The professionals – examples of B2B products done right

The professionals are a good place to start with grouping products. AI will gravitate first toward use cases that can be profitable and allow for research and optimization. Because B2B products are products that are made for and used by other businesses, their use case is oriented completely toward the business world. This impacts everything from how they’re marketed to how they’re bought, sold, used, and negotiated. So many B2B products speak to the business impact that a product can satisfy for a customer across multiple levels. It’s a great way to learn potentially helpful applications of AI.

Part of the challenge with the rising supply of AI companies is that they need data to train on. Specifically, one of the ethical challenges with this expansion in data and AI products is being able to offer large amounts of data without leaking information that can identify you as a person, also known as private personal information (PPI) or personally identifiable information (PII). Hazy, a UK-based AI company, offers its customers just that: the ability to derive insights, understand signals, and share data using synthetic data. Because of the nature of data-hungry deep learning models, synthetic data is preferred when training neural networks, and because of that, Hazy has a bright future ahead of itself. After an initial seed funding round of $3 million, it acquired $9 million in funding during a series A round in March 2023 led by Conviction VC. Their most recent round included funding from UCL Technology Fund and M12, a Microsoft venture capital fund.

The nature of a successful B2B company lies in its ability to create success for the businesses it supports, and though we’re firmly in the data-rich era of big data, machine learning and deep learning still require massive volumes of data to learn and retrain from regularly. Hazy has done a fantastic job of alleviating businesses’ pain points when it comes to data availability, and it’s evident in the loyalty of their customer base. Another way Hazy maintains this loyalty is by educating its customers on the ethical and legal ramifications of traditional ways of anonymizing or masking existing real-world data.

In the world of generative AI and LLMs, Mistral AI is emerging as a strong competitor to OpenAI’s ChatGPT product. Their most powerful B2B product is Mistral Large, which has already been sold to Microsoft for upward of $15 million to support their Azure platform. Their other product, Mistral 7B, an open source version of their foundational model, has also been used by companies like Hugging Face and is free to use. Companies like Mistral AI are taking the idea of powerful LLMs and customizing them for more specialized uses. This is a significant step in the direction of AI democratization because it allows for companies that want to adopt AI without having to front the significant cost, commitment, and time to develop their own LLMs. When companies as large as Microsoft accept this kind of partnership from third parties, it’s clear that it’s a solution that can help transform the B2B landscape when it comes to making generative AI more accessible.

Another great example of an AI company driving success in the B2B marketplace is a California-based gaming company called GGWP. While they don’t create their own games, they use AI to reduce toxicity in the gaming culture and provide gaming companies with a dashboard where they can see how their users are performing from a moderation perspective. As gaming companies consistently take a more serious look at the safety and health of their gaming communities, they will come to rely on companies such as GGWP to ensure all their users feel safe. Moderation has long been a topic of discussion in social media companies and gaming companies alike, and it’s really nice to find an AI company finding success in this field.

GGWP’s most recent funding round in July 2023, led by SK Telecom Ventures and Samsung Ventures. won them $10 million in funding. Their success serves as a positive reminder of the importance of leveraging AI over human workers in specific fields. The biggest issue in employing human moderators is first and foremost the emotional toll it takes on their mental health as they go about their day. Scanning for hurtful or violent language isn’t for the faint of heart, and finding depictions of graphic or hateful content on a consistent basis can deflate even the strongest of us. There’s a strong case to be made for the ethical use of AI in helping us tackle the problems that we, as human beings, would rather not be tasked with.

There are also many examples of big tech companies that are investing heavily in expanding their B2B AI products. Microsoft has integrated AI into its business solutions in products like Dynamics 365 that provide enhanced customer relationship management (CRM) and enterprise resource planning (ERP) systems through things like leveraging predictive analytics, customer insights and behaviors, decision-making automation, and operational efficiency. Microsoft uses models like BERT and GPT (transformer models) for text understanding and language generation. Google Cloud has also been leveraging B2B AI offerings in healthcare applications by providing AI tools for imaging analysis, patient data management, and personalized treatment recommendations. In the case of Google, they use their own models (BERT and T5) for medical data extraction, summarization, and clinical decision-making, both of which are also transformer models. IBM Watson has also been expanding into financial services applications by leveraging AI for risk assessment, fraud detection, and customer service automation. IBM uses BERT, XLNet, and other tailored deep learning models for analyzing customer sentiment, interpreting contracts, and automating workflows.

When selling to other businesses in the B2B world, there’s a standard operating procedure most businesses are comfortable with following. This includes everything from sales tactics to the approvals of budgets or statements of work to getting contracts approved through leadership, procurement, and all stakeholder teams in between. Because most B2B products are concerned with cost saving, productivity, and revenue generation, there are more structured interactions between all the players involved.

A couple of takeaways from the professionals are:

• Businesses looking to expand their B2B AI applications should start with core business needs first. Addressing specific business challenges, like improving CRM, operational efficiency, or risk management, is not only good business but will help you get traction from your business stakeholders. The closer you can align AI integrations and applications to business objectives, the easier the journey to getting buy-in will be.
• In many of these applications, AI is being leveraged to enhance human decision-making, so look for contexts where you can enhance predictive analytics and gather insights from large datasets that will help those within an organization come to a data-driven decision, particularly in cases where a business is helping to predict customer behavior or detect fraud. If we take predicting customer behavior further, we arrive at a deeper level of personalization. So, finding ways to apply AI functionality in ways that enhance customer satisfaction, loyalty, and lifetime value is also an elegant solution that is well worth the AI investment, particularly if you’re able to leverage this at scale.

The spirit of a professional is competence and integrity, no matter what arises in the moment, and B2B markets can be thought of in many ways as well-oiled machines. Much like the products they represent, these companies themselves are looking for ways to optimize productivity, generate revenue, and lower costs in their sales cycles and market interactions. By starting with a clear strategy and selecting the right AI tools and applications, businesses can make a significant impact in their markets and elevate their products for the new competitive landscape.