Framework

Our process starts by learning about the customer problems that you are trying to solve— and how AI could help solve them.

We engage executive decision makers, product and domain experts, data scientists, data engineers, software engineers, and machine learning ops teams to arrive at your AI strategy.

We also gauge how ready your company is to embrace AI technologies, and how valuable your data set is today to power your AI strategy.

Assess your team, product and data for AI-readiness.

Define the business problem to be solved with AI and its value
Identify the best use cases for applying AI
Define prediction variables and AI features
Create the AI data strategy
Review the latest solutions and literature
Determine the model and training approach
Experiment to find the signal in the data

Set your AI objectives and dig into your data to determine its value for AI.

Scope ability for AI/ML to enhance product roadmap ​
Look for measurable signal value in data sets  
Validate clean and ready-to-go training data for experimentation
Determine metrics needed to achieve success using AI/ML Investigate new techniques to consider for use cases Understand current frameworks, algorithms, and models

Refine your product strategy and align it to your category.

Sync your product to your new category and vision
Study metrics, KPIs, market and competition
Evaluate current sources of data vs alternatives  
Investigate optimal approaches: build, buy or reuse technologies

Deliver enhanced product value with AI.

Build the AI MVP into your product or service
Deliver your AI-enhanced product in sync with the new category vision
Develop a plan to build out your AI team expertise
Scope your AI responsibility and risk management

Learn  about aligning product and AI strategy to drive growth, revenue & value.

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