Generative AI is here:
How tools like ChatGPT could change your business
Generative AI and other foundation models are changing the AI game, taking assistive technology to a new level, reducing application development time, and bringing powerful capabilities to nontechnical users.
Business uses abound
These models are in the early days of scaling, but we’ve started seeing the first batch of applications across functions, including the following (exhibit):
— Marketing and sales—crafting personalized marketing, social media, and technical sales content (including text, images, and video); creating assistants aligned to specific businesses, such as retail
— Operations—generating task lists for efficient execution of a given activity
— IT/engineering—writing, documenting, and reviewing code
— Risk and legal—answering complex questions, pulling from vast amounts of legal documentation, and drafting and reviewing annual reports
— R&D—accelerating drug discovery through better understanding of diseases and discovery of chemical structures
There are many possible generative AI use cases across the business that could could create early impact.
Example use cases1 (not exhaustive)
Marketing and Sales
Write marketing and sales copy including text, images, and videos (eg, to create social media content or technical sales content)
Create product user guides of industry-dependent offerings (eg, medicines or consumer products)
Analyze customer feedback by summarizing and extracting important themes from online text and images
Improve sales force by, for example, flagging risks, recommending next interactions such as additional product offerings, or identifying optimal customer interaction that leads to growth and retention
Create or improve sales support chatbots to help potential clients understand, including technical product understanding, and choose products
Create or improve customer support chatbots to resolve questions about products, including generating relevant cross-sell leads
Identify production errors, anomalies, and defects from images to provide rationale for issues
Streamline customer service by automating processes and increasing agent productivity
Identify clauses of interest, such as penalties or value owed through leveraging comparative document analysis
Automatically generate or auto-complete data tables while providing contextual information
Generate synthetic data to improve training accuracy of machine learning models with limited unstructured input
Risk and legal
Draft and review legal documents, including contracts and patent applications
Summarize and highlight changes in large bodies of regulatory documents
Answer questions from large amounts of legal documents, including public and private company information
Assist in creating interview questions for candidate assessment (eg, targeted to function, company philosophy, and industry)
Provide self-serve HR functions (eg, automate first-line interactions such as employee onboarding or automate Q&A or strategic advice on employment conditions, law, regulations, etc)
Optimize communication of employees (eg, automate email responses and text translation or change tone or wording of text)
Create business presentations based on text prompts, including visualizations from text
Synthesize a summary (eg, from text, slide decks, or online video meetings)
Enable search and question answering on companies’ private knowledge data (eg, intranet and learning content)
Automated accounting by sorting and extracting documents using automated email openers, high-speed scanners, machine learning, and intelligent document recognition
1 Given that generative AI is in the early stages of maturity, organizations will want to consider use cases and their implications carefully and determine the appropriate level of human oversight.
Initial steps for executives
In companies considering generative AI, executives will want to quickly identify the parts of their business where the technology could have the most immediate impact and implement a mechanism to monitor it, given that it is expected to evolve quickly. A no-regrets move is to assemble a cross-functional team, including data science practitioners, legal experts, and functional business leaders, to think through basic questions, such as these:
— Where might the technology aid or disrupt our industry and/or our business’s value chain?
— What are our policies and posture? For example, are we watchfully waiting to see how the technology evolves, investing in pilots, or looking to build a new business? Should the posture vary across areas of the business?
— Given the limitations of the models, what are our criteria for selecting use cases to target?
— How do we pursue building an effective ecosystem of partners, communities, and platforms?
— What legal and community standards should these models adhere to so we can maintain trust with our stakeholders?
Meanwhile, it’s essential to encourage thoughtful innovation across the organization, standing up guardrails along with sandboxed environments for experimentation, many of which are readily available via the cloud, with more likely on the horizon.
The innovations that generative AI could ignite for businesses of all sizes and levels of technological proficiency are truly exciting. However, executives will want to remain acutely aware of the risks that exist at this early stage of the technology’s development.