As we build technology that helps machines get smarter, this will change. AI can serve a purpose in all departments within an organization, including sales, customer service, marketing, and operations.
But to get there, a company must carefully evaluate AI applications and expected outcomes versus risk.
Overall, the processes best-suited for today’s AI implementations are those that are high-volume, repetitive, follow patterns, require limited judgment, and carry low cost in the event of a mistake. So far, most AI-based automation is occurring at the periphery of a business, where companies are testing its value and effectiveness while maintaining core business processes and decisions as human-directed.
For example, in high-ticket B2B sales scenarios, interaction with salespeople is one of the most important factors influencing buyer decisions. AI-based chatbots, such as automated online financial advisers, have the potential to smooth out these sales processes once smart enough to learn when a salesperson should be brought into the conversation. An AI system can be trained to have better responses to transactional requests based on historical chat data, as well.
In marketing, we can expect AI to become very good at personalization and microsegmentation. Realizing the potential of this market and the issues AI can address, one question remains: How do marketers begin implementing AI and taking advantage of all it has to offer? To capitalize on its benefits, we can look to a simple but effective checklist to determine where AI is applicable and how to successfully implement it:
1. Start by identifying the business problem. Decide where using AI can improve efficiency. For example, in commercial airline travel, AI could potentially automate the next step for when a customer misses a flight by suggesting alternatives for another flight.
2. Distinguish the data source and focus on collecting data from the relevant customer touch points. When the missed flight is noted, the airline should have internal data on the customer’s travel history and preferences to incorporate into the AI-based suggestions.
3. Develop an AI-based solution to aid algorithmic decision making. Make use of Natural Language Processing to help AI understand the request and utilize real-time data to identify alternative flights.
4. Once the AI solution is developed, it can be implemented and training provided. Staff must work alongside AI, understanding its use and applications and the right times to intervene on behalf of the organization and the customer.
With the march to AI in marketing and other business operations becoming more clear, organizations must begin thinking about its implementation and use cases now. As with other similar disruptive technologies, early adopters may be the first to achieve early successes.