In a world driven by data and technology, the evolution of artificial intelligence (AI) continues to astound us. One facet of AI has captured the imagination of businesses across various industries – Generative AI, often referred to as Gen AI.
Generative AI (Gen/AI), is a branch of artificial intelligence specially crafted to create innovative and imaginative content, encompassing text, images, and even music. A “Large Language Model” (LMM) is a type of artificial intelligence model designed to understand and generate human language. These models are pre-trained on vast text datasets from the internet, enabling them to learn grammar, semantics, and context.
Generative AI (Gen AI) and Large Language Models (LMMs) work together to enhance natural language processing. Gen AI contributes to LMMs by providing the foundation for understanding and generating text.
LMMs leverage Gen AI’s pre-trained knowledge to comprehend context, grammar, and semantics effectively. LMMs, in turn, refine their behavior through fine-tuning, aligning with specific applications. This partnership enables LMMs to generate human-like text responses, making them valuable in diverse linguistic applications.
Generative AI empowers organizations to do more with less, optimize operations by augmenting workforce skillsets, and automating mundane tasks, freeing up existing resources to focus on more valuable work, and stay ahead in today’s competitive business world.
Generative AI can automate tasks such as content creation, streamline operations, enhance customer experiences through tailored conversations and recommendations, and reduce operational costs by augmenting workforce activities.
Businesses can also benefit from consistency in tasks like customer support, which helps maintain a uniform brand image. Generative AI can help streamline processes, elevate customer experiences, and spark innovation in ways we could scarcely imagine just a few years ago.
Embracing Generative AI offers businesses numerous advantages, including automated content generation, personalization, efficiency, and cost savings.
In this blog, we are joined by our resident GEN/AI expert to answer some critical issues about this exciting technology.
Our Gen AI Expert: Ian Clayton
Ian is the go-to person when it comes to Generative AI, making complex tech stuff easy to understand.
Now, let us delve into some common questions surrounding Generative AI:
Q: Are there any ethical concerns associated with using GEN/AI within an organization?
Ian: Yes, there are ethical concerns from various perspectives. Let us take three obvious ones, personal, Gen/AI technology providers, and industry sectors.
Individuals using Generative AI must consider issues like privacy and the potential misuse of generated content. For example, a marketing team using AI to generate customer profiles should ensure they have consent and transparent policies for data usage.
Technology providers and enablers should commit to transparency by disclosing their data sources and algorithms so the decision-making process can be trusted. They should also adhere to a kind of industry wide ‘AI manifesto’ that includes assurances that their platforms operate to an ‘island’ principle, where all data, prompt activity, and responses, do not escape or interact with other Gen/AI environments.
Already, Gen/AI is in heavy use within many industry sectors. Different sectors face unique ethical challenges and must weigh the amazing benefits with the inherent risks. In healthcare, for instance, AI-powered diagnostics must prioritize patient privacy and accuracy. In finance, AI-driven trading algorithms should follow stringent guidelines to prevent market manipulation.
Obviously, we need a family of ethical frameworks developed and promoted that address each of these concerns, promoting responsible use while respecting user rights, fostering trust, and aligning with sector-specific needs. Much of this development work has begun.
Q: What privacy considerations should organizations be aware of when implementing GEN/AI?
Ian: Privacy, well I think that must be separated into at least two areas. Personal privacy, and organizational trade secrets and intellectual property. The first is easy, everyone involved should respect an individual’s privacy. This means obtaining clear consent when collecting their data and ensuring that it is stored and processed securely. Think of it as being a good digital neighbor and following all relevant data privacy laws, like GDPR or HIPAA, depending on your industry.
If your organization has its own unique AI sauce, it is crucial to protect it. Keep your algorithms, datasets, and AI models under lock and key. This involves using encryption, setting up access controls, and having non-disclosure agreements in place, especially with employees and partners who have access to your AI secrets. Again the ‘island’ principle can help here.
Q: How can organizations maintain quality control over GEN/AI-generated content?
Ian: I am reminded of the adage, “garbage in, garbage out (GIGO).” One of the biggest reasons for a Gen/AI system provide suspect responses, to ‘hallucinate,’ is the input it receives, in the form of how information is requested by a user, the ‘prompt.’
Some form of training in prompt engineering is paramount as it empowers individuals to effectively harness the capabilities of AI systems by crafting precise and instructive queries, raising the likelihood of meaningful and accurate results. This is the number one reason I authored the ‘ChatGPT for Beginners’ at the beginning of 2023. At that time, it was one of the first books on the topic.
The organizational offering or maintaining the model should review and verify the trustworthiness of any data sources, fine-tune the model to improve the AI’s understanding of niche topics, and include a feedback mechanism where users can rate the AI-generated responses, and be input to regular model updates.
Q: Are there security risks associated with using GEN/AI, and how can they be mitigated?
Ian: Yes, obviously, security is a massive concern. A disabling one. Like the early days of cloud computing, individuals and organizations must have confidence that their data and conversations are secure. Given how pressing and vital this topic is, I am confident the industry will solve this and provide assurances today versus tomorrow. That said, we should all use the same security principles used now, including encryption, access controls, and regular security audits.
