Everyone is following the news about ChatGPT. The question is with the risks and limitations to using a publicly trained generative AI model, is there a successful way for B2B companies to build products with ChatGPT?
Gravitate Founder Qiuyan Xu discussed generative AI and ChaptGPT potential pitfalls with startup founders Davit Buniatyan, CEO of Activeloop and Eugene Malobrodsky, Partner at One Way Ventures. You can find the full article here in the OneWay Ventures newsletter.
Key takeaway: Build AI products that are sustainable both in their unique differentiators and in how the business delivers the customer experience.
As the newsletter references, ChatGPT is a "borrow" approach for product teams -- meaning it's not really a build or a buy decision. Creating a unique product without differentiating the data sources and the algorithms just isn't possible. And, there are inherent risks that come with an outside data source, even one that is as well trained and tested as ChatGPT. Companies need to think about brand reputation, customer data privacy and quality control.
Potential pitfalls for startups building without an AI product blueprint
Missed opportunities to start collecting strategic data assets.
Missed opportunities to customize algorithms and datasets to deliver a differentiated B2B product.
Building without proper infrastructure, thereby incurring massive technical debt and other unnecessary costs.
Just because there are risks and potential challenges to look out for, it doesn't mean it's impossible.
>> Curious what an AI product blueprint includes?
Using custom algorithms and infrastructure are key to a successful AI product strategy
At Gravitate AI, we believe the answer is yes, it's definitely possible to build successful generative AI products with components similar to or directly from ChatGPT. We've been making it happen with our clients, and here's a little of our founder's insights.
"Adding on algorithms and infrastructure are a recipe for success. We’ve seen it work really well when both technical and business sides come together to outline their best opportunities to differentiate with the unique data they have or that they can gather through their customer experience." Qiuyan Xu, PhD.
Startup founders and enterprise leaders who understand the key data assets needed to build a defensive Moat (read more about building AI technical long-term competitive advantage) and develop data-driven products, will be able to make difficult trade-offs, leading teams to prioritize product development that positively impacts their AI advantage.
Read Insights from 3 experts on generative AI product strategy here.
Two case studies demonstrating how to get it right with generative AI
There are examples out there where companies are getting it right with their AI products, including how they’ve developed a blueprint to create strategic data assets as part of a larger AI product strategy.
In today's fast-paced business environment, several organizations are successfully addressing enterprise-level complexities, deploying artificial intelligence (AI) products without relying on off-the-shelf offerings. These two case studies show how companies can create custom chatbots or generative AI products that integrate human feedback and domain-specific knowledge. They each prove there are ways to ensure custom data remains secure and adheres to customer data privacy standards without compromising customer trust.
ZERO bridges the gap between generative AI and enterprises while preserving high security standards
To generate high-quality models for custom enterprise AI solutions, ZERO has applied high-quality labeled data, while maintaining ethical walls and data access rules. This success would not be possible using ChatGPT as a stand-alone solution.
Company profile: ZERO, a cognitive automation company, creates smart solutions that enable professionals to spend more time on higher-value work by giving knowledge workers back time and driving productivity. Professional services teams use ZERO solutions to automate, organize, and advance their timekeeping and data management tools.
Use Case: Fortune 100 companies in the professional services industries, such as legal and consulting, can increase billable hours by automating a large portion of their highly manual regular reporting and billing-related tasks.
The majority of data in corporations is unstructured, which makes it unusable for AI applications unless it’s structured. Since general models like GPT may not suffice for domain-specific tasks or real-world use cases, organizations must employ both general Large Language Models (LLMs) and domain-specific models fine-tuned to their data.
AI application: ZERO has created a solution that simultaneously generates structured data where there is none and maintains enterprise data integrity. For example, financial or project-based reporting can be fully automated by connecting emails, legacy systems such as CRM, and project management software through a prompt-based API and harnessing the power of LLMs.
ZERO’s AI engine, Hercules, operates within the client's security perimeter and automatically labels unstructured data. This process enriches, interconnects, and depersonalizes the data, preparing it for multi-mode. ZERO's solution also automatically inherits ethical walls and data access rules, ensuring sensitive elements are stripped from outgoing data, while maintaining role-based internal access.
