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This is blog is foundational to a series of blogs to come. I will highlight what to expect as an organization when you decide to execute your first AI project, from the perspective of Program Management and Service Delivery. This blog provides general information and not a simple "how-to" guide.

Artificial intelligence (AI) has changed the way we work, as an organization, how do we prepare ourselves to execute first AI project successfully? I hear you! It's just another project and why can't we use the existing project management methodologies? It's just another new technology and we can run an AI project as we run any other project in our organization!

Although most of conventional Program/Project Management techniques are still applicable, organizations need to think outside the box

Align:

  • AI is not a robot in your office
  • It’s not planned to replace existing employees, but allows them to concentrate on high value tasks
  • Work with stakeholders to create an AI project, to assist, help identify the value creation
  • Prepare the organization for adoption by empowering your team to be AI ready (as most of the staff are unaware of AI, NLP, ML, Hybrid SaaS, etc.)

Define:

  • Initiate the AI project with clear definition by identifying a 'Use Case' (AI vendor's preferred term for scope)
  • Companies at different levels of data science maturity have different objectives, expectations, experience and resources
  • Depending on your data science background, determine your AI project objectives
  • Document the scope of your project, which defines the 'Use Case'

Prepare:

  • Explore how to use data effectively to help grow your business rapidly
  • Discover new opportunities faster and support business development
  • Define if you need to build your own data science team or
  • Identify a partner who will assist you in your AI journey
While preparing to start your AI project, you need to determine the best approach for delivering it. So first, define the basic assumptions underlying your project.

Execute:

  • Will your AI project deliver a critical differentiator for your business?
  • What is more important – cost and time or the differentiation?
  • Do you have in-house capabilities to run the project (Technical, Data science, Natural Language Processing, etc.)?
  • As any project, you can either run the project in-house or outsource depends on some of the following factors:

   In-house if:  

  • Your team members have appropriate competencies
  • Your team is ready to take on a new challenge (Availability, readiness, ability, etc.)
  • Time is not that crucial (Running a pilot AI Project)

    Outsource if:

  • You are in initial stages of adopting AI, your team doesn't have appropriate competencies for the project  
  • Your team is not ready or early stages of adoption
  • Time is crucial and lack of resources available to run an AI project
  • You need a high level of customization, quick wins

Final words:

  • Form and engage an internal AI team to work with your preferred external AI partner
  • Keep it lean (Try to use agile methodology to run your AI project)
  • Keep in mind that things will keep changing
  • Have regular meetings with internal / external AI teams
  • Prepare a communication plan (internal, external, investors, key stakeholders, etc.)

For your company to be great at AI,  you must have:

  • Resourcing - Prepare your resources to think and execute AI projects
  • AI understanding (read, talk to people, watch videos, etc.)
  • Implement a process to identify ‘value adds’
  • Engage stakeholders pass on enough knowledge to them about AI
  • Company wide AI Strategy even if you start with small pilot
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