Opinion by Nell Watson
écrit le 10 June 2024
10 June 2024
Temps de lecture : 8 minutes
8 min

Would you trust AI to speak to your customers? 5 ways AI could hurt your brand voice

Artificial Intelligence has made remarkable strides in recent years, revolutionising many sectors, particularly creative industries. These AI systems, fueled by deep learning on vast datasets, have demonstrated increasingly versatile and adaptable capabilities. However, they still primarily function as tools, providing outputs to narrowly defined human queries.
Temps de lecture : 8 minutes

We are now on the brink of a new era in AI: agentic AI systems that can autonomously pursue open-ended objectives by taking sequences of actions in complex environments. Agenticness refers to the extent to which an AI system can adaptively achieve intricate goals in multifaceted environments with minimal direct supervision. Agentic AI builds upon deep learning but is distinguished by greater autonomy, adaptability, and the capacity for independent decision-making and long-term planning.

The rise of agentic AI systems, with their ability to act independently, innovate, and tackle complex problems, is set to transform the startup landscape. These AI systems can enhance decision-making, streamline processes, personalise customer experiences, drive innovation, and optimise logistics.

However, as entrepreneurs eagerly embrace this technology, they must also be mindful of the potential pitfalls, especially when it comes to maintaining a consistent and authentic brand voice. The increased autonomy and complexity of agentic AI introduces new challenges in ensuring that AI-generated content and interactions align with a startup's unique identity, values, and messaging.

Here are five ways agentic AI could potentially undermine your brand voice if steps aren't taken to carefully mitigate these risks:

  1. Inconsistent tone and style: One of the primary risks of using agentic AI for content creation is inconsistency in tone and style. AI systems, even when trained on a startup's existing content, may generate text that strays from the established brand voice. For instance, an AI system trained on a startup's blog posts might produce content that adopts a more formal tone or incorporates jargon not typically associated with the brand. This inconsistency can confuse customers and dilute the brand's identity. To avoid this, entrepreneurs must provide clear guidelines and examples for the AI to follow, regularly monitor the output, and invest in human oversight to ensure consistency. It's essential to establish a well-defined brand voice and continuously train the AI system to adhere to it. Furthermore, implementing a review process that involves human editors can help catch and correct any inconsistencies before content is published.
  2. Lack of emotional connection: Agentic AI, while capable of generating human-like text, may struggle to capture the emotional depth and nuance that is crucial for building strong customer relationships. A startup's brand voice often relies on establishing an emotional connection with its target audience, conveying empathy, passion, and shared values. AI-generated content, if not carefully crafted, can come across as generic, impersonal, or lacking in emotional resonance. For example, an AI-powered chatbot might provide accurate information but fail to convey the warmth and understanding that a human agent could offer. To mitigate this risk, entrepreneurs should prioritise the human touch in their content creation process. They can leverage AI to streamline certain aspects of content generation, such as research and data analysis, but ensure that the final output is shaped by human creativity and emotional intelligence. By harnessing the strengths of both AI and human talent, startups can create content that not only informs but also inspires and resonates with their audience on a profound level.
  3. Misalignment with brand values: Agentic AI systems, if not properly aligned with a startup's core values and mission, can generate content or make decisions that contradict the brand's ethos. This misalignment can erode customer trust and tarnish the startup's reputation. For example, an AI system tasked with generating social media posts might inadvertently create content that is insensitive, offensive, or inconsistent with the brand's stance on certain issues. To prevent this, entrepreneurs must ensure that their AI systems are trained not only on the startup's content but also on its values, mission statement, and ethical guidelines. They should implement strict content filters and regularly audit AI-generated output to identify and correct any instances of misalignment. Moreover, having a diverse team of human overseers can help provide multiple perspectives and catch potential issues early on. Ethical AI frameworks, such as the IEEE Ethically Aligned Design guidelines, can also serve as valuable resources in guiding the development and deployment of AI systems that align with a startup's values.
  4. Overreliance on AI: As agentic AI becomes more sophisticated and efficient, startups may be tempted to rely too heavily on automated content creation and customer interactions. While AI can undoubtedly streamline processes and save time, overreliance on technology can lead to a loss of authenticity and human connection. Customers may begin to feel that they are interacting with a machine rather than a brand with a unique personality and story. To avoid this, entrepreneurs should strive for a balanced approach that leverages AI's capabilities while still maintaining a strong human presence. This can involve using AI to handle routine tasks and data analysis, while reserving more complex and nuanced interactions for human team members. Additionally, startups should be transparent about their use of AI and emphasise the human element in their brand messaging. For instance, a startup could communicate that while AI helps streamline certain processes, the final output is always reviewed and refined by a team of human experts passionate about delivering exceptional experiences.
  5. Lack of adaptability: Agentic AI systems, once trained, may struggle to swiftly adapt to changes in a startup's brand voice or market conditions. As startups grow and evolve, their brand voice may need to shift to reflect new priorities, target audiences, or industry trends. If the AI system is not regularly updated and retrained, it may continue to generate content that is no longer aligned with the startup's current identity. To mitigate this risk, entrepreneurs should prioritise continuous learning and adaptation in their AI systems. This involves regularly feeding the AI with updated content, guidelines, and feedback to ensure it stays in sync with the brand's evolution. Additionally, startups should foster a culture of experimentation and iteration, allowing room for the AI system to learn and improve over time. For instance, a startup that initially targeted a younger demographic but later expanded to include older audiences could retrain its AI system with content that resonates with the new target group, ensuring that the brand voice remains relevant and engaging.

