THE FRAMEWORK
Proven Approaches To AI Implementation
Strategy
AI Strategy designed to your needs
Understand the impact of a well designed strategy on your AI implementation
Strategy design
Strategy Implementation
People
Get The Best People
What are the building blocks to recruit and retain the best people?
Find the people
Get the people
Technology
Optimize your technology
Is your tech stack optimized for AI and are you utilizing best practices for deployment?
Making the right choices
Architecting for the future
Data
Data is the backbone of all AI insights
To ensure you are able to scale your efforts you need a supporting data infrastructure in place
Secure & Compliant
Federated and accessible
Operating Model
Building the organization to enable AI best practise processes
Have you set up the organisation and processes you need to onboard and retain the right people. Do you know how to properly screen and scale?
fit for purpose
Processes & guidelines
Mastery
Master the next level of your AI implementation
The differentiating factor in AI implementation that goes beyond the other parts of The Framework.
Next Level expertise
Beyond the framework
LATEST ARTICLE ABOUT STRATEGY
Strategy Completeness
Do you know what is the most important part of your strategy? The answer might surprise you. The most important part of any strategy is not its tactics or even it’s vision, but instead completeness. Strategy completeness is about making sure that everything needed to execute a plan exists and has been accounted for. It ensures that there are no gaps in talent, resources, time, or other key pieces necessary to execute on the strategy. Making strategic decisions without considering strategic completeness can lead to execution failure because plans will fail when they lack one of these components. This blog post teaches how to create a complete plan by answering three questions: What should be included in my planning process? How do I design an effective plan? And finally, how do I
LATEST ARTICLE ABOUT DATA
Most affordable data source
It’s not a secret that data is one of the most important assets for any organization. Data analytics and insights are essential to every decision-making process, from strategy development to product design. We all know that data has become even more critical as we move into the era of AI and machine learning (ML). Organizations need more than ever accurate and comprehensive data in order to stay competitive with their technologies. However, businesses are struggling with how much they should invest in capturing this valuable resource when there are so many other areas where resources could be allocated. So what does an organization do when it comes to deciding on whether or not they should invest in collecting higher quality data? The answer lies in looking at the ROI potential of investing in high quality data