Imagine a tool that doesn’t just analyze data but ignites it into a transformative source of innovation. Generative artificial intelligence stands at the forefront of such a revolution, promising to reshape our entire approach to data interaction, task automation, insight generation, and decision-making. But the question remains: How can your organization harness this powerful tool?
To embark on this journey, we’ll lay down a solid foundation of generative AI, establish frameworks for creating use cases, explore top industry examples, and explain why analysts play pivotal roles in implementation and management.
Consider the potential: McKinsey research suggests that generative AI could contribute between $2.6 and $4.4 trillion annually to the global economy. With such staggering figures on the horizon, the opportunities for analysts to champion generative AI are boundless.
Defining Generative AI
While there are many ways to explain what generative AI is and how it works, one of the simplest ways to describe it is that generative AI is an AI model trained on a lot of language data that creates novel outputs.
Key things to know to understand generative AI:
- Generative AI is powered by a language model (most are large language models, but there are small language models as well.)
- Generative AI is enabled by a technology called transformers. Transformers allow machine learning to focus on distinct parts of input differently. They are the foundation of advanced AI language systems.
- A GPT model is a pre-trained transformer. This is the breakthrough that has enabled generative AI.
A use case framework for exploration
There are a lot of different use cases for generative AI; listing them all would be impossible. Instead, I have found the following framework helpful when researching use case applications.
Look at the capabilities of generative AI, what it is good at, and where it can excel, and then extrapolate a use case from there.
What generative AI is best at:
- Summarization – “Read this travel and expense report and summarize corporate spending for the month in one paragraph.”
- Code generation – “Using these variables, write a Python script that will predict my sales for the next quarter.”
- Data generation – “Create a dataset that imitates corporate travel and expense data. Include columns for First Name, Last Name, Description, Date, and Amount.”
If you use these three capabilities as building blocks, you can then piece them together and inform the use cases you want to explore. Using this framework, let’s get started and break down some of the most common generative AI use cases.
Text Generation
Generative AI can create copy in different voices, tones, and styles based on user input, speeding up processes such as writing emails, outlining blog posts, and summarizing data and analysis.
Insight Generation
Generative AI can work with different data sources and analyze them to provide insights. It can even do this by summarizing the results in an email or creating a PowerPoint presentation to speed up the process.
Data Set Creation
When healthcare facilities, financial organizations, and other heavily regulated industries need to create and test models, it can be costly and risky to use real patient or customer data. Generative AI can create synthetic data to train models. Then, once the models are ready, they can apply them to their patient data. Along with reducing the risk of violating regulations, this method can also speed up deployment, saving time and reducing costs.
Natural Language Interface
You can use generative AI to speak directly to your data. The technology can use natural language processing (NLP) to interpret what you’re asking, query your data to find the result, and return its findings to you in a way you can understand.
Workflow Summary and Documentation
Documenting workflows is a task that needs to be done but also one that (almost) nobody likes. Generative AI cannot only do this automatically, but it can also improve governance and auditability.
Generating Use Cases
Yes! You read that right. One of the use cases for generative AI is identifying, selecting, and building new analytics use cases for you. It can cut back any indecisiveness by automatically ideating new ways to use generative AI.
Tools like Alteryx Playbooks can use generative AI to generate possible use cases based on your data.
According to recent research, some of the most common use cases for IT and data leaders when using generative AI include:
- Data analysis (43%)
- Cybersecurity (37%)
- Customer support (34%)
- Code generation (32%)
- Financial forecasting (32%)
- Text generation (32%)
Real-world applications of AI and machine learning for analytics
Let’s explore the ways different industries and departments can use generative AI to enhance their business decisions and drive innovation.
Finance
- Financial Trend Analysis: Many factors can affect financial performance, and understanding all of them is a time-consuming task. AI and machine learning can analyze financial data for trends and outliers and provide explanations for how they relate.
- Banking Risk Assessment: Risk comes from many different areas in banking, and it requires an exhaustive level of detail to assess it all. AI and machine learning can apply deep learning mechanisms to supply comprehensive risk assessment reports. It can closely examine data to generate detailed explanations to help clarify and share while including recommended actions to mitigate risk. This speeds up the process of identifying potential problems and allows more time to decide how to proceed. Zurich Insurance used Alteryx to analyze large volumes of risk, claims and financial data.
- Tax Compliance Analysis: Tax preparation or accounting teams can provide tax compliance data to generative AI for analysis, plus generate comprehensive tax compliance reports. Information provided includes:
- Explanation of tax codes
- Potential deductions
- Recommended strategies for minimizing tax liabilities
Human Resources
- HR Talent Pool Optimization: HR teams can use AI and machine learning to help with employee retention projects that may need to work extra hours to complete. AI can provide personalized recommendations on which skills employees should develop for career growth. With the time that AI saves, HR teams can evaluate the recommendations and adjust them as necessary before supplying them to employees.