Building trust in technology

Agentic AI has the potential to be a game-changer for startups, but it also presents new challenges in maintaining a consistent and authentic brand voice. By proactively addressing inconsistencies, lack of emotional connection, misalignment with brand values, overreliance on AI, and lack of adaptability, entrepreneurs can harness the power of AI while preserving the human essence that makes their brand unique.

Transparency in AI-driven processes is paramount. Customers and ecosystem partners must understand what a system is doing, in what way, and for whose benefit. This provides a foundation from which one can analyze models for disproportionate or unfair biases, along with the data that powers them. This can enhance the accountability of systems, so that if things go awry, we can understand why and prevent such occurrences in the future.

Novel cryptographic techniques can help us safeguard personal data. Homomorphic Encryption, for instance, enables us to perform machine learning processes on encrypted data. It's akin to sharing the shape of data but not its texture. In other words, Homomorphic Encryption allows AI systems to process and learn from data without actually "seeing" the raw, unencrypted information. Just as we wouldn't want to conduct our online banking without the little padlock in our web browser assuring us of a secure connection, soon consumers and legislators will demand similar protections in how we interact with AI.

Furthermore, the newfound autonomy of agentic AI systems means that issues of Value Alignment and Goal Alignment, previously theoretical, are now becoming critical for steering agentic processes. If machines are to act as concierges at arm's length, they need to have a keen awareness of our intentions and boundaries, not simply working according to their instructions. For example, an AI system designed to optimize a factory's production efficiency should not only focus on increasing output but also consider factors such as worker safety, environmental impact, and product quality. Misaligned goals can lead to unintended consequences, while properly aligned AI systems can make decisions that balance multiple objectives and align with human values. These issues represent an order-of-magnitude increase in challenges alongside capabilities.

Fortunately, we have new standards, certifications, and professional credentials that can provide a robust foundation for the design, deployment, and maintenance of AI solutions. The Turing Institute's AI Standards Hub offers a compendium of resources for securing AI in a range of industries, jurisdictions, and risk levels. These standards and certifications can help startups navigate the complex landscape of AI ethics and safety, providing guidelines and best practices for developing and deploying AI systems that are transparent, accountable, and aligned with human values.

I also highly recommend examining the AI Incident Database, as it provides a powerful resource for horizon scanning to understand the risks of using AI, including the resulting scandal and blowback for those deploying it.

If business leaders can comprehend the risks associated with AI technologies and certain use cases, along with the means to mitigate those risks, we have an incredible opportunity to enjoy AI in a consistently safe, humane, and beneficial manner.

Nell Watson is an AI expert, ethicist and author of Taming the Machine: Ethically harness the power of AI.

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