Implementing Atlas AI - Discovery & Scenarios

Atlas AI leverages new technology. Although GPT models have been around for some years now, to say both parties are on the very cutting edge of implementing Generative AI is a correct and factual statement.

To run any project properly in a diligent manner, specific processes should be created and followed based on best practice and experience. AI is no different.

ClearPeople have been working in this sector for over two decades, however the new AI technologies present new opportunities for us and our clients - both parties are tied to it success. As such, we have utilized our collective knowledge and experience to establish a process for implementing our AI tools in Atlas, most of which focusses on discovery and education. A lot of this process is driven through collaborative workshops, a key output being documentation to baseline the expected Scenarios (use cases) you are expected to take value from when setting up and interacting with AI. But we also need to cover training, governance, and any unique policies or protocols your organization may have in place related to both AI or content

The way we are presenting the implementation of Atlas AI is through a Pilot to help ensure we are planning for success and validating accordingly, prior to pushing the Atlas AI toolset out to end-users.

This article will discuss the implementation process of Atlas AI in 3 sections:

• Implementation Process, High-level plan & key workshops
• Discovery process in more detail (business workstream)
• Technical Deployment process in more detail (technical workstream)

Implementation Process & High-level Plan

The implementation process for Atlas AI toolset is split into 3 iterative phases: Discovery. Experience & Confirm. Alongside the technical workstream to deploy Atlas AI and Microsoft Open AI

High-level Plan

Please note this is a high-level indicative plan. The necessary timeframe may need to be extended or stretched based on how prepared you are for both the technical and business workstream

Week 1

• Kick-off (1 hour)
• Planning Session (1 hour) - both to prepare for the project but also the wider use of AI
• Optional Atlas AI demo & Art of the possible (1 hour) 
• Start technical deployment of AI tooling and infrastructure (ongoing)

Week 2

• Discovery Session (90 minutes)
• Continue technical deployment (ongoing)

Week 3

• Playback session to verify and validate scenarios (60 minutes)
• Validate technical deployment (ongoing) - signals the end of Discovery phase
• Atlas AI Admin training 1 - introduction, set-up, permissions & governance - signals start of Experience phase

Week 4

• AI Set-up & configuration against confirmed scenarios
• Configuration and set-up of any necessary Atlas workspaces and AI-ready content
• Atlas AI Admin training 2 - How to use the Atlas AI Assistant and Intelligent Knowledge Studio (IKS)

Week 5 onwards

• Ongoing touchpoints to set-up needed scenarios, training, Q&A, assessments and validation
• Feedback and alterations
• Approval of existing scenarios that have been solutionised and are working in Atlas AI
• User Training
• User testing
• Feedback & amends
• Internal governance plan, including technical and business ownership and administration
• Internal communication plan
• Roll-out to end-users

Pre-requisites:

• Deployment of AI and basic set-up with permissions so that your stakeholders have the necessary level of access
• Scenarios need to be discussed, agreed, and baselined. This will provide direction for what we set-up and how
• Content being available. Content will need to be available inside Atlas workspaces for the AI to index.
• Understanding AI GPT models - this is not Atlas technology and therefore the settings and configuration are generic. A knowledge of these is beneficial for the project but will be essential for technical ownership and administration
• Ownership, both from technical and business side

Discovery Process in more detail

There is supplementary material for this discovery process which you will receive, mainly in PowerPoint slides and Excel spreadsheets. This provides more detail, however it is useful for us to spell out this process below at a high-level so you can plan accordingly.

• Planning Session - Discussion on what is needed and when, and who is responsible for what. Usually working with Project Manager and Atlas and/or AI owners, as well as technical owner.

  • 2x discussions:

    • • AI planning. Overview of discovery and necessary documentation on Scenarios, presenting high-level questions such as: What kind of content are you looking to bring into AI and how do you expect users to leverage this content? We aim to understand what your expectations are and then to plan accordingly. We will use this time to address any risks or concerns, and ensure we all leave feeling more confident in the AI implementation.
      • Technical deployment. Discuss and baseline plan for deployment of Azure OpenAI and Atlas AI code and infrastructure.

  • Any other questions you would like to ask and overview of any requirements already known.

• Discovery Session - to understand potential scenarios you are looking to achieve with AI and to confirm configuration options available. 
  
  • • Time to discuss the specific Scenarios and requirements in more detail. Evaluating feasibility and value as well as how to deliver against the use case scenarios in more detail. Inc. planning of content and architecture as well as business ownership
    • We aim to start documenting the scenarios (Scenarios) in a specific format - shown in the screenshot below
    • Prioritise and agree outcomes via the Scenarios. Which Scenarios will be targeted and how.

• Playback session - to verify and validate Scenarios
  • Play back and review documentation for any clarifications needed, and to reach agreement on how we reach and plan for each use case to help ensure a positive outcome in the agreed timescale.
  • Discussion on success criteria and how to assess value

• Training Sessions - to introduce Atlas AI Assistant and the Atlas Intelligent Knowledge Studio to the business and technical users for this AI pilot workstream
  • • Atlas AI Admin training 1 - introduction, set-up, permissions & governance.
    • Atlas AI Admin training 2 - How to use the Atlas AI Assistant and Intelligent Knowledge Studio (IKS)
    • Atlas AI Admin training 3 - introduction and run through of configured Knowledge Collections and Scenarios in play

Technical Deployment Process in more detail

• This process can be run in parallel to the above Discovery (Business) process
• The technical deployment of Atlas AI is dependent on following our Atlas Runbooks. If you are an existing client you should be well aware of this process. If you are a new client and are yet to deploy Atlas or Atlas AI tooling, we will support you through this process.

• The Atlas Runbooks have specific sections on AI. These will need to be followed and the tooling deployed and set-up accordingly. The Runbook will ensure the Atlas code and infrastructure necessary for the AI is set-up and available. Your designated Atlas lead (not necessarily AI workstream lead) will provide you the appropriate information in good time.

• There is a dependency for AI to deploy the necessary Microsoft Azure infrastructure in your 365 tenant. This is primarily in relation to the Azure Open AI service. Please follow these article below. These can be started anytime as they run independently to the Atlas deployment and set-up.

• • Atlas AI - Azure Step 1: Review access to Azure OpenAI in your Azure subscription and request access to Microsoft (Atlas 6.0+)
  • Atlas AI - Azure Step 2: Compatible regions for the Azure Open AI service (Atlas 6.0
  • Atlas AI - Azure Step 3: Manual creation of Azure OpenAI in your subscription (Atlas 6.0+)

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