- HR Employee Survey Analysis: Because AI is optimized to find patterns in data, it can help HR teams by analyzing employee survey information and creating engagement strategies. It can identify trends within organizations, compare them to any existing data about the significance of those trends (such as employee satisfaction or engagement), and suggest actions to take.
- Address Employee Turnover: By analyzing historical employee data, HR teams can identify patterns and factors contributing to employee turnover. Kingfisher, an international home improvement company based in London, used criteria modeling with Alteryx to predict which employees were likely to leave, and what reasons they likely had for leaving.
Legal Departments
- Legal Document Automation: Writing legal documents takes time, and the mental fatigue that comes with the process can lead to one misplaced comma or the use of the wrong word. AI can speed up legal document creation by suggesting relevant clauses. While suggestions should always be reviewed and edited before approval, AI removes the time-consuming work of generating a first draft, leaving teams with time and energy to apply knowledge and expertise to ensure everything is in order.
- Legal Data Summarization: Generative AI can help review legal documents, too. It can extract insights from the text, analyze them, and present key findings as concise summaries. Again, legal documents should always have people reviewing the work to ensure it’s all accurate, but AI can save a significant amount of time, especially when it takes over repetitive processes.
Retail
- Inventory Forecasting: Understanding the habits and connections between products and external factors takes time. AI and machine learning can aid teams by making predictive inventory suggestions after setting up the framework to guide it. AI can then do the heavy lifting of automatically forecasting demand.
- Customer Segmentation: When customers receive personalized content, they’re more likely to buy products and report higher customer satisfaction. AI can improve the value of campaigns and help customers get more relevant recommendations. Generative AI can assist by recommending products, creating marketing copy, and suggesting product recommendations based on existing data.
Consulting
- Consulting Data Analysis: When organizations spend money on consulting services, they expect their consultants to understand their business and how it operates. Generative AI can help consultants provide beneficial experiences to clients by enhancing the quality of consulting reports and generating customized insights.
The critical role of analysts
While generative AI offers exciting possibilities, its implementation and management require human expertise to mitigate risks and ensure success. Generative AI tools lack critical thinking and strategic planning skills; this is where analysts become crucial players. Let’s delve into the critical areas where Analytics Champions can make a difference.
Understanding Nuance: While generative AI thrives in recognizing patterns, it encounters difficulties with logic and reasoning. Analysts play a pivotal role in bridging this disparity by deciphering the AI’s output and applying judgment to real-world situations.
Navigating Regulatory and Compliance Landscape: Analysts navigate the regulatory and compliance landscape, ensuring that the use of generative AI aligns with local, national, and global regulations. Data usage, rights, and adherence to industry standards are accessed to mitigate regulatory risks.
Preserving Privacy: Analysts evaluate data sources to ensure compliance with privacy regulations. They assess data suitability for training, including its sourcing and permissible usage, to safeguard data privacy.
Maintaining a Competitive Edge: The availability of generative AI presents both opportunities and challenges. While these tools offer transformative potential, your competitors have access to the same technology. Analysts and human inputs are required to build competitive outcomes when leveraging generative AI.
Enhancing Security Measures: Analysts collaborate with cybersecurity experts to fortify security measures surrounding generative AI implementation. They assess data storage, usage, and platform security to mitigate risks associated with data breaches and misuse.
Ensuring Effective Governance: Analysts establish robust governance frameworks to ensure the accuracy and reliability of generative AI results. They implement safeguards against potential issues like hallucinations, ensuring trustworthy outcomes from AI tools.
Addressing Scalability Needs: Analysts are critical in evaluating the scalability of their analytics architecture. They assess if the generative AI tools can handle an organization’s growing data demands. Analysts can design workflows to integrate generative AI within their existing architecture.
In essence, analysts serve as the linchpin in navigating the complexities of generative AI implementation and management, leveraging their expertise to unlock its full potential and mitigate its risks.
How to get started
Alteryx, the AI Platform for Enterprise Analytics, leverages automation and trusted AI to streamline data processes. Analysts gain valuable time to focus on core tasks and strategic thinking, ultimately driving better business outcomes.
Alteryx offers multiple AI solutions designed to transform how you utilize data for better decision-making:
- Alteryx Auto Insights focuses on automating data exploration and insight generation. By combining no-code dashboards with machine learning, Auto Insights surfaces trends, explains the “why” behind the data, and delivers actionable intelligence in minutes. This empowers anyone, regardless of technical expertise, to gain valuable knowledge from data and make data-driven decisions faster. Try the Auto Insights Simulation to see an AI-enhanced, personalized demo.
- Alteryx AiDIN expands on insights by leveraging generative AI for generating presentations, reports, and even workflow summaries. This translates to faster time-to-value, streamlined operations, and improved innovation. AiDIN’s Magic Documents and Workflow Summary Tool are key features that automate report generation and workflow documentation, saving analysts valuable time.