Prepare Your Data for AI

In this workshop, Shinydocs CEO Jason Cassidy will guides panelists in real-time through the steps required to prepare your data for your AI outcomes. You will follow along firsthand as panelists demonstrate their results each step of the way.

Watch the Webinar

Learn How To Prepare your Content for AI:
Start to finish in 60 minutes

Q: Why is it important for organizations to prepare their data for AI?

- Improved decision-making: If trained properly, AI models can analyze complex patterns and provide insights that can aid in decision-making. - Operational Efficiency: AI-driven automation can streamline operational processes, reducing the amount of human capital spent on mundane tasks and improving workflow efficiency - Unleashing Creativity and Innovation: If our employees can spend less time sifting through documents, they will have more time coming up with new ideas that keep businesses ahead of the competition - Predictive Analytics: AI models can help anticipate industry trends, customer behaviors, market shifts, allowing for proactive strategies rather than reactive responses - Security and Privacy: Training AI models can help to detect potential security breaches and protect sensitive information, maintaining consumer trust.

Q: How can your organization prepare your new AI model?

- Find The Information and Get It Ready: Most organizations have content and data saved across various platforms, channels and applications. During this stage your organization must understand what and where your data is on your various systems. - Analyze: During this stage it is important to analyze the data, to find and remove duplicates, clean the data, remove low business value content, and flag any PII or privacy concerns. - Tag the Data For Classification: Finally to prepare it for the new AI model the organization has to create an input model with tagged keywords to teach the AI to memorize what the data is about.

The Easy First Steps
Learn the immediate actions you can take today to collect and train your data to drive AI outcomes.
Strategies for AI Ethics Compliance
Have complete confidence in your AI outcomes by understanding and managing your data inputs.
Become an Expert, not a Spectator
Get useful tips, techniques, and best practices to stay ahead of the curve and remain competitive in the AI landscape.

Preparing Your Data for AI
Understand the critical role of the information manager to prepare data and information for various AI outcomes.
Use AI to find, classify, and respond
Create valuable outcomes such as finding data within content, classifying files, and generating answers to questions.

YOUR HOST

Jason Cassidy
Shinydocs

“With rapid advancements in AI technology, you don’t need to observe from the sidelines and speculate. You gain an advantage by rolling-up your sleeves and jumping right in. Building your first useful AI models is easier than you think.“

Don’t miss this opportunity to learn and interact with like-minded professionals.

Reserve your spot today.

Receive The Recording

FAQ – Preparing Your Data for AI

Why is it important for organizations to prepare their data for AI?

– Improved decision-making: If trained properly, AI models can analyze complex patterns and provide insights that can aid in decision-making.
– Operational Efficiency: AI-driven automation can streamline operational processes, reducing the amount of human capital spent on mundane tasks and improving workflow efficiency
– Unleashing Creativity and Innovation: If our employees can spend less time sifting through documents, they will have more time coming up with new ideas that keep businesses ahead of the competition
– Predictive Analytics: AI models can help anticipate industry trends, customer behaviors, market shifts, allowing for proactive strategies rather than reactive responses
– Security and Privacy: Training AI models can help to detect potential security breaches and protect sensitive information, maintaining consumer trust.

How can your organization prepare your new AI model?

– Find The Information and Get It Ready: Most organizations have content and data saved across various platforms, channels and applications. During this stage your organization must understand what and where your data is on your various systems.
– Analyze: During this stage it is important to analyze the data, to find and remove duplicates, clean the data, remove low business value content, and flag any PII or privacy concerns.
– Tag the Data For Classification: Finally to prepare it for the new AI model the organization has to create an input model with tagged keywords to teach the AI to memorize what the data is about.

Is it possible for an organization’s IT Team to tackle this process alone?

Organizations today are inundated with vast amounts of data, which comes from various sources. The volume, velocity and variety of data generated and collected by organizations has grown exponentially as a result of advancements in technology, increased digitization of business processes and the proliferation of connected devices. The data comes in both structured (databases, spreadsheets) and unstructured form (text, documents, images, videos), making it difficult for organizations to make sense of it all. It has become impossible for IT teams to manage this data in a way that will help organizations reach their business objectives. Organizations need to explore solutions that will assist IT staff in document management, freeing them up for more creative and strategic projects.

Can an AI Model make decisions for your organization?

If the data is prepared and trained properly, an AI model can help anticipate industry trends, customer behaviors, understand complex patterns, all allowing for proactive strategies rather than reactive responses. AI models, particularly machine learning models, are designed to analyze data, identify patterns and make predictions or recommendations based on the information provided to them. However, there are several factors that organizations need to consider when relying on AI models for decision-making: accuracy and reliability; how easy its decisions are to explain; ethical and legal considerations; and the need for human oversight and intervention.

Risk vs. Reward: What should I consider before opening up my data to a third party vendor?

When opening up your data repositories to third party vendors, there are things that need to be considered. Organizations should assess the vendor’s data security measures, including encryption, access controls and data breach response protocols to ensure they comply with relevant privacy regulations. Researching the vendor’s reputation, track record and reliability is also important, as well as clearly defining how your data will be used and the level of control you maintain over it.

As an organization’s amount of data and information continues to grow, third-party vendors with specialized data management skills and technology will help to bring expertise, experience and best practices to the table, optimizing an organization’s processes and improving outcomes. Vendors have a specialized understanding of risk management, data analytics platforms and industry insights, providing scalable solutions without the overhead costs of hiring and training additional staff.

Learn How To Prepare your Content for AI: Start to finish in 60 minutes

Jason CassidyShinydocs

FAQ – Preparing Your Data for AI

Learn How To Prepare your Content for AI:
Start to finish in 60 minutes

Jason Cassidy
Shinydocs