What is the best generative AI platform for market insights and knowledge management?

A comparison between DeepSights™, Microsoft Copilot, and DIY generative AI for market insights tools 

If you are an insights professional conducting market research and analysis, you know how tricky it can be to get the right market insights, at the right time — and to share these quickly with the business. That’s where generative (gen AI) capabilities come in to support you in this process.  

But in the ever-evolving gen AI space, you are increasingly flooded with options for market insights. It can be overwhelming to choose. For instance, you may be choosing between building an in-house Retrieval Augmented Generation (RAG) solution or investing in a proven, market-ready platform. You might already be experimenting with Microsoft Copilot, which has become the default option some enterprises end up trying out. 

So, when it comes to gen AI for insights, what is the difference between the available options on the market? What capabilities do you need to consider for knowledge management, and how do the platforms fare when comparing these? 

Deploying the right AI solution for your organization’s market research needs is crucial. Learning what generative AI can do for you — from market research to knowledge management — and understanding the difference between gen AI market insights offerings, is crucial. To help you in your search, we break these down below. 

Features to prioritize when selecting generative AI for insights market research tools 

With so many generative AI for market insights platforms offering different capabilities, narrow down contenders by focusing on performance of the following features:  

• Insights ecosystem integration: How well-connected is the platform to your entire insights data ecosystem? From insights to action, your gen AI platform should be able to pull in all your sources and easily integrate insights into any system you use. This enables insights managers to access only trusted knowledge and data in near real-time, and to flow the insights easily to the business. With Microsoft Office 365 being the primary information ecosystem for enterprises, ask yourself: How easily can the AI platforms you are considering integrate with repositories like Microsoft SharePoint and Google Drive? Are out-of-the-box connections available to business tools — like Microsoft Teams, Google Chat, or Slack — which allow your colleagues to easily access the platform, wherever they work? 

• Semantic understanding of document content: A major challenge in interpreting market research presentations is accurately identifying and handling different types of information, such as study background, sample descriptions, questionnaire statements, respondent quotes, research findings, and vendor pitches. An AI that doesn’t recognize these nuances can make significant errors, like mistaking questionnaire statements or individual quotes for representative data, or misinterpreting sample compositions as actual consumer numbers. For example, taking questionnaire statements (“Is a brand I liked”) or individual customer quotes (“The new package looks like a jewel box!”) for representative ground truth. Generic gen AI tools and generalist in-house platforms do not come with the built in understanding of market research content, which could lead to missed information, or worse — misrepresentation of your valuable insights. 

• Awareness of contextual applicability: Consumer, category, brand, and market knowledge is highly context-dependent and perishable, meaning insights valid in one market or time may not apply elsewhere or later. An AI ignoring these factors may offer irrelevant information. For example, imagine an AI tool applying pre-Covid Italian consumer attitudes to present-day England. Without your context-dependent knowledge, such misunderstandings are likely to happen.  

• Customer-specific context and best practices: It is crucial to customize the AI by providing customer-specific context for each business to reflect corporate context and best practices accurately. These settings include company-specific jargon, background knowledge, and guidelines on data usage. Ideally, your gen AI platform allows individual users or admins to configure such settings, enabling the AI to adapt to their specific context. 

• Intuitive user experience: Does your gen AI platform effortlessly integrate complex AI processes, allowing you to input questions or problems and receive optimal responses? A design that eliminates the need for user training and includes robust interaction guardrails to prevent misuse from inadequate prompts should be prioritized. 

Comparing Microsoft Copilot, DeepSights™, and DIY Solutions: Key differences 

Now that you know some of the key features to look out for in generative AI for market insights, explore the difference between commercial solutions like Microsoft Copilot, DeepSights™, and developing your own in-house (DIY) Retrieval Augmented Generation (RAG) systems. Here’s a comparison overview: 

Insights ecosystem and data integration

• Microsoft Copilot: It operates within the Microsoft Office 365 environment, leveraging data from Office applications. But while powerful in its ecosystem, it does not offer customization to the extent needed for specific market insights applications. It can fully leverage data in the Microsoft Office environment as well as general web content via Bing search, but has no means of integrating with specific gated external sources and partners. Overall, it is a general-purpose assistant lacking the specificity needed for market insights. 

