Trend 4
Consumer concerns
Ethical AI is the key to innovating without alienating
Social and corporate responsibility was already a key point of brand differentiation for a select group of customers but, as companies increasingly adopt AI-powered tools and solutions, ethical consumerism — especially around the application of technology and personal data — is poised to go mainstream as a defining characteristic of brand loyalty.
Along with the excitement about generative AI, there are genuine concerns that require immediate attention. Business leaders must address questions about the checks and balances in place to ensure models are fair and unbiased. What safeguards are in place to prevent harmful outputs, often referred to as "hallucinations"? Transparency isn't merely a compliance requirement; it's essential for maintaining customer trust. Companies must be forthright about their data collection, storage and use, particularly in relation to fine-tuning models. Despite its immense potential, generative AI risks undermining long-term brand integrity and customer confidence if organizations neglect to establish robust governance frameworks. Such a framework should prioritize accountability and proactively mitigate adverse impacts on individuals, industries and society as a whole. Against this backdrop, the delicate balance between ethics and innovation becomes crucial. As we move toward 2025, building trust — rather than hyper-personalization — will emerge as a pivotal trend. Any level of personalization relies heavily on customer data, and consumers are increasingly discerning about whom they share their information with and why. Thus, building trust through ethical AI practices will be paramount in the years ahead.
Learn more: Balance the risks and rewards of generative AI.
Balancing the risks and rewards of a GenAI implementation
As organizations adopt generative AI, it's essential for business leaders to implement robust strategies that harness the technology’s potential while identifying and managing associated risks.
Identify and mitigate initial risks
New technology means new risks, and there are fraud and cybersecurity vulnerabilities unique to AI such as data poisoning. Look at operational, reputational, compliance, and strategic risks and create a comprehensive risk management framework, and remember that risk assessments will need to be refreshed on a regular basis to stay aligned with technological developments and reflect lessons learned.
Raise awareness
AI will touch all aspects of an organization and so training will need to be business wide. As well as improving skills, the training should be focused on ethical use of, and potential risks associated with, the technology. This will ensure employees are confident about using AI and about questioning or validating its outputs.
Establish governance structures
Create a cross-departmental steering group to oversee risk management and regulatory compliance and integrate governance into existing operations. Consider using outside experts that specialize in AI governance and risk management, and that can stay aligned with the pace of technological change; to strengthen governance and ensure you remain aligned with industry standards.
Develop mitigation strategies
Engage different organizational departments to work together in identifying potential risks as generative AI is deployed. This can be facilitated through structured regular workshops, brainstorming, or feedback sessions and findings relayed to the steering group overseeing governance.
Monitor
Generative AI needs technical and non-technical risk management strategies. As well as collecting, analyzing and sharing data related to performance, you need to conduct regular audits of use and outputs to ensure alignment with ethical standards.