We’ve been riding a year-long teeter-totter of AI stories in 2023.
On one side, there is potential. We’ve seen what AI can offer by generating instant information and visual assets, eliminating redundant or tedious tasks and solving capacity issues. It can see unique trends and identify minute anomalies.
On the other side, there is fear. Could AI lead to risky, unchecked outcomes if we embrace the technology too soon? Will it steal creativity and intelligence-based jobs from humans by borrowing, stealing or riffing from previously created assets?
Everyday applications
You’re already interacting with AI-based products whether you know it or not. The technology is operating all around us in mostly beneficial ways through digital services like:
- Personalizing shopping experiences
- Fraud prevention
- Voice and text assistants
- Autonomous vehicles
- Spam filters
- HR application reviews
- Analyzing population health trends/disease detection
- Route planning
These are detail-oriented jobs that rely heavily on data analysis ― the kind better left to a trained computer than to the human eye. Some less obvious use cases have been immensely beneficial, too. AI has proven to be as good or better than doctors at diagnosing certain cancers, including breast cancer and melanoma. Warehouse automation grew during the pandemic and is expected to increase with the integration of AI and machine learning. A chatbot that is fed examples of text can learn to generate lifelike exchanges with people online. Unlike their human customer service representative counterparts, these bots don’t need to sleep or take breaks, offering 24-7 service to all kinds of consumers.
Marketing applications
In the B2B marketing and communications world, AI is already being integrated into analytics and CRM platforms to uncover information on how to identify and better serve customers. It’s also being leveraged for targeted advertising and recommending content.
According to Forrester’s “Global State of AI in B2B Marketing Survey” AI is used in marketing by two thirds of B2B organizations. The most common uses of AI in B2B marketing are:
- targeting, including advertising and contact syndication (40%)
- personalization (36%)
- marketing automation/tactic orchestration (36%)
- conversation automation, including conversational AI, chatbots, and virtual assistants (33%)
- audience insights (31%)
Using AI for machine learning helps businesses segment and source customer leads, which have historically been identified by analyzing customer data manually. By doing this, sales and marketing personnel can effectively outsource their most complex predictions and decisions to drive programmatic buying in online advertising, e-commerce recommendation engines, and sales propensity models in customer relationship management (CRM) systems.
AI can also facilitate a self-directed customer journey with task automation of repetitive, structured tasks that require low levels of intelligence during basic interactions, taking customers down a defined decision tree.
Possessing such a tool, however useful it might be, is only powerful if you know how to use it. The challenges facing B2B marketers with AI-enabled capabilities are a lack of skills to implement and operate the technology (13%); privacy concerns using AI to mine customer insights (10%); unintended, potentially negative and unethical outcomes (9%); and limited access to the necessary data (8%). It’s not a human and is prone to unexpected outcomes.
Get curious
The best way to discover how AI can work for your organization is to take time to dabble in some of the tools. Basic introductions and tutorials exist to help you get you started. Then, spend time reading and researching. Use your network to learn from others. Dream about ways it can help your organization. Be skeptical of unrealistic expectations. AI isn’t a strategy, but rather a means that might support your plans.
Organizations must decide what vision and direction to adopt, and what sort of business case and KPI’s support it. Take intentional, rules-based steps into AI implementation. Assess what you are trying to do. Where can AI support your efforts? Where might it be risky? Map out how and where AI fits within your workflow by aligning people, processes and technology.
After testing yields satisfactory results, you will be ready to scale. Be critical of the results. Is it effective, ineffective or hurting?
Like any technology, AI reflects good or bad processes – as well as good and bad data. An AI tool is only as good as the information it is fed. Often technology gets labeled as responsible for a bad outcome, but it often holds up a mirror to the process. Theoretically, AI can go as far as you can imagine. It relies on humans to have vision, clarity and creativity to use AI to its fullest.
Otherwise, it’s another greater technological invention that gets put on a shelf and blamed for its poor performance.
Jordan Buning is President of ddm marketing + communications, a leading B2B digital marketing agency for highly complex and highly regulated industries. Throughout Jordan’s 28 years in marketing, he has served clients among a diverse range of industries, including healthcare, financial services and global manufacturing as well as public transportation, higher education, recreational products.
