5 Key Strategies for Effective AI-Driven Communication in Business Operations

How can businesses harness the best that an AI-powered future offers? By implementing five key strategies, they can effectively embrace AI-driven communication and unlock its potential to enhance internal operations and customer experiences.

Whether it’s instant chat features, AI-powered answers, call transcription and summaries, or multi-lingual content creation, 97% of business owners believe ChatGPT will enhance customer and internal communications.

The use cases for AI-driven chatbots are extensive, but scaling AI poses data reliability and security challenges.

While many companies are keen to adopt AI across multiple departments, only roughly 50% of projects transition from pilot to implementation, according to a Gartner survey. IT teams need help meeting numerous business needs at once whilst staying ahead of the crowd.

The promise of AI is immense. Let's look at the proactive strategies businesses can implement to embrace AI-driven communication successfully.

1. Connect AI algorithms to the business value proposition

The AI revolution reminds us of the saying, “data is the new oil.” Unrefined oil is of little use, yet when properly refined, it creates tremendous value in powering planes, cars, and our energy needs. The same applies to the data used in your AI: Refined data creates powerful tools.

What can business leaders do to distill high-quality data? Rather than overloading ChatGPT or other AI tools with company data to handle every use case, data teams need to be clear on what constitutes the most pertinent data for their intended AI use case. Data needs to be filtered as part of the development of the AI model. Bad data INPUT means bad AI OUTPUT.  Think about what is the RIGHT data and its relationship to the value proposition you are building.

For example, in sales, AI can help agents handle buyer objections. Business leaders need to pair customer product requirements, pricing, and support needs with the appropriate objection-handling responses.

Generative AI can provide real-time prompts for agents to handle sales objections and also enable sales agents to store and rate responses. Separate reinforcement learning algorithms will process the data, refine the model's training, and improve future accuracy.

2. Support customers with effective prompting

AI NLP (Natural Language Processing) input fields like ChatGPT are freeform; you can ask it anything. The model statistically selects the most likely output based on the surrounding message context. But the specific reasoning needs to be clarified, and depending on the clarity of the prompt, the tool can produce very different results.

To increase response accuracy, always provide context, and be specific in tone, structure, and output type. For instance, say you are building a tool for customers to obtain car quotes. A customer asks the chatbot: ‘Can you give me three quotes for an off-road car for a family of four.’ Before providing three quotes, set the model to ask several clarifying questions; what is your budget? Do you have a preferred model? Do you require any special features?

The tool can refine the search with user-facing prompts to better understand the customer’s needs and ultimately deliver better results. The idea is to drive specific intent. By prompting the customer, you enable AI to serve the best possible customer experience.

3. Define the level of risk in the use of AI

What is the level of risk? Is the use case where you want to implement AI subject to data protection, existing regulation, or legal compliance?

For example, AI-powered personalized medicine has great potential for improving patient outcomes and overall efficiency. However, feeding confidential documents, individual health data, proprietary IP, and other protected information into AI models require data security methods to safeguard information.

Of the companies in the Gartner survey who stated they had experienced data breaches, 60% reported an internal party compromised the data. Data theft is a real risk in any business today, and the use of AI expands data accessibility. Companies with highly confidential data must weigh the costs and complications of securing their data effectively. In every scenario, ensuring company-wide staff training on the risks of your AI use cases is vital.

It is worth noting that not using AI is also a business risk, especially when 48% of respondents believe advanced AI will be as important as the creation of the Internet. A recent survey noted that a quarter of generative AI users found answers faster with it than compared with a traditional search engine.

4. Create a data governance strategy

Using generative AI to improve communication in business can be a powerful tool, but it also brings several challenges, especially concerning data privacy, security, and ethical considerations. This is where creating a data governance strategy becomes crucial.

Data teams and business leaders must establish governance policies and create data visibility with detailed authorization lineage and consent policies. If using third-party data sources, ensure due diligence on the authorized use of this data. A data governance strategy helps establish guidelines and protocols to ensure compliance with data protection regulations (e.g., GDPR, CCPA), privacy laws, and industry standards. Adhering to industry standards such as SOC 2 compliance, which audits and certifies the quality and reliability of data security and privacy controls, will help ensure the integrity of client data.

5. Keep the human-in-the-loop

According to Forbes Advisor, 77% of workers expressed concern about job loss due to AI adoption. However, AI can only function with sufficient human oversight. AI offers immense potential, but human involvement remains crucial, hence the human-in-the-loop (HITL) concept.

The HITL serves a variety of vital services. Foremost, humans can review and curate the training data for the chatbot. This process involves filtering out inappropriate or misleading content and ensuring the data is diverse and representative. In this regard, human oversight ensures the ethical and responsible use of AI.

Moreover, the HITL can monitor AI responses in real-time. This allows authorized team members to intervene if a chatbot provides inaccurate or harmful responses, or if the conversation goes off-topic or becomes inappropriate. A human's provision of context, empathy, and critical thinking is vital. Human moderation not only helps maintain a high level of quality and ensures that the chatbot adheres to ethical guidelines, but by moderating responses, they can improve the data set that the AI model is working from.

Summing up

Businesses across every industry are seeing the impact of AI-driven communication. To drive positive results, they must carefully evaluate their value proposition, data sensitivity, the tools’ limitations, and governance strategies to mitigate them. And, always remember, overseeing success is the human-in-the-loop.