• DeepSights™: The purposely-built gen AI for insights platform seamlessly integrates with Office 365 and Google Workspace and is specifically tailored for market insights, providing deep contextual understanding and specialized capabilities for handling market research data. It allows extensive customization, enabling users to adapt the AI to their specific corporate context and best practices — which is crucial for gaining accurate and relevant insights.  

When it comes to data integration, DeepSights™ comes with pre-integrated access to a wide range of data sources, including specialized research tools, making it easier to deploy and use immediately. With the addition of newly released DeepSights™ API, the platform unifies your proprietary, paid, and public sources — and connects knowledge to business action.  

The API seamlessly integrates DeepSights™ outputs with downstream applications, allowing for unified, evidence-based responses across an organization’s tools. It also supports advanced content search within ingested data and streamlines document upload and download. This comprehensive functionality helps your business maximize your data’s potential, enhancing decision-making and operational workflows with minimal effort. Stay tuned for our upcoming blog post, where we will share tangible use cases for the DeepSights™ API. 

• DIY RAG system: Many organizations opt to build their own solutions, either tailored specifically for Market & Consumer insights content, or for general purposes across various internal knowledge repositories like HR materials, onboarding documents, sales data, and company strategy documents.  

But building an in-house RAG system poses substantial challenges in integrating internal repositories with external data sources, particularly around gated or proprietary content. While accessing internal and general web content is feasible, integrating internal and external data sources in-house can be challenging. Connecting with specialized research tools and external partners demands extensive customization, development, and commercial negotiations regarding third-party content access and ongoing partner management.  

2, 3 – Semantic understanding and awareness of contextual applicability 

• Microsoft Copilot: While it can handle user tasks and document sifting, it lacks proficiency in understanding evidence and knowledge, because qualitative data interpretation poses challenges as language-based answers may conceal inaccuracies. Generic AI tools often overlook nuances in market research, target consumers, and competition, necessitating a system tailored to interpret such data accurately.  

Copilot is not bound to provide responses based on your proprietary knowledge base. Rather, it may provide responses based on web content, emails, or other internal documents it comes across in the MS environment. Additionally, Copilot does not consistently provide citations that point back to the evidence informing its responses. 

• DeepSights™: Unlike generic AI tools, purpose-built AI platforms like DeepSights™ are adept at scanning entire insights repositories for relevant findings, minimizing hallucinations and inconsistencies. By using proprietary methods to evaluate contextual relevance, including specialized evidence classifiers, DeepSights™ implements proprietary approaches and advanced methodologies to ensure contextual relevance and accuracy, providing high-quality insights without the need for continuous development and adjustments. It discerns relevant knowledge — leading to superior answers and actionable insights. This saves time as you can trust their accuracy in handling market insights data. 

• DIY RAG system: Achieving the same level of contextual sensitivity and accuracy in an in-house system is complex and demands ongoing refinement. In our experience, in-house platforms tend to be built with generalist document ingestion in mind, in order to satisfy cross-team needs internally.  

4. Customer-specific context and best practices: 

It is crucial to customize settings for each business to reflect corporate context and best practices accurately. In this case: 

• DeepSights™ enables customers to configure such guidance to adapt the AI to their context. 
• Microsoft Copilot, on the other hand, offers no such mechanisms.  
• An in-house RAG system can be an ideal solution, but is costly and time-consuming, often requiring extensive expertise and resources that many organizations lack. 

5. Intuitive user experience (including implementation and expertise)

• Microsoft Copilot: Qualitative data interpretation is challenging because language-based answers can hide hallucinations and misinterpretations. In this case, Microsoft Copilot supports simple interactions and prompts but does not facilitate complex multi-stage processes.  

