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As advisors to the industry, we consistently field questions about GenAI from CEOs, strategy teams, and investors. This article centres on how GenAI is impacting the data and business information sector, an industry deeply rooted in content creation, and therefore susceptible to disruption.
Contrary to “traditional” AI, we see GenAI as a tool that reduces the hurdles to AI adoption and alters the trajectory of AI adoption. Essentially, it acts as an accelerator. Why? Primarily because you don’t necessarily require data science capabilities or even have technical expertise to derive benefits from GenAI—although they can certainly enhance its value.
As GenAI adoption accelerates, it has the potential to:
- Disrupt markets and redefine success for businesses
- Enhance productivity across various sectors
- Drive growth and create opportunities for generating new revenue streams
Throughout last year, GenAI garnered numerous announcements, and we anticipate an even greater surge in 2024. The elements are aligning for a more significant impact, including:
- Expanded tool availability
- Ever more powerful models
- Improved usability for non-technical users
- A maturing ecosystem of channel partners
Considerable research has been dedicated to exploring the industries and functions that may be susceptible to risks. While this is directionally helpful, we contend that firms within the same industry will experience varied outcomes. Those outcomes hinge on the strength and resilience of competitive moats (e.g., trusted brand, proprietary data, integration capabilities with customer and third-party data, distribution channels, and customer relationships) as well as each company’s adaptability. Given the rapid pace of technological advancement, businesses that can swiftly adapt will gain a competitive edge.
We expect that this disruption will generate opportunities and advantage for the data and business information industry. It’s no secret that the business information industry has undergone significant disruption in recent years. Over the past few decades, it has endured multiple waves of change, typically linked to digitisation in all its forms. Additionally, the industry has progressed by advancing along the value curve, transitioning from news to data, analytics, and workflow solutions.
We see the difference with GenAI as it being a strong accelerator of the pace of change. Speaking to and working with management teams across the industry, we notice a shared collective belief that this technology must be understood and harnessed as soon as possible, or risk being made redundant. It appears there are no Luddites.
How Leading Business Information Providers are Leveraging AI for Transformation and Growth
The largest data and business information providers recognise the risks of immobility. They also understood the scale of opportunity early on and have been moving to utilise GenAI as a catalyst for (partial) transformation and for growth.
The stand-out transaction is Thomson Reuters $650m acquisition of Casetext in 2023, Casetext being an AI-powered assistant for lawyers. One of Casetext's key products is CoCounsel, an AI legal assistant powered by GPT-4 that delivers document review, legal research memos, deposition preparation, and contract analysis. In fact, Casetext was granted early access to OpenAI's GPT-4 large language model to hone its product. CoCounsel is being incorporated into TR flagship products, including Westlaw and Practical Law.
Other firms are developing add-ons and/or integrations with GenAI providers, which is a widely employed strategy, as well as developing AI-based point solutions. Notable examples of recent moves include: Wolter Kluwer’s integration of Della and UpToDate, LexisNexis's Lexis+AI and Scopus AI initiatives, or Bloomberg’s launch of Bloomberg GPT.
These are examples of early movers. As provider, deployer, and customer maturity evolves we expect to see GenAI developments in the industry to snowball.
Data and business information providers will explore numerous avenues to capitalise on GenAI advancements. However, several key principles must be emphasised:
- Incorporate GenAI strategically and thoughtfully. There must be a method to the madness.
- Begin small and learn. Don’t go for big bang.
- Maintain customer trust throughout. This is essential.
Navigating GenAI Success: Insights and Best Practices
Insights into effective GenAI development are crucial, especially in light of recent high-profile failures—notably in consumer brands' rushed launches of inadequately tested chatbots. It's vital to emphasise the indispensable role of human involvement and oversight throughout GenAI product development. Let's delve into additional success factors for launching GenAI applications.
A fundamental principle is that the best ideas often originate from those closest to the business. Success hinges on granting GenAI access and empowerment to business teams—not just IT or senior management—thus fostering a bottom-up approach to initiating GenAI projects. Insights from client experiences have yielded the following tips:
- Cultivate innovation through diverse, cross-functional teams (e.g., business, marketing, IT)
- Start with grassroots initiatives like hackathons or prototypes.
- Maintain a balance between automation and human oversight. Recognise the balance between GenAI capability and human safety. Determine your product's position on this spectrum.
- As tasks grow more complex, risks rise, emphasising the importance of GenAI supporting human efforts. Yet, as output value increases, consider taking calculated risks to enhance value creation.
