Fueling the AI revolution:
The critical role of AI-ready data
Strategies for choosing trustworthy and effective AI systems
Gen AI is already
proving its worth ...
Generative AI is a catalyst for business modernization, not just another technological advancement. Companies achieve substantial financial gains, heightened efficiency, and sustainable growth by strategically integrating it into their operations. The message for CEOs is clear: generative AI is not optional for remaining competitive in the rapidly evolving market. According to a recent report by Google Cloud:
3 in 4
organizations (74%) are currently seeing ROI from their gen AI investments.
+6%
growth in revenue: 86% of organizations using gen AI in production and seeing revenue growth estimated 6% or more gains to overall annual company revenue.
84%
of organizations successfully transform a gen AI use case idea into production within six months.
Enter some text...
…but AI is only as good as the data behind it
Generative AI is a catalyst for business modernization, not just another technological advancement. Companies achieve substantial financial gains, heightened efficiency, and sustainable growth by strategically integrating it into their operations. The message for CEOs is clear: generative AI is not optional for remaining competitive in the rapidly evolving market. According to a recent report by Google Cloud:
43%
of organizations are prioritizing data quality and knowledge management investments.
Data challenges
As anyone who has used publicly available gen AI chatbots has discovered, general-purpose large language models (LLMs) can be limited in their ability to produce results that are relevant and accurate enough to support business decision-making. Getting actionable insights from gen AI faces challenges that these LLMs were built to overcome.
Normalization: Diverse unstructured data sets, such as sales call notes, support tickets, and poll results, create difficulties in processing and analysis.
Accuracy: LLMs trained only on public internet data are prone to hallucinations that make it difficult to trust the responses they deliver.
Completeness: LLMs cannot easily pull in data from other applications or integrations to ensure that the analysis takes into account the appropriate inputs.
Relevance: Non-customized LLMs often draw on data that isn’t relevant to the analysis being performed, diluting accuracy and clarity.
Enter some text...
Is your data ready for AI?
67%
of organizations say they don’t completely trust the data used for decision-making.
49%
report inadequate tools for automating data quality processes as the top factor preventing high-quality data.
75%
of organizations expect to face scalability challenges with their AI-driven data systems.
Solution: a purpose- built approach
Large language models (LLMs) are powerful tools, but when it comes to supporting business-critical decision-making, you need a purpose-built data pipeline to ensure quality and trustworthiness. This requires a multi-layered strategy, from initial data collection and structuring to leveraging advanced cloud capabilities for AI model training and application. The ideal solution should provide:
Curated datasets that emphasize accuracy, completeness, consistency, and relevance of data. This means ensuring the data is correct, has sufficient context, is uniform, and is pertinent to the task at hand.
Data structuring and normalization to transform unstructured data into a structured, normalized format that AI models understand and use effectively.
A powerful cloud platform for building, training, and fine-tuning machine learning models. These tools also help manage and process large datasets, ensuring data quality at scale.
Pendo and Google Cloud help organizations purpose-build their AI data pipelines
Powered by Google Cloud’s data processing, Pendo’s software experience platform collects data about your application usage and engagement to provide a 360-degree view of user behavior. This gives teams visibility into what’s working and what’s not, enabling them to make data-driven decisions about improving their applications.
Optimize product usage and increase adoption through retroactive analytics, segmentation, and targeted in-app guidance.
Streamline compliance and deliver enterprise- ready experiences with in-app workflows, BigQuery reporting, and Vertex AI-powered checks.
Improve R&D efficiency by centralizing feedback, making data-driven prioritization decisions, and validating ideas faster.
Optimize the workforce by revealing journey friction in employee apps, guiding process changes, and speeding ramp.
Accelerate growth and help customers succeed by improving the user experience and encouraging the adoption of new paid features.
Curated data in, actionable analysis out
The quality of the insights that AI is able to deliver is dependent upon the quality of the data that informs them. The ideal AI-powered application should draw from carefully curated inflows of enterprise data that are:
1. Accurate
Insights and recommendations provided have minimal hallucinations.
2. Complete
Includes all the data necessary to create a clear picture.
3. Consistent
Formatted in a way that AI can interpret.