Q: Is overreliance on GEN/AI a potential issue, and how can organizations avoid it?
Ian: I worry about this myself all the time. ChatGPT and other systems have become my personal assistants. I rely on them being accessible. Individuals and organizations should have contingency plans in place for when GEN/AI systems are unavailable, fail, or produce incorrect results. Maintaining a balance between GEN/AI and human expertise is essential to avoid this pitfall.
Q: Can GEN/AI inadvertently generate misinformation, and how can organizations prevent this?
Ian: Indeed, there is a possibility of GEN/AI inadvertently generating misinformation, as the system’s output is heavily reliant on the quality of the input data and the precision of the prompts provided.
To guard against this, organizations should establish robust content verification procedures. They can implement automated fact-checking mechanisms and invest in AI tools designed to detect and correct inaccuracies in generated content.
Additionally, it is crucial to empower human users with the responsibility of verifying and validating AI-generated information. While AI systems are becoming more sophisticated in providing citations and sources, human oversight remains a critical element in ensuring the accuracy and integrity of the information that is disseminated.
By combining the strengths of AI and human expertise, organizations can effectively mitigate the risk of misinformation and uphold the reliability of their output.
Q: What financial considerations should organizations keep in mind when adopting GEN/AI?
Ian: Costs for organizations can be substantial. When organizations embark on the adoption of GEN/AI, they must consider various financial aspects. Significant expenses include infrastructure, which encompasses essential hardware and software components like computing resources, storage solutions, and network capabilities.
Data storage is critical to accommodate the vast amount of data generated and used by AI systems. Ongoing training is necessary to keep the workforce updated on evolving AI technologies and best practices.
Additionally, organizations should anticipate maintenance costs as GEN/AI systems often operate on a metered basis, charging for data traffic. A well-defined financial plan or business case is essential to effectively manage these costs and ensure the successful implementation of GEN/AI.
Q: How can organizations address the challenge of finding and retaining GEN/AI expertise?
Ian: Finding and retaining talent can be tough. In the competitive landscape of GEN/AI, sourcing and retaining talent poses a significant challenge for organizations. To overcome this hurdle, businesses can adopt several strategies.
First, investing in training programs and upskilling existing staff can help nurture in-house talent. Collaborating with educational institutions and universities can be a valuable source of fresh expertise.
Additionally, offering competitive compensation packages and benefits is essential for attracting and retaining skilled professionals in the rapidly evolving field of GEN/AI. Place a premium on those who have an innate ability to converse, they can make the best ‘prompt engineers.’
Q: Are there specific regulatory hurdles that organizations should be prepared to navigate when using GEN/AI?
Ian: Indeed, organizations must be ready to navigate a variety of regulatory hurdles when implementing GEN/AI, and these can vary depending on the industry and application. They are also highly likely to adapt to societal and regulatory needs at a pace.
To address these complex requirements effectively, it is crucial to have legal and compliance experts on board who can provide guidance and ensure that your GEN/AI initiatives align with the necessary regulations and standards.
Q: Can user resistance to GEN/AI systems affect organizations, and how can this be managed?
Ian: User resistance to GEN/AI systems can undoubtedly affect organizations, but with the right strategies and a focus on user-centric approaches, this resistance can be managed effectively. Embracing AI in the workforce is not just about technology; it is also about people and their comfort with change.
Some of the steps can include a companion education and training plan, user-centric design that involves the workforce, organizational change management, transparency in the objectives and expected outcomes, and incentives to reward employees to engage and contribute.
Q: Where should organizations begin when considering the implementation of GEN/AI systems?
Ian: Organizations embarking on the journey of implementing GEN/AI systems should adopt a gradual and iterative approach, leveraging what is commonly referred to as an “intelligent automation strategy.” This entails beginning with small, manageable steps while keeping the process simple and allowing for continuous improvement.
Start by clearly defining your business goals and identifying how GEN/AI systems can contribute to achieving them. Ensure your data is in decent shape, as data quality is a linchpin of AI success.
Select the right tools that align with your objectives and budget. Launch pilot projects to test the waters and gather user feedback, using these insights to fine-tune your approach. Prioritize data security and privacy, addressing these concerns from the outset. Invest in user training and change management to facilitate a smooth transition.
Finally, remember that the process does not end with implementation; it is an ongoing cycle of assessment and refinement to harness the full potential of intelligent automation and its benefits.
Generative AI is not just a technology; it is a strategic advantage. It is no longer a distant vision but a present reality, and the question is not whether to adopt it but when. As we peer into the horizon, the future of Generative AI is brimming with potential. Its continual evolution will unlock fresh opportunities for businesses to innovate, automate, and thrive.
Here at Advance Solutions, we fully appreciate the transformative impact of Generative AI, especially when synergized with the ServiceNow platform. We urge you to embrace the boundless possibilities that Gen AI presents and invite our team of ServiceNow Experts to lead you on this transformative journey.