Quantify the potential value of your AI investments

  • Know the value drivers including revenue growths, cost savings and improved risk posture.
  • Transform customer experience, streamline operations and detect fraud by augmenting human expertise.
  • Generate metrics that quantify exactly what AI provides, including expected ROI, payback period and net present value (NPV).

Use the Business Value Assessment to uncover the relative magnitude of value you can extract from investing in data science and AI.


Take the assessment now

Build your case for the business value of investing in AI

By assessing the value of AI for your business, you build a business case for investing in machine learning and optimization techniques to drive business goals. The interactive tool helps determine financial benefits and ways to achieve your objectives by analyzing your best practices and details about your business, industry and IT environment.

Use the results to

  • Gain consensus within your organization
  • Document proof for costs and project benefits
  • Measure progress of your enterprises's initiatives

Learn how IBM data science and artificial intelligence solutions can help your business modernize operations.

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Industries

How can a Business Value Assessment help my industry specifically?

IBM provides data science and AI solutions for a wide variety of verticals. The following summaries explain value drivers (e.g. revenues, costs and risk management) that these solutions can drive for specific industries.

Take the assessment

Solving customer experience challenges provides a clear entry point for seeing the value of AI. Provide the following details see what AI can do for you in this area.

Reduce time to provide a best offer or complete transactions

Baseline number of transactions per day (current operational limit)

Average net value (profit) per transaction

Potential value with reduced transaction time using IBM AI solutions (%)

Value: 30

Predict member behavior and increase customer retention

Current churn rate (%)

Potential improvement in retention using IBM AI solutions (%)

Value: 5

Decrease call center agent response time

# of full-time equivalents (FTEs)

Hours saved annually (per FTE)

Value: 20

Tell me more ...What are your business pains? Click all that apply

How many data scientists are there as well?

What technical drivers do you believe can help you get more value from the data science tools?

Which capabilities are most important to your data science operation?

Differentiating Capabilities

Low

Med

High
Data preparation
Data visualization
Model development
Model management
Model deployment
Retraining
Model Visibility and explainability
Productivity and automation
Decision optimization
Visual Recognition and Natural Language Classifier

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Your Customized Assessment Results

Thanks for providing your input. Based on your responses, we recommend you consider Watson Studio CloudWatson Studio LocalWatson Studio Premium-Add on for IBM Cloud Pak for Data.


In addition, we have customized recommendations for you as shown below.

High

Data Preparation

If you need end-to-end data preparation including data collection, organization and transformation you can use Data Refinery from Watson Studio Premium Add-on to IBM Cloud Pak for Data.

Data Visualization

If you need visualizations with interactive tables and charts to help you understand patterns and improve a predictive analytics model, you can look at Data Refinery and SPSS Modeler in Watson Studio Premium Add-on to IBM Cloud Pak for Data.

Model Development

You plan to build predictive models into modern software architecture.  You can use Watson Studio Premium Add-on for IBM Cloud Pak for Data, which provides functionality to build micro-service-based models for your apps.

Model Management

With Watson Machine Learning you can compare a collection of up to 10 machine learning model versions at the same time. Over time, the model versions can be evaluated against each other to assess the best ones to keep on your production environment.

Model Deployment

With Watson Machine Learning you can redeploy projects without changing the URL and you can assign or configure workers for your projects.
If you want to develop deep learning models leveraging GPUs consider the Watson Machine Learning Accelerator and upgrade training infrastructure.

Retraining

You can automate retraining of models along with model groups to keep your best models on your production environment. Watson Machine Learning can pull in the latest assets from the source project and update the deployments with them.

Model Visibility and Explainability

You can use OpenScale for IBM Cloud Pak for Data to ensure AI models are free from bias,  explained and understood by business users, and are auditable in business transactions.

Productivity and Automation

The AutoAI tool in Watson Studio automatically analyzes your data and generates candidate model pipelines.  These model pipelines are created over time as AutoAI algorithms learn more about your dataset and discover data transformations, estimator algorithms, and parameter settings.

Decision Optimization

With Decision Optimization included in Watson Studio Premium Add-on for IBM Cloud Pak for Data, you can access solution engines for mathematical and constraint programming, create and edit models in Python, create models with natural language expressions and import OPL models.

Visual Recognition and Natural Language Classifier

For tools to classify text and image data including deep learning libraries you can consider Watson Machine Learning Accelerator and IBM Power Systems servers.

Medium

Data Preparation

If you have needs to fix data that is incorrect or incomplete or you need to filter, sort, or combine columns, you can use Watson Studio data refinery capabilities.

Data Visualization

With Watson Studio you can shape and visualize data in a few clicks.  You can learn more on this data refinery tutorial from shaping to profiling and visualizing data.

Model Development

Watson Studio provides tools to create, train and evaluate models that can be deployed in your private clouds, Azure or AWS. Users can also deploy their models with Watson Machine Learning.

Model Management

The Watson Machine Learning dashboard provides an overview of a project release including model metrics, top requests, and most recent job runs.

Model Deployment

With Watson Machine Learning you can deploy assets as a web service, an app or a job. You can also redeploy assets when necessary.

Retraining

You can schedule scripts to run at regular intervals in Watson Machine Learning to retrain and evaluate models.

Model Visibility and Explainability

Watson OpenScale provides tools to trace and audit predictions made in production applications explaining factors contributing to final outcomes in understandable business.

Productivity and Automation

The NeuNetS tool in Watson Studio synthesizes a neural network and trains it on your training data without having to design or build anything by hand.  With the neural network modeler you can create a neural network design flow by using the deep learning nodes.

Decision Optimization

You can run optimization models in conjunction with predictive results from machine learning with. Decision Optimization for Watson Studio.

Visual Recognition and Natural Language Classifier

With the associated Watson Services, you can use visual recognition  and natural language classifier to build apps. while extracting meanings and perform queries against images and texts.

Low

Data Preparation

You can start with the base data preparation capabilities of Watson Studio. As your needs grow you can start to look at more advanced data preparation capabilities.

Data Visualization

Many of your favorite open source visualization libraries, such as matplotlib, are pre-installed in Watson Studio. You can visualize data and explore from various perspectives.

Model Development

You can get started with building models with Watson Studio. You can explore pre-trained models and sample data in the Watson Studio community.

Model Management

With Watson Machine Learning, you can start, stop and view the status of models in your project.

Model Deployment

Watson Machine Learning can help you deploy your projects into production.

Retraining

You can get started by evaluating model metrics in Watson Machine learning  and ensure continuous learning including monitoring of model performance, retraining, and redeployment to ensure prediction quality.

Model Visibility and Explainability

You can use the tools available from Watson Studio and Watson Machine Learning to provide visibility and explainability of your models.

Productivity and Automation

SPSS Modeler provides a node palette for drag and drop creation or editing of flows to quickly create models.

Decision Optimization

You can run multiple sets of input and compare results manually with Watson Studio and review the decision optimization concept.

Visual Recognition and Natural Language Classifier

You can perform Visual Recognition and Natural Language Classification in Watson Studio, which use the pre-trained models with Watson Services.

Free one-on-one consultation

Have a machine learning use case? Would you like to estimate the value of your use case with this business value assessment tool? IBM can help you quantify the value of deploying AI into your business. Get one on one time with IBM’s AI and machine learning expert to discuss your ideas and needs.

Schedule consultation now

Learn More

Visit Watson Studio and Watson Machine Learning sites.

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