How Enterprises can leverage the power of Open LLMs and enhance Business Value
3AI September 12, 2023
Author: Raghavendra Prasad, JPMorgan
With the advent of ChatGPT and several Open LLMs in the last few months, most of the business leaders have been pondering on how their enterprises can leverage these technologies to not only enhance customer experience across their products and services but as well as drive efficiencies & innovation internally with their respective enterprises. The fear of being left out by the competition if they do not adopt these new technologies is even more evident across all industries and business leaders.
Enterprises come in varying sizes and the level of adoption that an enterprise can do towards these technologies can vary accordingly. Big Enterprises with deep pockets can explore enterprise version offerings of the LLMs from OpenAI etc., that can come with an essential on-ground support and infrastructure computing costs as per their use cases. Small enterprises who cannot afford either of these will evidently move towards exploring the cheaper alternatives of open LLMs as they would not want to left behind by the competition or be left irrelevant altogether
Most of the use cases within enterprises have been focused towards leveraging vast volumes of textual data to streamline operations, enhance customer experiences and generate valuable insights via Open Large Language Models (LLMs), powered by Advanced Natural Language processing (NLP). However, the adoption of Open LLMs also comes with concerns with Privacy and Trust.
Trust and Privacy are the 2 pillars that are integral to any enterprise that are non-negotiable in the pursuit of innovation and operational excellence. With Open LLMs having democratized access to advanced NLP capabilities to one and all that present immense potential, their use also requires careful consideration of ethical and privacy concerns. As the famous quote from the Spider-Man movie goes “With Great Power Comes Great Responsibility” applies to ethical use of AI enabled technologies and applications of it as well
So how does one preserve privacy and trust in the era of democratized LLMs?
Data Anonymization: Enterprises must ensure that sensitive and personal data is not exposed or used inappropriately when training or deploying Open LLMs. One way to do this is through rigorous data anonymization processes, which involve removing or encrypting personally identifiable information (PII) from training datasets. This is very critical in ensuring data privacy. With several regulations like GDPR and Data Privacy Laws that are prevalent across various jurisdictions, data anonymization is the first critical step in ensuring privacy
Controlled access: Enterprises must limit access to Open LLMs to only authorized personnel. Implement strict access controls and data governance policies to prevent misuse of these powerful models. Build strong guard rails to ensure no exceptions are allowed to the control access ecosystem. It is imperative to control the access, else one lapse could trigger a privacy incident potentially leading major repercussions for the enterprise to do future business with the customers and other enterprises
Differential Privacy: Enterprises must adopt techniques like differential privacy, which inject controlled noise into the data to protect individual privacy while still allowing for useful insights to be derived. This is essential to not expose your complete data and have a way to identify noise over actual insights from the output
Transparency: To build trust, enterprises should be transparent about their use of Open LLMs. Clearly communicate to customers and stakeholders how these models are being utilized and the steps taken to protect their data. This is crucial to establish credibility with your customers and stakeholders on how their data is being leveraged and protected at the same time
Ethical AI principles: Develop and adhere to ethical AI principles that guide the use of Open LLMs. Consider factors such as fairness, accountability, and transparency in decision-making processes. DO NOT compromise on the principles on the agreed use of open LLMs under any circumstances irrespective of the potential benefits that we stand to get from it (“Play by the Rules Always”)
Auditing: Regularly audit the performance and outcomes of Open LLMs to ensure they align with the defined values and objectives of the enterprise that were defined at the onset of usage of Open LLMs for the respective use cases and the benefits that are ought to be derived from it.
Accountability: Establish clear accountability mechanisms for AI-related decisions who would need to held accountable in case of any untoward incident during to usage of Open LLMs. Accountability is one aspect that needs to be clearly established within the enterprise at every stage across functions
Automate: Open LLMs have the potential to automate routine tasks such as customer queries, content generation and data analysis which take a significant portion of an employee’s time in a typical enterprise setup. Automating the routine work will free up an employee’s time to do more complex and creative task, ultimately increasing efficiency and productivity leading to better work-life balance and purpose driven work from an employee well-being perspective
Drive Personalization: Personalization is key for an enhanced user experience across any product or service. Open LLMs can be used to deliver personalized experiences to users that could include recommending products or services that are tailored to an individual needs
Generate Insights: Open LLMs excel at generating insights from data available in public domains such as social media, news articles and customer reviews etc. This could be leveraged to provide valuable competitive intelligence and market analysis
Enterprises can harness the potential of Open LLMs to drive desired business outcomes, provided they address privacy and trust concerns diligently without any compromises. By implementing robust privacy measures, building trust through transparency and ethical practices and leveraging these models for various business applications, enterprises can reap the benefits of advanced NLP technology like Open LLMs without compromising the integrity of their operations or the trust of their stakeholders. Striking the right balance between innovation and responsible use is key to realizing the full potential of Open LLMs in the modern business landscape.
Title picture: freepik.com