How to Overcome the Top 8 Challenges in Generative AI

Generative Artificial Intelligence (GenAI) stands as a transformative force in the digital landscape, promising innovative solutions and creative approaches to data synthesis. However, GenAI faces its fair share of adoption hurdles. Organizations committed to leveraging generative AI must navigate through myriad challenges, ensuring both the solution efficacy and ethical application.

Let’s delve into the top 8 challenges and explore problem-solving approaches to unlock the potential of Generative AI at work.

Challenge 1: Quality of output depends on data inputs             

Generative AI systems are deeply tethered to the data that shapes them. If the input data is biased, incomplete, or erroneous, GenAI’s outputs may be skewed, rendering them unreliable or even harmful. This dependency means GenAI results are directly correlated to the quality of the data employed during the model's initial training.

Similarly, introducing high-quality, ethically-sourced data into a static inference model based on the pre-trained GenAI model enables you to make efficient predictions in near real-time. Strategies that help include:

• Data Auditing: Regularly review and sanitize the data for inaccuracies and biases.
• Diverse Datasets: Look beyond internal data to enable more holistic responses. For example, news data can provide real-world context to inform analysis of customer data.
• Human-led Turning: Implement feedback loops to allow manual adjustment of the inference model to continuously optimize performance over time.

MORE: 6 ways premium news data yields better business performance

Challenge 2: Ethical concerns & accountability in the use of generative AI

As helpful as it can be, Generative AI can produce content that sometimes blurs ethical lines, potentially leading to misinformation, misrepresentation, or misuse.

Moreover, determining who is accountable when an AI system produces harmful or misleading content becomes a critical concern. Putting guardrails in place can help:

• Ethical Frameworks: Establish robust ethical guidelines and usage policies to help ensure GenAI is used responsibly.
• Transparency: Maintain transparency in AI operations and decision-making processes. This transparency should also extend to customers. If GenAI is being used in a chatbot or other customer-facing platforms, telling users up front helps you build trust.
• Accountability Measures: Implement mechanisms to trace and audit AI-generated content. And it wouldn’t hurt to provide digital literacy labs. Just as users needed to learn how to conduct internet searches, identify reliable sources, and apply critical thinking when the world wide web burst into public view, digital literacy is crucial for effective use of GenAI.

MORE: How to capitalize on generative AI to enhance decision making

Challenge 3: Maintaining legal & regulatory compliance in the dynamic environment of generative AI

With the rapid advancement of GenAI, legal and regulatory frameworks will be in a constant state of flux, making adherence challenging. AI operations might inadvertently breach regional or global regulations, leading to legal ramifications. Strategies to consider include:

• Policy Updates: Keep abreast with global policy changes and adapt operations accordingly. For example, the EU is well on its way to creating transparency requirements and other safeguards after passage of a bill in June 2023. Legislators in the U.S. also have generative AI in their radar.  
• Legal Expertise: Engage legal professionals experienced in AI, copyright compliance, and technology law.
• Compliance Audits: Regularly audit AI operations and outputs for compliance with existing and emerging regulations. In addition, validate that the third-party data you source comes from a provider that works with publishers and stays in scope with licensing agreements to ensure data is sourced ethically and legally.

MORE: Why trust is key to success for business

Challenge 4: Maintaining authenticity & originality when using generative AI

There's a risk with GenAI that the content produced might mirror existing works, undermining authenticity and originality. Furthermore, differentiating between AI-generated and human-made content becomes increasingly difficult, raising concerns about genuineness in various fields. To help ensure what’s being generated meets your standards, consider:

• Regular Auditing: Auditing content generated by GenAI appears more than once for good reason. Frequent assessments are a necessity as use and capabilities of GenAI grow. In this case, auditing for the originality helps you mitigate the risk of inauthentic or sub-standard outputs.
• Innovation Inclusion: Continuously integrate new data and ideas to fuel innovative outputs. If the data fueling GenAI isn’t evolving, your outputs won’t evolve either.
• Plagiarism Checks: Use advanced plagiarism-detection tools to ensure content authenticity.

MORE: Analyze market, media, and trends data for better business intelligence              

Challenge 5: Enabling accessibility & usability where generative AI offers greatest value potential

AI tools, especially sophisticated ones, might present steep learning curves or lack accessibility features. This can hinder adoption across varied user demographics, limiting the technology's reach and potential benefits. Develop GenAI solutions with the user in mind with these strategies:

• User-Centered Design: Adopt a user-centered design philosophy to make applications intuitive.
• Accessibility Features: Integrate features that ensure accessibility for differently abled individuals.
• User Education: Provide ample resources and training to facilitate easy adoption among users. Live demos with Q&As, as well as recorded demos and other training materials can help internal or external users get the most value from data delivery and GenAI tools.

MORE: The generative AI trend and it's growing role in tomorrow's workforce

Challenge 6: Ensuring security & privacy

The vast amount of data utilized by AI systems poses significant security risks, and there's potential for misuse or breaches. Additionally, protecting the privacy of individuals whose data is used for training or operations becomes paramount. Whether you’re concerned about IP leakage or accidental use of sensitive, private, or proprietary information, establishing a strong security foundation can help:

• Robust Encryption: Adopt top-tier encryption technologies to secure data inputs and outputs.
• Privacy Policies: Develop and enforce rigorous data privacy policies, including a framework for allowable datasets and data anonymization recommendations.
• Regular Security Audits: Conduct frequent security audits and updates, particularly for data that poses higher risks, such as personally identifiable information (PII).

MORE: The changing roles of risk managers in the age of data, technology, and AI

Challenge 7: Ensuring scalability & adaptability

As your organization increases adoption and use of GenAI, make sure your solutions are designed to scale and adapt accordingly. Doing so without compromising efficiency, speed, or accuracy becomes a complex endeavor, so keep these tips in mind:

• Modular Design: Build AI systems with modular architectures to facilitate scalability.
• Staged Rollout: Some departments, such as those associated with creativity like marketing, have a natural affinity with GenAI. By starting with familiar use cases first, you can build interest and buy-in for further expansion.
• Future-Proof Strategies: Develop strategies that cater to future expansions and adaptability.
• Resource Planning: Implement strategic resource planning to accommodate secure growth. As PWC notes, “The key to rapid ROI and far-reaching transformation through GenAI is a focus on discipline, scale and trust.”

MORE: How enriched data can help you track your business KPIs

Challenge 8: Addressing the societal impact and public perception of generative AI

The rapid rise of AI technologies has led to both awe and skepticism among the public. On any given day, you can find plenty of print and broadcast news covering GenAI, likely running the gamut from “the greatest thing since sliced bread” to “robotic doom and gloom.”  Balancing technological advancements with societal impacts is crucial, as is managing public and internal perceptions to ensure trust and beneficial integration.

• Public Engagement: Engage with the public and stakeholders to build trust and gather feedback.
• Social Impact Analysis: Assess and address the societal impacts of AI applications, particularly in areas where inadvertent bias could reflect poorly on your organization.
• Ethical Operations: Ensure operations align with societal norms and ethical considerations.

MORE: The responsible Generative AI checklist

Unleashing the promise of Generative AI

Navigating through the complexities and challenges of GenAI demands a holistic, ethically grounded, and strategic approach. Organizations that conscientiously approach these hurdles not only enhance the value potential of GenAI across the enterprise while also setting precedents for responsible and innovative AI use.

Download the  LexisNexis® Future of Work Report 2024: How Generative AI is Shaping the Future of Work to continue exploring this unfolding landscape and its multifaceted impact on our digital future.