4. Relevant
Provide AI only with relevant data, removing extraneous information.
RAG for the win
Retrieval Augmented Generation (RAG) is a technique that enhances the capabilities of LLMs by allowing them to access and incorporate information from external knowledge sources. RAG offers significant business value in several ways.
Improved accuracy: By grounding responses in real-world data, RAG reduces the risk of LLMs generating inaccurate or fabricated information.
Enhanced contextual relevance: RAG allows LLMs to provide more nuanced and context-aware responses by drawing on relevant information from specific domains or knowledge bases.
Real-time information: Using RAG to access and incorporate real-time information, enabling businesses to provide up-to-date responses and make informed decisions.
More current results: Rather than constantly retraining large language models on ever-changing data, RAG allows for the external data to be updated, keeping the results current.
Normalize unstructured data
The success of an AI-powered application depends not only on the volume of quality data it is able to evaluate, but also on the variety of data from multiple relevant sources, including unstructured data such as sales calls, support tickets, or polls. A best-in-class AI application will normalize unstructured data by transforming it into a structured version that an LLM can understand, in order to map it back to a user’s request.
For example
A user asks how many support tickets reference an issue within the project management tool Jira. The unstructured raw data is transformed into clean data that results in a 360-degree view of the user experience around that issue.
Case study
How ESO finds signal in the noise with AI-powered customer intelligence
ESO, a software company focused on improving community health and safety through data, faced challenges with disorganized user feedback, which hindered their ability to prioritize and act efficiently. To address this, ESO utilized Pendo Listen’s AI tools to gather, analyze, and validate feedback. This helped them tackle three problems at once: gathering and analyzing feedback, validating product ideas, and transparently keeping users and stakeholders involved.
Gather and analyze feedback: Drastically cut analysis time by quickly sort through large amounts of feedback to identify patterns, avoid overlooking blind spots.
Validate product ideas: Improved discovery with suggested ideas and fast, in-app validation to de-risk roadmap decisions.
Keep users & stakeholders informed: Democratized insights across teams, leading to faster customer responses and less overwhelmed product managers.
Leverage a cloud platform built for data analytics and AI
A best-in-class cloud platform offers several advantages to make AI solutions more powerful. These include:
AI and machine learning capabilities. AI applications leverage built-in tools to analyze data, make forecasts, or tailor user experiences at scale.
Data processing and analysis. Cloud data processing and analysis deliver a comprehensive picture of a complex area such as customer experience.
Scalable infrastructure. Scalable infrastructure allows organizations to handle massive amounts of data and provide enterprise-grade security even during peak usage without compromising performance or data privacy.
Powerful computing resources. Only the cloud can provide the vast compute resources required for performant AI across multiple pipelines to create predictions and pattern-matching layers.
Case study
How Thomson Reuters stays a step ahead of the search box
Thomson Reuters tracked user interactions in Pendo to get insight
into three critical stages of their users’ search experience. This gave
them a complete picture of an application feature’s performance
and how it was impacting user sentiment.
Optimize software experiences using advanced analytics and AI-powered insights to drive user engagement and business results with Pendo on Google Cloud.
The Pendo software experience management (SXM) platform leverages the power of Google Cloud to generate trustworthy AI insights that adhere to strict data management best practices. It combines qualitative, quantitative, and visual data to provide a 360-degree view of your users’ actions. It then uses AI to combine this complex data and drive the right outcomes for your business.
Together, Pendo and Google Cloud empower you to build and deliver exceptional user experiences. Pendo is built on Google Cloud’s AI-ready infrastructure and robust AI capabilities to provide a scalable, secure, and intelligent solution.
Pendo on Google Cloud helps you differentiate your products and services by offering an all-in-one solution that combines quantitative, qualitative, and visual insights. This allows you to understand and optimize software usage, increase engagement and adoption, and ensure compliance and process optimization. Pendo leverages Google Cloud’s AI and machine learning capabilities, including Vertex AI and Gemini, to personalize messaging and insights at scale, further differentiating businesses from competitors.
Pendo is available on the Google Cloud Marketplace.
Learn more about how Pendo on Google Cloud helps you improve the apps you build, buy, and sell so you can deliver better customer and employee experiences.
