What makes an industry well-suited for vertical AI?

ResearchApr 10, 2024
What makes an industry well-suited for vertical AI?

By Marissa Moore, CFA and Taku Murahwi

While the interplay of factors that make a specific vertical more ready than others for AI-powered innovation is complex, we identified a few factors that have an outsized impact:

The volume and complexity of unstructured, industry-specific data produced.
A third-party software vendor’s ability to access said data.
A third-party software vendor’s ability to amass its own proprietary data and close off access to competitors.
Uniqueness/industry-specificity of the workflows and jobs to be done.
Degree of regulatory oversight and burden of compliance.
Degree of high-stakes decision-making.
Importance of personalization.
Prevalence of simulation and synthesis workflows.
Prevalence of exploratory analysis workflows.
Importance of creative content generation.

Volume and complexity of unstructured, industry-specific data produced

Industries with greater volumes and complexity of rich, unstructured, industry-specific data are better suited for vertical AI for several reasons:

The abundance of data provides vertical AI with a rich source of information to train its algorithms, enabling more accurate and nuanced insights tailored to the unique challenges and opportunities within the industry.
The complexity of the data reflects the intricacies of real-world scenarios within the industry, presenting challenges that generic AI models may struggle to navigate effectively.

Vertical AI, with its industry-specific focus and specialized algorithms, is better equipped to extract meaningful patterns and insights from such complex datasets, leading to more valuable and actionable outcomes for businesses operating within these industries.

Moreover, vertical AI’s ability to understand and interpret industry-specific nuances allows for the development of highly tailored solutions that address specific pain points and drive innovation and efficiency within the sector.

A third-party software vendor’s ability to access said data

An industry’s production of complex, unstructured, industry-specific data holds immense potential for vertical AI applications. However, this potential will remain largely untapped unless the data is easily accessible to third-party software developers (or in-house developers, to be fair, but we expect most innovation to occur at the periphery). Data accessibility allows developers to harness the data’s richness, enabling them to train specialized AI models that can unlock valuable insights and solutions tailored to the industry’s unique challenges and opportunities.

Access to data also fosters the ongoing enhancement of AI models, facilitating adaptation to emerging trends and maintaining a competitive edge. In addition to those benefits, data accessibility just generally cultivates an environment of collaboration and innovation within industries, opening avenues for partnerships and knowledge exchange.

A third-party software vendor’s ability to amass its own proprietary data and then close off access to competitors

While vertical AI can be constructed using open-source and publicly available data, its reliance on non-proprietary sources may diminish its appeal and ability to differentiate in the market. From a venture investor’s perspective, a vertical AI developer’s ability to amass proprietary data and restrict access to competitors is crucial for building a venture-scale, generational business. This proprietary data serves as a key competitive advantage, enabling the developer to create highly differentiated AI solutions tailored to specific industry needs.

Securing exclusive access to valuable datasets allows a developer to better build their ‘moat,’ making it more difficult for competitors to replicate or surpass its AI capabilities without access to comparable datasets. This exclusivity not only enhances the developer’s ability to attract customers, but also increases its attractiveness to potential acquirers or investors, positioning it for long-term growth and success as other elements of the AI landscape continue to shift (aka model selection, build vs. buy, etc.).

Controlling access to proprietary data also ensures the integrity and security of sensitive industry information, mitigating risks associated with data breaches or unauthorized usage. Ultimately, this strategy not only safeguards the developer’s market position, but also fosters trust and confidence in customers and among stakeholders by demonstrating a commitment to privacy and data protection.

Uniqueness/industry-specificity of the workflows and jobs to be done

Industries that have highly specific workflows with requirements unique to their domain, vs. more generic and easily transferable jobs-to-be-done are best suited for the adoption of vertical AI. Understanding those intricacies is paramount for effective vertical AI development. Just as a custom-made suit fits perfectly, industry-specific AI solutions are tailored to meet the unique needs of their target market.

Industry-specific workflows also provide the necessary context for AI algorithms to interpret data accurately and make informed decisions. This deep understanding of the domain also enables developers to integrate their solutions seamlessly into existing workflows and processes, ensuring practicality and ease of adoption. By aligning with industry-specific workflows and jobs to be done, vertical AI developers can not only drive broader adoption of AI technologies within the industry, but also gain a competitive advantage by offering solutions that are uniquely tailored to the needs of their customers.

Degree of regulatory oversight and burden of compliance

In addition to integrating industry-specific rules and standards, vertical AI developers can leverage their expertise to design solutions that not only ensure compliance but also streamline processes and enhance operational efficiency within highly regulated sectors like healthcare or finance. By understanding the intricate nuances of regulatory frameworks, these developers can embed compliance requirements directly into their AI algorithms, facilitating seamless adherence to regulations while minimizing the burden on organizations.