Outcome: This highly secure, hybrid approach to AI for regulated industries and Fortune 100 clients enables the implementation of generative AI and LLMs with high security standards. As a result, organizations can achieve a 20x improvement in productivity and efficiency in their business processes.
TrustCloud ensures customer trust by customizing AI while building strategic data assets
For companies building out a B2B customer experience that includes generative AI, it’s important to build with trust as a priority. That means data stays within the organization, and the most stringent data governance and privacy practices apply. For industries with a heavy focus on security and compliance, there may be concern about upholding compliance standards with publicly trained data models. Having an effective AI solution under these strict restrictions presents companies with extreme challenges and TrustCloud has the AI-based solution.
Company profile: With the fastest, most cost-effective way to get audit-ready, answer security questionnaires and track risk, TrustCloud turns Governance, Risk & Compliance (GRC) into a profit center. TrustCloud empowers businesses to earn trust from their partners and customers with a transparent, reliable compliance program. Predictive intelligence and programmatic verification ensure companies meet their customer, audit, and governance commitments so they can stay secure and grow their business.
Use case: Reduce the time and resources required to complete security questionnaires by automating responses based on prior questionnaires and a company's current security posture. Security questionnaires are sent during the sales process, when an enterprise is determining whether they can work with a specific vendor. Currently, most companies are relying on their employees to hunt down answers on an ad-hoc basis, or try keyword searches in a status knowledge base to find previous answers, update them, and insert them into a questionnaire. Both paths are very time-consuming and leave a lot of room for error.
AI application: TrustCloud applies natural language processing and deep learning to create the best responses specific to a company’s security and compliance program.
Using generative AI to match security questions with answers pulled from a users’ compliance program and previous questionnaires, TrustCloud applies custom domain knowledge as part of the algorithm tuned to the security and compliance domain, incorporating a specific internal lexicon.
Similar to ChatGPT’s reward model, using human labeling to reinforce Natural Language understanding, TrustCloud has a dedicated compliance expert that frequently reviews the output of the algorithms, and provides domain feedback to improve the accuracy of the results. Additionally, customers review the output, and any edits they make are fed back into the model. That human touch along with the high accuracy improves the quality of answers over time, and develops more trust with its customers. In this process, TrustClouds builds its own strategic data with smart annotations.
Outcome: TrustCloud completes 60-70% of new questionnaires and 100% of previously-seen questionnaires, saving customers hundreds of hours that would be otherwise spent finding, updating and inserting answers to questionnaires. The increased accuracy and efficiency ensures these customers never have to jeopardize a sales deal due to security questionnaires.
Companies building generative AI products should think beyond ChatGPT
Startups CAN avoid the potential development pitfalls and create success with the right data, the right customization, and the right tooling. Even with the popularity of ChatGPT that does not change. If anything, it puts more pressure on companies to differentiate their experience further so the end user doesn’t go elsewhere once they tire of the novelty ChatGPT currently provides.
So what can B2B business leaders do to develop a product roadmap with the right guardrails for including ChatGPT? Be sure to include these three elements in the AI product strategy:
Data Moat - Developing key defensive data assets ensures B2B companies don’t miss valuable opportunities to differentiate. As legal and ethical debates rise and leveraging public data for training diminishes, access to proprietary, high-quality data has never been more important for verticalizing your generative AI product (e.g., artwork for indie games or architectural design elements).
Customized AI algorithms - Companies will see the most success combining company knowledge with domain specific data and internally-generated company data. Tech leaders and founders should monitor trends in Large Language Model API providers who, due to cost pressures and performance concerns, are converging towards a single, general LLM (like GPT-4). Domain-specific LLMs have the power to outperform general LLMs and provide a competitive edge to companies.
Infrastructure - B2B startups need to be able to operate in a lean fashion, and build their infrastructure to handle scale. Companies utilizing solutions optimized specifically for foundational model training, provided by companies like Activeloop, can improve time-to-market, and ship their generative AI products faster.
Even with a sustainable infrastructure and strong differentiator, there are other potential blockers keeping startups from successful AI products. We are considering exploring the risks and legal implications in a second article. Please let us know your thoughts and questions and give it a like if this article was helpful.
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