Preparing organizations for AI adoption
Authors: Louis Lehot and Natasha Allen
Natasha Allen and Louis Lehot of Foley & Lardner LLP discuss key steps organizations can take when looking to adopt artificial intelligence tools, including ways to address regulatory compliance, data governance and intellectual property concerns.
Artificial Intelligence (AI) will revolutionize business operations, offering unprecedented opportunities for efficiency, innovation, and competitiveness. It is no longer a question of “if” but rather a question of “when” and what will be the impact to employment and cost structure.
Incorporating AI technologies has become indispensable for every kind of organization. Alongside its benefits, however, come legal considerations that organizations must navigate to ensure compliance, mitigate risks, and maximize the benefits of AI adoption.
Before embarking on a full-scale AI implementation, understand your organizations risk tolerance, understand your capabilities from a technical and business processes point of view. Identify where you have gaps and where you may need to acquire and internally develop to achieve your identified AI results. Does the organization have the skillset/domain expertise to execute an AI vision?
Below, we examine some of the key steps organizations can take when looking to adopt AI tools:
(1) Define your goals and identify capabilities: Assessing the organization’s risk tolerance as it relates to AI, and setting clear goals from the beginning is vital to the success of your project. Consider where there is a need for improvement in your organization, how AI capabilities complement your existing services, and identify cases where AI can provide business solutions and deliver provable value. Before embarking on a full-scale AI implementation, it is also important to understand your capabilities from a technical and business processes point of view. Identify where you have gaps and where you may need to acquire and internally develop to achieve your identified AI results.
(2) Understand the legal and regulatory environment: The legalities surrounding AI are evolving, with new regulations, case law, and industry standards emerging regularly. Data privacy, consumer protection, and intellectual property rights regulations may apply depending on the industry and jurisdiction. For example, the General Data Protection Regulation (GDPR) imposes strict requirements on processing personal data, including using AI algorithms. Organizations can develop AI systems that align with legal requirements by identifying relevant regulations early on.
Stay informed and adapt your AI strategies accordingly. This may involve monitoring regulatory updates, participating in industry associations, and seeking guidance from legal experts specializing in AI.
(3) Conduct a legal risk assessment: Organizations should conduct a comprehensive legal risk assessment before deploying AI technologies to identify potential liabilities and mitigate risks. This assessment should cover data privacy, security, intellectual property rights, and liability for algorithmic decisions. By identifying legal risks early in the process, organizations can implement appropriate safeguards.
(4) IT infrastructure: Decide who will develop and implement the technological solution for this initiative. Will you tap someone within the organization or look for outsourced partners?
(5) Implement robust data governance practices: Robust data governance practices are essential for AI adoption, as data forms the backbone of AI systems. Organizations must establish clear policies and procedures, as some data may be subject to GDPR, HIPAA compliance, or other legal and regulatory controls.
(6) Address bias and fairness concerns: AI algorithms are susceptible to biases inherent in the data used to train them, raising concerns about fairness and discrimination. Organizations must implement measures to detect and mitigate bias throughout the AI lifecycle, including bias assessments, training data diversification, human review of AI outputs, and fairness-aware algorithm implementation. Prioritizing fairness and transparency helps organizations build AI systems that uphold ethical standards and mitigate legal risks associated with discrimination.
(7) Establish intellectual property rights: Intellectual property rights play a crucial role in AI adoption as organizations seek to protect their innovations and competitive advantages. Organizations must identify and protect their intellectual property assets, including algorithms, datasets, and AI-generated outputs. This may involve obtaining patents, implementing trade secret protection, and entering licensing agreements. Organizations should also audit what content is internally generated versus use AI technology to mitigate against copyright infringement. By establishing intellectual property rights, organizations can safeguard their investments in AI technologies and mitigate the risk of infringement.
(8) Foster collaboration with legal and technical experts: Effective AI adoption requires close collaboration between legal and technical experts to address complex challenges. Legal professionals must work closely with data scientists, engineers, and other stakeholders to ensure AI systems comply with legal requirements and ethical standards. Organizations can develop AI solutions that balance innovation with legal compliance and ethical considerations.
While AI presents opportunities for innovation and competitiveness, it also poses legal challenges that organizations must address proactively. It is critical for organizations to work with legal and technical professionals to ensure they are adopting AI technology responsibly, especially as the technology and regulations surrounding it evolve rapidly.
Businesses that survive and thrive will adapt to implement AI technologies. Businesses that ignore AI will not be able to compete. Intelligent adoption of AI will be a competitive advantage in the marketplace.
Originally published at https://today.westlaw.com.
