In the first part of this three-part series, generative AI and how it works were described.

IBM Consulting sees tangible business value in augmenting existing enterprise AI deployments with generative AI to improve performance and accelerate time to value. There are four categories of dramatically enhanced capabilities these models deliver: 

  • Summarization as seen in examples like call center interactions, documents such as financial reports, analyst articles, emails, news and media trends. 
  • Semantic search as seen in examples like reviews, knowledge base and product descriptions. 
  • Content creation as seen in examples like technical documentation, user stories, test cases, data, generating images, personalized UI, personas and marketing copy.
  • Code creation as seen in examples like code co-pilot, pipelines, docker files, terraform scripts, converting user stories to Gherkin format, diagrams as code, architectural artifacts, Threat models and code for applications.

With these improvements, it’s easy to see how every industry can re-imagine their core processes with generative AI. See examples here.  

Leading use cases do more than simply cut costs. They contribute to employee satisfaction, customer trust and business growth. These aren’t forward-looking possibilities because companies are using generative AI today to realize rapid business value including things like improving accuracy and near real-time insights into customer complaints to reduce time-to-insight discovery, reduction in time for internal audits to maintain regulatory compliance and efficiency gains for testing and classification.

While these early cases and the results they’ve delivered are exciting, the work involved in building generative AI solutions must be developed carefully and with critical attention paid to the potential risks involved including:

  • Bias: As with any AI model, the training data has an impact on the results the model produces. Foundation Models are trained on large portions of data crawled from the internet. Consequently, the biases that inherently exist in internet data are picked up by the trained models and can show up in the results the models produce. While there are ways to mitigate this effect, enterprises need to have governance mechanisms in place to understand and address this risk. 
  • Opacity: Foundation models are also not fully auditable or transparent because of the “self-supervised” nature of the algorithm’s training. 
  • Hallucination: LLMs can produce “hallucinations,” results that satisfy a prompt syntactically but are factually incorrect. Again, enterprises need to have strong governance mechanisms in place to mitigate this risk.  
  • Intellectual property: There are unanswered questions concerning the legal implications and who may own the rights to content generated by models that are trained on potentially copywritten material.  
  • Security: These models are susceptible to data and security risk including prompt injection attacks. 

When engaging in generative AI projects, business leaders must ensure that they put in place strong AI Ethics & Governance mechanisms to mitigate against the risks involved. Leveraging the IBM Garage methodology, IBM can help business leaders evaluate each generative AI initiative on how risky and how precise the output needs to be. In the first wave, clients can prioritize internal employee-facing use cases where the output is reviewed by humans and don’t require high degree of precision. 

Generative AI and LLMs introduce new hazards into the field of AI, and we do not claim to have all the answers to the questions that these new solutions introduce. IBM Consulting is committed to applying measured introspection during engagements with enterprises, governments and society at large and to ensuring a diverse representation of perspectives as we find answers to those questions.  

Learn more about this three-part series by reading the first or third installment, and reach out to an expert for start a conversation about your needs.

Register for our webcast: What does ChatGPT mean for business? – How to drive disruptive value with Generative AI

Was this article helpful?

More from Business transformation

Business process management (BPM) examples

7 min read - Business Process Management (BPM) is a systematic approach to managing and streamlining business processes. BPM is intended to help improve the efficiency of existing processes, with the goal of increasing productivity and overall business performance. BPM is often confused with other seemingly similar initiatives. For example, BPM is smaller in scale than business process reengineering (BPR), which radically overhauls or replaces processes. Conversely, it has a larger scope than task management, which deals with individual tasks, and project management, which…

Using generative AI to accelerate product innovation

3 min read - Generative artificial intelligence (GenAI) can be a powerful tool for driving product innovation, if used in the right ways. We’ll discuss select high-impact product use cases that demonstrate the potential of AI to revolutionize the way we develop, market and deliver products to customers. Stacking strong data management, predictive analytics and GenAI is foundational to taking your product organization to the next level.   1. Addressing customer inquiries with an AI-driven chatbot  ChatGPT distinguished itself as the first publicly accessible GenAI-powered…

Integrating AI into Asset Performance Management: It’s all about the data

3 min read - Imagine a future where artificial intelligence (AI) seamlessly collaborates with existing supply chain solutions, redefining how organizations manage their assets. If you’re currently using traditional AI, advanced analytics, and intelligent automation, aren’t you already getting deep insights into asset performance? Undoubtedly. But what if you could optimize even further? That’s the transformative promise of generative AI, which is beginning to revolutionize business operations in game-changing ways. It may be the solution that finally breaks through dysfunctional silos of business units,…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters