The AI Landscape
Artificial Intelligence (AI) is rapidly evolving, with its ecosystem developing into three distinct layers: foundational models, AI infrastructure, and AI applications.
Foundational models are the core of the AI stack, providing the essential building blocks for AI systems. Leaders in this space include Anthropic, Cohere, and OpenAI, companies that require significant capital to build and train these models.
Index Ventures: The Rise of Vertical AI
AI infrastructure encompasses various tools and technologies that support the development and deployment of AI applications. This layer includes data enhancement, fine-tuning, databases, and model training tools. For instance, vector databases like Weaviate are becoming increasingly popular, while companies like Scale specialise in data generation, labelling, and training. Hugging Face has established itself as a leader in model discovery and inference, and Weights & Biases is a recognized name in MLOps. LangChain, an open-source framework, simplifies the creation of new applications using large language models (LLMs).
These foundational models and infrastructure layers have spurred a surge in AI business applications, which can be employed across various industries to perform a wide range of tasks. However, the effectiveness and applicability of these AI-powered tools vary significantly between horizontal and vertical use cases.
The Challenge with Horizontal AI in the Enterprise
Horizontal AI tools like ChatGPT, Bard and Jasper are designed for broad use cases, such as writing emails and summarising meeting notes, and can be applied across different industries. While these tools are powerful, they often fall short in addressing the specific needs of enterprise users.
In an enterprise setting, employees require AI solutions that understand the nuances of their industry and can support their unique business processes. Horizontal AI lacks the domain-specific knowledge and data to provide meaningful insights and automation for industry-specific tasks. This gap highlights the need for Vertical AI solutions, which are tailored to the specific workflows and data sets of particular industries.
“With the AI Platform Shift upon us, we believe that the next logical iteration of vertical SaaS will be Vertical AI – vertically-focused AI platforms, bundled alongside workflow SaaS, built on top of models which have been uniquely trained on industry-specific datasets.”
— Paris Heymann, Index Ventures
Why Enterprises Need Vertical AI
Vertical AI is designed to address the specific challenges and needs of particular industries. By leveraging industry-specific data sets and supporting specialised business processes, Vertical AI can provide more relevant and actionable insights than horizontal AI solutions.
“Much of the AI hype of 2023-24 has been centered around horizontal capabilities of foundational models. The real opportunity of AI lies not in creating mediocre marketing copy, but in the ability to reconfigure value creation across vertical value chains.”
— Sangeet Paul Choudary, Platformation Labs
For enterprises, the value of Vertical AI lies in its ability to enhance productivity and decision-making within specific contexts. For example, in the Consumer Products industry, Vertical AI can optimise sales visits by turning handwritten notes into digital requests, increase average basket size by recommending complete customer orders, and guarantee trade promotion compliance through AI Assistant photo checks.
The Importance of Understanding Personas in Vertical AI
To effectively leverage Vertical AI, it is essential to deeply understand the personas within the enterprise, their daily workflows, and the specific business processes they carry out. Each persona represents a different role within the organisation, with unique responsibilities and challenges. By understanding these personas, AI solutions can be tailored to meet their specific needs and add significant value to their operations.
Mapping out the business processes that these personas engage in is equally important. These processes often involve multiple steps and interactions with other roles and systems. Vertical AI solutions designed with a comprehensive understanding of these workflows can provide seamless integration, automation, and efficiency improvements, directly addressing the pain points of the personas.
At Aforza, we take a persona-driven approach to everything we do, which has led us to create the industry’s first Business Process Library for Consumer Products. This library contains best practices, workflows, and AI-driven solutions specifically tailored to the Consumer Products industry.
You can see an example of the personas we focus on below and explore their day-in-the-life challenges and business processes:
Field Sales
“How does Vertical AI help me to drive revenue & execute visits efficiently?”
Telesales Agent
“How does Vertical AI help me to maximize the value of every customer engagement?”
Merchandiser
How does Vertical AI help me to optimize product placement, availability & compliance?”
By focusing on personas and business processes, we ensure that our AI solutions are relevant, effective, and capable of driving real, measurable improvements in our customers’ operations.
Vertical AI Needs an Ecosystem
For Vertical AI to truly drive business impact, it must be part of a comprehensive ecosystem that enables seamless integration and actionability.
Aforza’s Studio Marketplace is designed to fill this crucial role, providing a platform where insights uncovered by AI can be transformed into actionable strategies. This marketplace facilitates the development and deployment of industry-specific applications, ensuring that AI-driven insights are not only understood but also acted upon efficiently and effectively.
“Vertical AI’s market capitalization will be at least 10x the size of legacy Vertical SaaS as Vertical AI takes on the services economy and unleashes new business models.”
— Bessemer Venture Partners, State of the Cloud 2024
Studio Marketplace
The Aforza Studio Marketplace offers a robust ecosystem where businesses can access a wide range of tools and applications tailored to their unique needs.
By connecting AI insights with actionable workflows and applications, enterprises can optimise their operations, streamline decision-making processes, and ultimately drive significant business impact.
This integrated approach ensures that AI’s potential is fully realised, translating data-driven insights into practical, real-world outcomes that enhance productivity and competitive advantage.
Aforza Vertical AI Use Cases for Consumer Products
Here are 3 examples of Vertical AI Use Cases from Aforza for the Consumer Products industry for Field Sales Reps, Telesales Agents and Merchandisers. This is just a subset of the use cases we have available and you can explore more in the Aforza Demo Center by filtering on AI.
1. Turn Handwritten Customer Notes into Digital Orders for Field Sales Reps
Many field sales reps still rely on handwritten notes taken during customer visits to track orders and customer preferences.
This method is not only time-consuming but also prone to errors and misinterpretations. Handwritten notes often lead to delays in order processing, lost information, and inefficiencies in managing customer relationships.
Aforza’s Vertical AI solution transforms this process by using advanced AI algorithms to convert handwritten notes into digital orders instantly. The AI-powered process can accurately interpret various handwriting styles and seamlessly integrate the extracted data into the Aforza order management system.
This not only speeds up the order processing time but also reduces errors, ensuring that customer orders are accurate and timely. Sales reps can now focus more on engaging with customers and less on administrative tasks, leading to improved efficiency and customer satisfaction.
2. Increase Basket Size by Recommending Complete Customer Orders for Telesales Agents
Telesales agents often struggle to increase basket size due to a lack of personalized insights and recommendations. Relying on basic customer data and limited historical purchase information, agents find it challenging to suggest additional products that would interest the customer.
This results in missed opportunities for upselling and cross-selling, leading to lower average order values and suboptimal sales performance.
Aforza’s Vertical AI solution empowers telesales agents with intelligent recommendations based on a comprehensive analysis of customer behavior, previous purchases, and current market trends.
By leveraging AI, Aforza provides agents with tailored suggestions for complete customer orders, helping them to effectively upsell and cross-sell products that complement the customer’s existing preferences. This personalized approach not only increases basket size but also enhances customer experience, as customers receive relevant and valuable product suggestions.
3. Instantly Understand Stock Facings & Availability with the Aforza AI Assistant for Merchandiser
Merchandisers have traditionally had to conduct manual checks to assess stock facings and product availability on store shelves. This involved physically counting items, noting down discrepancies, and manually updating inventory records.
This labor-intensive process was not only inefficient but also prone to human error, leading to inaccurate stock levels and potential out-of-stock situations that could negatively impact sales and customer satisfaction.
Aforza’s AI Assistant for Merchandisers transforms this process by providing real-time insights into stock facings and availability. Using advanced image recognition and AI algorithms, the Aforza AI Assistant can analyze photos of store shelves taken by merchandisers to instantly identify product quantities and placement accuracy.
This automated system ensures accurate and up-to-date inventory records, enabling faster restocking and reducing the risk of out-of-stock scenarios. Merchandisers can now spend less time on manual checks and more time on strategic activities that enhance store performance and customer satisfaction.
In conclusion, Aforza’s Vertical AI solutions address the inefficiencies and challenges faced by the consumer products industry in traditional methods. By leveraging AI to automate and enhance various aspects of sales, telesales, and merchandising, Aforza not only improves operational efficiency but also drives significant business value and customer satisfaction.
How You Get Started with Vertical AI
To get started with Vertical AI, enterprises need to focus on several key areas:
1. Identify the Right Persona Use Cases
Start by identifying the specific business processes and challenges for your personas that could benefit from AI solutions. Look for areas where industry-specific insights and automation can provide significant value.
2. Leverage Industry-specific Data
Collect and curate high-quality data sets that are relevant to your industry. The effectiveness of Vertical AI depends on the quality and specificity of the data it is trained on.
3. Partner with Vertical Experts
Collaborate with Vertical AI solution providers, like Aforza, who have expertise in your industry. These partners can help you design and implement AI solutions that are tailored to your unique needs.
4. Invest in Training & Change Management
Ensure that your team is equipped to work with AI tools and understand how to integrate them into existing workflows. Training and change management are crucial for successful AI adoption.
5. Measure & Iterate
Continuously monitor the performance of your AI solutions and make adjustments as needed. AI is an evolving technology, and ongoing iteration will help you maximise its value.
By focusing on these areas, enterprises can effectively leverage Vertical AI to enhance their operations, drive innovation, and gain a competitive edge in their industry.
Blog References
- Index Ventures: The Rise of Vertical AI
- Substack: How to win at Vertical AI
- Bessemer Venture Partners: State of the Cloud 2024
- Bessemer Venture Partners: Vertical AI shows potential to dwarf legacy SaaS
- Referenced Foundation Model: OpenAI
- Referenced Foundation Model: Cohere
- Referenced Foundation Model: Anthropic
- Referenced AI Infrastructure: Hugging Faces
- Referenced AI Infrastructure: LangChain
- Referenced AI Infrastructure: Pinecone
- Referenced AI Infrastructure: Scale
- Referenced AI Infrastructure: Weaviate
- Referenced AI Infrastructure: Weights & Biases