- Rely on technologists to implement AI safely. Major enterprises establish a "centre of enablement" for GenAI initiatives, ensuring consistent guidelines and preventing rogue actions.
- Unlike entrusting the entire project to IT, innovation should occur at the periphery with IT overseeing safety measures.
- Acknowledge the non-linear nature of progress in AI development. Moving from prototype to production can be swift (weeks) yet transitioning to full-scale implementation takes longer (months) due to safety considerations.
- Despite setbacks, remember challenges are surmountable with innovative thinking.
Looking Ahead to Technological Developments
We observe a continual drive to reduce the cost of intelligence per dollar, largely fueled by the open-source market, encouraging competition and innovation while lowering barriers for developers and businesses. Additionally, AI systems are expanding in modalities, encompassing diverse input sources and output formats. Innovations like Sora, facilitating text-to-video conversion, highlight this trend. Furthermore, there's a shared ambition within the AI community to attain Artificial General Intelligence (AGI), striving for systems mirroring human cognition. Sora, for instance, is gaining prominence with its ability to transform text into fully realised videos.
Alongside these advancements, the AI solutions market is diversifying. Providers range from proprietary firms to open-source GenAI developers, many adopting hybrid approaches. These leverage open-source solutions for routine tasks and proprietary technologies for more complex ones. Furthermore, the rise of one-stop shops, especially among proprietary firms, reflects the demand for integrated multi-modal AI solutions. Several factors merit consideration:
- AI's role in predictive tasks is crucial. While GenAI shows promise in task orchestration, traditional AI excels in specific mathematical domains, while LLMs excel in "people tasks."
- Managing data and maintaining model relevancy are critical challenges. Retraining models on the latest data and handling situations where it's unavailable are key concerns. With data evolving rapidly, outdated LLMs pose accuracy and relevancy challenges. Techniques like retrieval-augmented generation (RAG) are employed to access current data sources, enhancing model understanding.
- Lastly, predicting ROI for GenAI projects remains challenging. A bottom-up approach for upfront ROI estimation minimises research costs, providing insights into AI's transformative potential.
Divergent Approaches: Regulatory Frameworks in the EU and U.K. for AI Adoption
The EU and U.K. have different regulatory approaches regarding AI, aiming to foster competition and manage risks. The EU favours comprehensive laws for clarity, mainly targeting high-risk AI applications and leaving out GenAI. Risk management for GenAI is currently addressed through testing and standardisation, with detailed regulation postponed. In contrast, the U.K. and SE Asia entrust GenAI oversight to industry bodies. The future market landscape, whether regulated or not, depends on subsequent standardisation efforts by regulatory bodies.
Regulatory Timeline
Despite drama leading up to the last moment, EU negotiations are done. The final EU Parliament vote was a bureaucratic step, but all previous steps had substantial disagreement between countries (liberty vs. security, left vs. right). Now, the EU is waiting for official publication for the clock to start ticking on implementation.
Within six months, AI applications conflicting with EU values, such as privacy, will be prohibited. In 12 months, a detailed framework for GenAI implementation is expected. High-risk use cases, like critical infrastructure and education, will see increased implementation within 24 months.
Regarding standards, the EU anticipates their emergence around 12 months from now. Meanwhile, the U.K. Safety Institute operates on a monthly timeline, mirroring developments in the U.S. and Singaporean safety institutes.
Implementation
EU law covers both AI providers and deployers if their products or tools impact the EU, raising questions for the U.K. The EU aims to become a standard-setter like GDPR, but success is uncertain and hinges on implementation details. GenAI regulation is competitive, with weak political coordination and a focus on domestic rather than international standards. The Retrieval-Augmented Generation (RAG) model is utilised in business information, sparking copyright debates over data protection.
Creative industries' pressure has tempered calls for GenAI deregulation in France and Germany. Open source isn't a liability shield due to GenAI's complexity, impacting liability. The regulatory journey is early, with differing approaches yielding similar outcomes. Vertical-specific regulation is limited, with industry regulators awaiting opportunities for tailored laws. Enforcement of AI regulation, particularly for GenAI, is a concern, but shouldn't hinder risk management framework development.
About Stax
Throughout our 30+ years of consulting experience, Stax has worked alongside numerous Software & Technology companies, including more recent GenAI companies. Our team of dedicated experts remain aware of the latest trends and developments, providing our clients with actionable insights and successful strategies to help bolster value creation and successful exits. To learn more about Stax and our expertise, please visit www.stax.com or click here to contact us directly.
Baird
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