THE Generative AI the party is still raging. This zeitgeist has shaken the business world in a thousand and one ways every day, and the ground is still shifting. Now, four months into 2024, we are starting to see companies, especially those with rarefied pragmatic brands, begin to demand proof of value, of the path to true AI-derived ROI . As pragmatic voices for value are heard, what is the response from thoughtful business leaders?
Alteryx has researched exactly this question. What are the concrete paths to AI value? We interviewed leading CIOs and board members and discovered a highly informed approach to integrating emerging AI capabilities into business outcomes.
Our investigation revealed that Generative AI is already having an impact the achievement of organizational objectives in 80% of organizations. What led the way, as use cases #2 and #3, was analytics, both the creation and synthesis of new information for the organization. These use cases lagged behind only content generation in terms of adoption.
What makes analytics and generative AI such a powerful combination? To explore this, let’s start by looking at the key challenges that generative AI solves, how it works, where it can be applied to maximize the value of data and analytics, and why generative AI requires governance to succeed.
Overcoming Analytics Challenges with Generative AI
Businesses have long recognized the benefits of using data and analytics to improve revenue, manage costs and mitigate risk. Yet achieving large-scale data-driven decision-making often becomes a slow, painful and inefficient exercise, due to three major challenges.
First, there are not enough data science, AI, and analytics experts to provide the breadth of insights needed across all aspects of the business.
Second, businesses are often hampered by legacy, siled systems that make it difficult to know where data is, how to access it, and how to use it.
Third, even though we face the first two challenges, data continues to grow in complexity and volume, making it much more difficult to use. Combined with the lack of strong governance policies, companies are then faced with poor quality data that cannot be relied upon to make decisions.
Apply Generative AI to Analytics
Generative AI presents two huge opportunities to address these challenges by improving the usability and efficiency of business analytics tools.
Let’s talk about usability first. Generative AI makes it easier to use analytics tools. Much of this is due to the incorporation of natural language interfaces that make it much easier to use analytics, as the “coding language” can be a simple natural language. This means that users can perform complex analysis tasks using basic English (natural language) instead of learning. Python. As we all know, coding languages have a high learning curve and can take years to truly master.
Then, in terms of efficiency, generative AI significantly improves the quality of automation that can be applied throughout the data analysis lifecycle, from extract, load and transform (ELT) to data preparation, analysis and reporting.
When applied to analytics, generative AI:
- Streamlines the fundamental steps of ELT data: predictive algorithms are applied to optimize data extraction, intelligently organize data during loading, and transform data with automated pattern recognition and normalization techniques.
- Accelerates data preparation through data enrichment and quality: AI algorithms predict and fill in missing values, identify and integrate external data sources to enrich the dataset, while advanced recognition Shapes and anomaly detection ensure data accuracy and consistency.
- Improves data analysis, such as geospatial data and autoML: Mapping and spatial analysis via AI-generated models enable precise interpretation of geographic data, while automated selection, tuning and validation of machine learning models increase the efficiency and accuracy of predictive analytics.
- Elevates the final step of analysis, reporting: Customized, generative AI-powered applications provide interactive data visualizations and analytics tailored to specific business needs. At the same time, natural language generation transforms data into narrative reports (data stories) that make information accessible to a wider audience.
Top Use Cases of Generative AI for Analytics
The impact of generative AI for analytics is clear. Integrating generative AI into analytics can unlock the capabilities of major language models and help users analyze mountains of data to arrive at answers that drive business value. Beyond content generation, main use cases for generative AI are summarizing analytical information (43%), generating analytical information (32%), developing code (31%), and documenting processes (27%).
Alteryx is well equipped to support a range of generative AI applications, including the following use cases, offering both the development tools and deployment infrastructure:
- Insight Generation: Generative AI can work with different data sources and analyze them to provide insights to the user. To add more value, it can also provide and summarize this information in more understandable formats, such as an email report or PowerPoint presentation.
- Building Datasets: Sometimes using real customer or patient data can be expensive and risky, but generative AI can create synthetic data to train models, especially for heavily regulated industries. Using synthetic data to establish a proof of concept can accelerate deployment, save time and costs, and most importantly, reduce the risk of violating any potential privacy laws or regulations.
- Workflow summary and documentation: Generative AI can automatically document workflows to improve governance and auditability.
Building a holistic and governed approach
While there are countless automation opportunities and new use cases yet to be discovered, leaders must understand that trust in AI and LLMs depends on the quality of the data captured. The insights generated by AI models are only as good as the data they have access to. The success of generative AI requires the application of data governance in responsible AI policies and practices for AI adoption.
On its own, using generative AI without guardrails can lead to data privacy issues, inaccurate results, hallucinations, and many other risks, challenges, and limitations. It is important that businesses work with vendors who have industry-standard principles and frameworks in place to ensure they can responsibly adopt generative AI at scale.
To help businesses mitigate these risks, Alteryx integrates different mechanisms within its platform to control these challenges and simplify the AI governance process throughout the lifecycle, while remaining grounded in principles that help us , we and our customers, to adopt AI responsibly. For example, we have built our platform to provide private data processing capabilities, enabling our customers to track their AI training and deployment entirely within their own firewall.
Finally, it is extremely important to implement appropriate controls and integrate human feedback mechanisms to enable continuous verification and validation of AI models. This ensures their accuracy, reliability and alignment with desired results.
Integrate emerging AI capabilities into business outcomes
When used in a responsible, secure, and governed manner, generative AI can lead to key benefits such as market competitiveness (52%), improved security (49%), and improved product performance or functionality (45%).
With Alteryx AiDIN AI Engine for Enterprise Analytics, Alteryx makes navigating the generative AI landscape within an organization smoother and more manageable for analytics. Overall, the platform helps organizations leverage their generative AI investments by applying generative AI to their data to improve customer experiences, streamline operations, and drive personalized interactions.
Asa Whillock is vice president and general manager of machine learning and artificial intelligence at Alteryx.
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Generative AI Insights provides a place for technology leaders, including vendors and other external contributors, to explore and discuss the challenges and opportunities of generative artificial intelligence. The selection is broad, ranging from in-depth reviews of technology to case studies to expert opinion, but it is also subjective, based on our judgment about which topics and treatments will best serve the technically sophisticated audience from InfoWorld. InfoWorld does not accept marketing materials for publication and reserves the right to edit all contributed content. Contact doug_dineley@foundryco.com.
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