Copilot requires users to provide detailed context and instructions through prompting techniques, necessitating training to obtain useful results. Users may inadvertently misdirect the AI with inadequate prompts. Although Copilot’s prompt library helps, it is limited to simple, single-interaction exchanges and cannot handle complex, multi-stage processes. This makes it challenging to rely on Copilot as a single-source-of-truth for obtaining answers to business questions specific to market insights that are grounded in your proprietary research and trusted, third-party sources. 

• DeepSights™: Designed with user-centric principles, DeepSights™ provides complex, multi-stage AI processes within the product, allowing users to receive optimal responses simply by inputting their questions or problems. This design eliminates the need for user training and includes strong guardrails to prevent misuse through inadequate prompts. By simply asking business questions, users can obtain answers to business questions and optionally generate deep dive research reports. 

DeepSights™ offers a ready-to-use, sophisticated solution with built-in capabilities specifically designed for market insights, eliminating the need for extensive development and customization efforts. It encapsulates complex AI flows that require no training for users to interact effectively, ensuring strong interaction guardrails. 

• DIY RAG system: DIY RAG-system solutions provide customization possibilities, but come with high costs and complexity attached, along with the possible additional challenge of ensuring market insights-specific customizations are introduced into generalist corporate tools. Creating a user-friendly interface that minimizes the risk of inappropriate interactions requires substantial investment in design and training.  

Our tip? If you decide to develop your own solution, it is highly recommended to conduct an A/B testing exercise to compare your results with DeepSights™. This will allow you to directly evaluate the performance, scalability, and integration capabilities of our platform against your in-house development. Contact your customer teams to discuss how we can support this testing. 

Results and tips for getting started

How do the three generative AI solutions for market insights compare? 

Microsoft Copilot

Microsoft Copilot is a powerful assistant for general tasks, drafting emails, and sifting through documents. While it can pull in data from all over your Office 365 environment, it lacks proficiency in understanding specific market research, and here’s the thing: it’s a general-purpose tool. It’s not specifically built to handle the nitty-gritty details of market insights and intelligence. While it offers strong integration within the Office 365 ecosystem, it lacks the market insights specificity of DeepSights™ and consumer insights understanding. This requires an AI system tailored to interpret such specialized data, such as DeepSights™ or a DIY solution.  

Unlike generic AI tools, using a purpose-built AI platform — trained to meticulously scan your insights repository — avoids inconsistencies. It also reduces the need to verify results, by ensuring relevant and accurate answers. 

RAG solution

And while building an in-house RAG solution may appear to offer advantages, these typically do not address fundamentally important aspects when it comes to working with market insights and intelligence. Moreover, building a RAG offering is a costly and time-intensive undertaking. Most organizations do not have the know-how and expertise to build in a timely fashion and at a production-ready level, nor to invest in the keep and continual improvement of such a solution. 

DeepSights™

DeepSights™ on the other hand, offers tailored, ready-to-use capabilities specifically designed for market insights, which minimizes the need for extensive in-house development and expertise. It fully embraces and extends the Office 365 environment, including Copilot — and by using the new API, DeepSights™ seamlessly integrates with any business application. This helps to infuse invaluable insights into your business processes — and as a result, drive better decision-making in your organization. By leveraging a SaaS platform that constantly innovates with new features and improvements through close interaction with its customers, you can future-proof your organization and stay ahead of the competition. 

Conclusion 

When selecting a generative AI knowledge-management tool for your tech stack, prioritize solutions that: 

• Are purposely built for AI insights  
• Are specifically built and trained for that purpose 
• Possess architectural capabilities for interpreting data, along with providing insightful natural language processing (NLP) explanations. 

If you’re looking for a generative AI for market insights tool that understands and enhances your market insights, DeepSights™ stands out because it is a plug-and-play solution that requires minimal training. It is designed with features that specifically address the challenges that insights professionals face, and tailored for diving deep into market data. It’s like having a specialized AI expert by your side, making your job a lot easier and more effective.  

Get started today. Bring trusted generative AI insights into daily work effortlessly with DeepSights™. Transform the way you access and share insights, and how knowledge flows through the business.