Capitolo 3
Customer Service and AI
The COVID-19 pandemic accelerated digital customer interactions, with 53 percent of brands adding messaging channels in 2020. Companies like Clorox embraced AI-powered conversational agents that use natural language technology to understand and respond in humanlike ways.
AI-powered chatbots and virtual agents now handle customer inquiries at scale across text and voice platforms. Using NLP and NLG, these tools understand human language and create humanlike responses, handing off to human agents when appropriate. The most sophisticated systems can detect sentiment in customer messages and take actions based on mood and needs.
Today's AI systems can identify which customers are most likely to churn and flag dissatisfaction during engagements, allowing brands to take preventative action. AI tools also surface relevant customer data from CRM systems when customers call or chat, giving reps immediate access to detailed information for personalized conversations.
Companies like Amazon, Drift, Google, LivePerson, Microsoft, Thankful, and Zendesk offer AI-powered customer service solutions that transform customer experiences while reducing costs.
Email marketers are increasingly turning to AI because human intuition, while valuable, often falls short when dealing with email strategy at scale. The numbers are staggering - 293 billion emails were sent daily in 2019, with projections showing 347 billion by 2022, creating an intensely competitive landscape for attention. Research shows that 50% of consumers make their open/delete decision in less than 3 seconds based solely on subject lines, while modern email clients employ sophisticated algorithms to block, filter, and automatically sort communications into primary, promotional, or spam folders.
AI transforms email marketing through multiple sophisticated capabilities. At the data level, AI systems evaluate vast databases of contact information, automatically validating email addresses to ensure deliverability and reduce bounce rates. The technology excels at advanced list segmentation, analyzing hundreds of variables simultaneously to create highly targeted audience segments - a task that would be impossible for human marketers to manage manually.
Modern AI tools can conduct natural-language email conversations with prospects, responding intelligently to inquiries and nurturing leads through automated yet personalized exchanges. Contact database management is revolutionized as AI systems clean records, remove duplicates, and enrich contact information by pulling from multiple data sources. Subject line optimization becomes data-driven, with AI analyzing millions of previous email campaigns to generate and test variations that drive higher open rates.
Companies are seeing remarkable results with AI-powered solutions. Phrasee's natural language generation technology creates subject lines that consistently outperform human-written versions by 2-3%. Rasa.io's AI engine personalizes newsletter content for each recipient based on their interaction history, increasing engagement rates by up to 50%. Seventh Sense's send time optimization analyzes individual recipient behavior patterns to deliver emails at the precise moment when they're most likely to be opened, achieving open rate improvements of 20-35%.
Beyond these core functions, emerging AI capabilities include sentiment analysis to gauge recipient response, predictive analytics to forecast campaign performance, and dynamic content optimization that automatically adjusts email content based on real-time engagement data. The technology is also becoming more sophisticated at A/B testing, simultaneously testing multiple variables while continuously learning and improving from results.
McKinsey Global Institute's analysis found that AI could create $9.5-$15.4 trillion in annual value globally, with marketing and sales leading at a projected $3.3-$6.0 trillion. Despite this potential, most businesses are still in early adoption stages. The 2021 State of Marketing AI Report found that while 52% of marketers consider AI very or critically important, only 17% have reached the scaling phase.
To scale AI successfully, organizations should follow a ten-step blueprint:
1. Think Strategically: Approach AI as you would any marketing technology investment - it must solve real business problems by reducing costs or increasing revenue.
2. View Data as Essential: Start by identifying opportunities to extract more value from your data. According to Accenture, 72% of successful AI scalers credit a core data foundation for their success.
3. Become an Informed Buyer: Use the Marketer-to-Machine Scale when evaluating existing marketing technologies and researching new AI-powered additions.
4. Prioritize Use Cases to Pilot: Focus on one use case at a time. AI thought leader Andrew Ng advises prioritizing success over value in your first AI projects to build familiarity and convince others to invest further.
5. Define Priority Business Goals: AI initiatives must focus on reducing costs or accelerating revenue. Early wins often come from cost-saving projects, but long-term vision should include revenue growth.
6. Educate and Engage Leadership: Leaders need to understand that early AI pilot projects may not always meet goals, but these setbacks shouldn't derail long-term plans.
7. Reimagine Your Marketing Team: New roles will emerge, like marketing AI specialist, AI ops leader, AI trainer, machine manager, recommendation engine director, and chief algorithms officer.
8. Train Your Team: With 70% of marketers citing lack of education as the top barrier to AI adoption, organizations must develop comprehensive learning programs.
9. Focus on Mutual Learning: Organizations that systematically invest in continuous human-machine learning are 73% more likely to achieve significant impact with AI.
10. Consider How AI Can Make Your Brand More Human: AI has the potential to make brands more human by freeing marketers to focus on listening, relationship building, creativity, empathy, culture, and community.
Even the best brands struggle with AI implementation, as illustrated by Apple Card's problematic launch in 2019 when its algorithm was labeled "misogynist" due to apparent gender bias. AI systems inherit biases from their training data, whether through direct discrimination based on characteristics like gender or race, or through faulty data producing unexpected outcomes.
Creating an organizational AI ethics policy is essential for responsible AI use. This formal document should outline your company's position, specify acceptable uses, and detail steps taken to prevent ethical issues. Leading companies like Adobe have established robust ethics frameworks, including AI ethics committees, review boards, and impact assessment tools during product development.
According to BCG research, 55% of companies are less advanced in responsible AI implementation than their executives believe. BCG identifies seven dimensions of responsible AI: accountability, transparency and explainability, fairness and equity, safety and security, data and privacy governance, social and environmental impact mitigation, and human-AI collaboration.
Rather than viewing AI as a cost-cutting tool, leaders should see it as a way to redistribute resources and invest in customers, employees, and communities. Human-centered AI delivers personalization and convenience to consumers while respecting privacy, and removes repetitive tasks for employees so they can focus on uniquely human skills like compassion, creativity, and empathy.
As Kai-Fu Lee, former president of Google China, realized after his cancer diagnosis, the one thing machines lack is the ability to love. We can teach machines to predict, see, hear, and understand, but we cannot make them human. The future is marketer plus machine, making marketing more intelligent and brands more human.
Humans naturally think linearly, predicting the future based on past experiences. However, technological change is exponential, not linear. Ray Kurzweil noted in 2001 that we won't experience 100 years of progress in the 21st century but rather 20,000 years of progress at today's rate.
The common question "Will AI take my job?" misses the point. For college students, AI presents unprecedented opportunities that require lifelong learning. For practitioners at all career stages, AI will affect every aspect of work and life. For leaders, AI represents the next frontier in digital transformation.
Consumer expectations and business practices have changed forever, and waiting to adopt AI means falling behind as first movers gain compounding advantages. Success doesn't require technical expertise in machine learning, but rather competency in AI capabilities to reimagine careers around uniquely human skills.
The authors have announced their mission to introduce AI to one million marketers by 2026 - representing 10% of global marketers - through content, webinars, conferences, podcasts, reports, tools, and courses. Their goal is to build a community of next-gen marketers who will reinvent marketing, build smarter brands, and make marketing more human.
The marketing revolution is here, and it's powered by AI. The question is no longer whether to adopt AI, but how quickly and effectively you can integrate it into your marketing strategy to create competitive advantage. The future belongs to those who understand how to combine the best of human creativity and empathy with the power of intelligent machines.