Furthermore, vertical AI’s ability to adapt to evolving regulatory landscapes ensures that organizations remain compliant in the face of changing rules and standards. This adaptability, coupled with the provision of detailed audit trails and explainable decision-making processes, instills trust and confidence among stakeholders, reinforcing the integrity of compliance efforts. By offering tailored solutions that address both regulatory requirements and operational needs, vertical AI can not only mitigate risks, but also drive sustainable growth and innovation within regulated industries.

Degree of high-stakes decision-making
Industries with higher-stakes decision-making are better suited for vertical AI due to the critical importance of accuracy, reliability, and precision in such contexts. Whether it’s healthcare, finance, or cybersecurity, vertical AI excels at analyzing vast amounts of data, identifying patterns, and making predictions with a level of accuracy and speed that surpasses human capabilities.

Vertical AI can also provide valuable insights and recommendations to support decision-makers in navigating complex scenarios and mitigating risks effectively (including incorporating regulatory burden, as described above). Leveraging vertical AI, industries with higher-stakes decision-making can enhance their decision-making processes, improve outcomes, and ultimately gain a competitive advantage in their respective markets.

Importance of personalization
Industries that prioritize personalization — whether for industry participants or their end customers — are inherently better suited for vertical AI due to the technology’s ability to tailor solutions to individual needs and preferences. Vertical AI excels at leveraging vast amounts of data to understand nuanced behaviors, preferences, and patterns, enabling personalized experiences at scale.

In industries such as retail and healthcare, where customer-centricity is paramount, vertical AI can analyze customer data to offer personalized recommendations, enhance user experiences, and drive customer loyalty. Similarly, in industries where personalization is key for industry participants, such as finance, vertical AI can provide customized insights and solutions tailored to individual requirements. By harnessing the power of vertical AI, industries can also unlock new levels of personalization, going beyond merely providing benefits in terms of operating efficiency vs. manual, human-powered personalization efforts.

Prevalence of simulation and synthesis workflows
Simulation workflows involve creating models to predict the behavior of systems under different conditions, commonly used in fields like engineering and biology for testing and analysis. Synthesis workflows, on the other hand, entail combining elements or data to generate new structures or systems, frequently employed in areas such as chemistry and computer science for designing compounds or algorithms. Vertical AI excels at synthesizing diverse datasets and generating realistic simulations, enabling industries including manufacturing, engineering, and pharmaceuticals to optimize processes, test hypotheses, and make informed decisions. In fields where experimentation and testing are costly or impractical, vertical AI offers a cost-effective and efficient alternative by simulating various scenarios and predicting outcomes.

Vertical AI can also identify patterns and trends within synthesized data, providing valuable insights that drive innovation and productivity. More on this in the next section.

Prevalence of exploratory analysis workflows
Exploratory analysis workflows involve the iterative process of visually and statistically analyzing data to uncover patterns, trends, and relationships. By utilizing techniques such as data visualization and descriptive statistics, exploratory analysis aims to gain insights into the characteristics and structure of the dataset, often serving as a precursor to more in-depth analytical tasks such as hypothesis testing or predictive modeling.

Industries characterized by a high prevalence of exploratory analysis workflows are inherently well-suited for vertical AI integration for a couple of reasons:

Such industries demonstrate a commitment to thorough data exploration, enabling them to cultivate a deep understanding of their unique datasets. This foundation of insight ensures that AI algorithms are trained on comprehensive and representative data, enhancing their accuracy and effectiveness in addressing industry-specific challenges.

The rigorous analytical practices inherent to these industries facilitate the identification of meaningful patterns, correlations, and anomalies within data sets, providing a fertile ground for AI-driven insights and decision-making.

Moreover, the systematic approach to data exploration enables organizations to refine their AI solutions to align closely with the nuanced requirements and objectives of their vertical, thereby maximizing the relevance and impact of AI applications. Overall, the prevalence of exploratory analysis workflows directly influences the quality, adaptability, and practicality of vertical AI implementations.

Importance of creative content generation
Industries placing a significant emphasis on creative content generation are ideally suited for vertical AI due to its ability to augment and enhance human creativity. Vertical AI excels at generating diverse and high-quality content across various mediums, including text, images, audio, and video. In sectors such as advertising, media, and entertainment, where creativity is paramount, vertical AI can assist in ideation, content creation, and even personalized content delivery: Vertical AI can generate innovative ideas, optimize creative campaigns, and tailor content to specific audiences. Moreover, vertical AI can automate repetitive tasks, allowing creative professionals to focus on higher-level strategic activities. Leveraging vertical AI, industries with a strong emphasis on creative content generation can improve productivity, and even deliver new and compelling experiences to their audiences.

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