What leads to the successful adoption of AI in marketing? Many organizations will sweat over deciding which large language model to use and which AI-enabled marketing apps and agents are the best for their needs. However, successful adoptions of AI in marketing are much more likely to depend on something other than which technology a company selects. The key to success? A people-friendly approach to adopting AI.
The human side of AI adoption
Marketers are worried about AI taking their jobs and in some cases, they’re right to be worried. Up to 80% of large organizations are looking to implement fully autonomous business and IT processes within the next 24 months, according to a study from Salesforce and Vanson Bourne. Senior managers want to cut costs and improve profitability and replacing humans with AI-driven automation sounds promising.
Some marketers are also intimidated by AI. Hearing that they have to take classes in prompt engineering or understanding large language models worries these marketers, not to mention all the acronyms and jargon (think NLP, NLG, LLMs, RRN, AGI, ML, AI hallucinations, neural networks and tokenization).
A people-friendly approach can reduce resistance to change, increase engagement and improve the chances of success for any organization adopting AI in marketing. Here are a few tips.
Involve employees upfront
People support what they help create. In the last 20 years, I’ve been involved in many adoptions of new technologies and new ways of working. I’ve learned the hard way that you need to involve employees at every level upfront. They’ll tell you their concerns and help you address them. If you choose the right influencers, they’ll also bring along everyone else.
Concerned employees are much more likely to believe one of their peers telling them that the adoption of AI will free them to work on more important things and eliminate some of the drudgery than if their leaders tell them the exact same thing.
Involving employees upfront also increases engagement. Instead of disengaging and coming up with one reason after another not to adopt AI in marketing, they become part of the solution. They identify ways that AI can augment human decision-making, rather than replacing it. They get excited about learning new skills and using them to do their job better.
Leadership plays a critical role
Leaders must champion change, demonstrate commitment and lead by example. They must explain why change is needed and what’s in it for the average employee. Too many leaders make the mistake of only talking about what AI will do for the organization: usually greater productivity and reduced costs. Employees hear “reduced costs” and immediately think “layoffs.”
Leaders must demonstrate commitment. If leaders talk about training everyone on AI but cut or leave the training budget flat and expect employees to train on their own time, they’ll see a lack of commitment.
Leaders also have to lead by example. Are leaders using AI to increase their effectiveness? Are they conversant with the latest advancements and able to use AI technology? If not, employees will question the leader’s commitment and perhaps competence in the new world of AI-driven marketing.
Address resistance to change
Involving employees upfront, championing change and modeling the behavior they want to see go a long way toward addressing resistance to change. But it’s not enough. Leaders at all levels have to explicitly address the causes of resistance to change. The classic driver of resistance to change is FUD: fear, uncertainty and doubt.
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Leaders must address employees’ fear of losing their jobs head-on. If the goal of implementing AI is to cut costs by eliminating positions, there is not much leaders can do about this fear other than be honest with people. A better goal would be to grow the business while keeping costs proportional to growth. Figuring out how the adoption of AI can increase topline growth should be the goal of every executive.
Uncertainty needs to be replaced by clarity. Clear messages on why the organization is adopting AI, what’s in it for the employees and how it will be implemented need to be repeated again and again to combat uncertainty.
Empathetic leaders know that doubt can be the toughest challenge. Many times employees do not express their doubts but keep them to themselves. They need to be encouraged to express concerns and learn that their concerns and doubts will be taken seriously.
Adopt AI in an incremental fashion
Don’t make the mistake of launching an AI transformation. Don’t spend millions of dollars to hire a consulting firm to develop a plan for your AI transformation; this will almost certainly be a waste of time and money.
Transformations don’t work, but incremental adoption does. Start small and adapt as you go. Correct what doesn’t work and gradually spread out the adoption of AI. During this time of incremental adoption, leaders must build coalitions and celebrate incremental wins to develop support.
Adopt AI slowly, focusing not on the technology but on generating better outcomes: more revenue, lower costs, more value for the customer, lower risk, more engaged employees and happier employees.
Don’t use the old models of workforce education, sending employees to 2-5 day training events. Younger people, in particular, respond better to just-in-time, just-enough education. Think do-it-yourself (DIY) education on YouTube. If I need to know how to replace the cabin air filter in my car, I watch a 2-minute video just before or as I replace the filter in my car. I don’t need to go to a two-day seminar on car maintenance.
People-centric AI adoption for marketing success
Organizations adopting AI should carefully select the right technologies. However, their approach to adoption is even more crucial. A people-centric strategy involves:
- Engaging employees from the start.
- Having leaders who champion and model AI adoption.
- Implementing new models of workforce education.
- Explicitly addressing resistance to change.
- Adopting AI incrementally.
This approach is more likely to yield significant benefits in marketing through AI applications.
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