December 18, 2023
3 Examples of Generative AI Product Launches in Information Services
The debut of ChatGPT in November 2022 sparked an innovation race. As an enthralled public imagined the extent of ChatGPT’s impact, leading companies had to weigh whether to take a “wait and see” approach or jump in the waters immediately. Those with prior experience in machine learning, AI-ready data, AI platforms and mature capabilities were well-positioned to be first-to-market with meaningful AI and LLM use cases.
What’s on the horizon, so to speak, is the power of generative AI to enact game-changing, transformational change, potentially upending current business models and providing new ways of driving user and business value. For some examples of how this is already happening within the information services sector, take a look at these three examples.
Thomson Reuters acquisition of Casetext
In August 2023, Thomson Reuters acquired Casetext, an AI technology company focusing on tools to help legal professionals. One of the first companies to deliver a GPT powered solution to market, Casetext built CoCounsel, an AI legal assistant that delivers document review, legal research memos, deposition preparation, and contract analysis within minutes.
CoCounsel handles legal tasks that, until recently, have proven time consuming and monotonous. By leveraging generative AI to automate and streamline these tasks, CoCounsel is positioned to transform the nature of legal work from being primarily clerical to a focus on human interactions and engagement.
Casetext, smartly, invested time in training and validating CoCounsel and putting it into the hands of potential users prior to launch. Casetext’s Trust Team invested nearly 4,000 hours training and fine-tuning CoCounsel on over 30,000 legal questions before releasing the product.
This recent acquisition aligns with Thomson Reuters’ broader “build, partner, and buy” strategy and their efforts to redefine the future of professional work through generative AI. In addition to the Casetext acquisition, Thomson Reuters has committed to invest $100 million annually to integrate AI into its flagship content and technology solutions, and is working with Microsoft to build a new plugin for Microsoft 365 Copilot.
What you can learn from this development:
- Partnerships, collaborations, and acquisitions are a key part of enterprise AI development strategies
- Aggressive and proactive AI strategies involve transformational change of industries, sectors, and professions—not incremental improvements on the margins
- AI is replacing monotonous and tedious tasks, but not those that require high skill and human engagement
- Building a proof of concept is fairly straightforward (and relatively easy), however, delivering an “AI-powered” product or feature into production requires mature capabilities and AI-ready data.
Moody’s partnership with Google Cloud & Microsoft
Financial services firm Moody’s is aggressively pursuing AI-based partnerships with several leading players in the space, including Google Cloud and Microsoft.
Their recent partnership with Google Cloud has three primary goals:
- Building LLMs to accelerate financial analysis. Moody’s and Google Cloud are joining forces to build a fine-tuned LLM purpose-built for financial professionals, enabling customers to perform faster, deeper analyses of reports, disclosures, and other materials. Deeper analysis provides richer insights and opportunities to cross-sell data, adding revenue streams while helping customers achieve their goals.
- Enabling Moody’s data access in BigQuery. Moody’s data will support BigQuery, Google Cloud’s serverless data warehouse, enabling customers to combine Moody’s data with their own native data sets, using LLMs to deliver insights that accelerate time-to-value and increase financial professionals’ efficiency.
- Enhancing enterprise search for financial data. Moody’s will incorporate Google Cloud’s Vertex AI search to increase efficiencies: automating manual workflows, and more seamlessly summarizing multiples.
Moody’s has also partnered with Microsoft, combining proprietary financial data and analytical capabilities with Microsoft Azure OpenAI service to enhance insights into corporate intelligence and risk assessment.
Moody’s CoPilot, an AI-powered tool, is now deployed to Moody’s 14,000 global employees. Situated directly within Microsoft Teams, Moody’s Copilot enables safe and secure insights for employees, leveraging Moody’s proprietary data, analytics, and LLMs and combining them with Microsoft’s generative AI technology. Democratizing access to Generative AI tools enables companies to seek efficiencies while providing team members with current technologies.
Microsoft, on the other hand, is leveraging Moody’s broad range of solutions to build applications that include third-party reference data, counterparty risk assessment, and supply chain management.
What you can learn from this development:
- Information services companies with proprietary data sets and analytics skill sets can attract high-value strategic partners to open up doors to additional revenue
- The data-AI relationship cuts both ways—information services companies are looking for new opportunities to operationalize their data, while technology companies are looking for better data to enhance their AI-powered applications
- GenAI and LLMs are increasingly focused on industry-specific use cases—which means companies with niche data for their vertical have greater opportunity to grow
LexisNexis’s expanded AI ecosystem
LexisNexis, a leading global provider of legal information and analytics, has expanded its generative AI-enabled product ecosystem, further enhancing their efforts to support conversational search, intelligent legal drafting, summarization, and document analysis.
According to a recent survey of commercial preview customers, LexisNexis reported that their Lexis+ AI achieved the following productivity gains:
- 89% estimate they will save up to six hours per week on summarizing case law
- 78% estimate they will save up to four hours per week on legal drafting
- 77% estimate they will save up to six hours per week uploading and summarizing documents
- 74% estimate they will save up to seven hours per week on legal research
Like Casetext, this is another example of how AI is helping to automate more tedious or monotonous tasks, changing the nature of how those in the legal profession do their jobs.
What you can learn from this development:
- Generative AI has the potential to transform the nature of professional work, reducing the need for administrative support while freeing more time for value added activities.
- Information services companies can not only generate external, insights-based content from their data, but use that data to train AI models and automate more internal tasks
- To save time, insights must be delivered to the user automatically at the point of need
No less than the biggest players in the generative AI space are looking for high quality, vertical-specific data sets. As such, it’s important to consider how generative AI can help your company build strategic partnerships and open additional revenue streams.
A common thread among all three of these information services providers, however, is that they aren’t going it alone. Rather, they’re layering their unique value prop over their partners’ generative AI expertise.
But AI expertise alone won’t make a good product. You also need someone who can help you:
- Minimize time to value. Going from proof-of-concept to a releasable product requires expertise, especially considering the complexities of generative. You need a product development partner who can find the fastest path to revenue, then expand from there.
- Solve for need. AI is only valuable if it solves a user problem. Your product development partner can conduct user research to find out where to direct your AI efforts. Then help you train and validate your model, product, or feature.
- Excel at change. AI technology is ever-evolving. You can’t just build a single, static product. You need a product development partner who can help you adapt to and evolve with the market. Generative AI requires AI-ready data and platforms to fuel LLMs and machine learning.
To learn more about how 3Pillar Global can support you in these three areas, set up a time with our team here.