and social platforms,” Dionnet says.

As marketers gain experience with these tools, they’re discovering some best practices and opportunities for improvement in applying AI to their marketing challenges.


As with any computer-aided decision-making process, the output is only as good as the input: “garbage in, garbage out.” The more data with which AI can work with, the better the answers it can provide. “AI and machine learning act on data from a wide range of marketing and business data sources, including customer, revenue, social, digital, and sentiment data—and even external data sources that help augment businesses’ data to deliver the answers,” Burton says. “Without substantial access to the data listed above, AI and machine learning tools will never perform optimally.”

It’s also important that marketing practitioners understand how AI works and what is required for it to perform effectively.

“AI opens up tremendous opportunities for hyper-personalization in marketing and customer intimacy,” Dionnet says. “However, deploying AI requires a learning curve, and starts with putting in place use cases that gain sophistication and evolve into unique marketing frameworks.”

Dionnet’s best advice for marketers: “Take this as a gradual journey to remain in control, preserve understandability, and embed AI across everyday workflows.”

Despite the promise and potential of AI, it’s still important to have a “human in the loop,” Burton says. “AI and machine learning are incredibly helpful and valuable tools, but humans should always be involved in the process,” she advises. “Having a savvy business analyst or marketing ops person in the marketing department is a great start—someone who can understand the right questions to ask from the onset and then analyze the answers to make certain the AI did not return a nonsensical answer.”

For marketers the bottom line when it comes to AI is to proceed with caution and oversight. Just as you wouldn’t turn loose a marketing team member without some means of monitoring and assessing performance, you shouldn’t turn AI loose either. 

Linda Pophal is a freelance business journalist and content marketer who writes for various business and trade publications. Pophal does content marketing for Fortune 500 companies, small businesses, and individuals on a wide range of subjects, from human resource management and employee relations to marketing, technology, healthcare industry trends, and more.

The following article from Linda Pophal, 2022 provides their research perspective. HERE

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