Develop an AI strategy

by

 Develop an AI strategy

Develop an AI strategy

Some common ways to use AI

For developing an AI strategy always remember some common ways to use
  • Developing more intelligent products
  • Developing more intelligent services
  • Making business processes smarter
  • Automating repetitive business tasks
  • Automating manufacturing processes 

Start up the AI strategy

  • Leverage AI to make a plus specific to your company
  • Design strategy that align with virtuous cycle of AI
  • Blue River – precision agriculture
  •  AI must be specialized or verticalized to your industry sector
  • Don’t compete with giants Creating a strategy
  • Strategic data acquisition
  • Unified data warehouse – Pull data into single repository, software can
  • Create network effect and platform advantages Uber, Careem, Facebook
    Low cost strategy
  • Low cost strategy
  • High value strategy

 Develop internal and external communications

  • Investor relations 
  • Government relations 
  • Consumer / user education 
  • Talent / recruitment 
  • Internal communication

 AI teams

  • AI teams may have hundreds of engineers.
  • A small team can have four or five members . 
  • Example role: Soft ware Engineer (Execute joke, set timer)
  • Machine learning Engineer
  • Machine learning researcher (Extend state-of-the-art, applied Machine learning scientist in between  machine learning researcher and machine learning Engineer)
  • Data Scientist (Provide insights, make presentation to team/executives)
  • Data Engineer (Organize data, data is saved in cost effective way, we have lot of data,scalability is important)
  • AI product Manager ( Help decide what to build; what’s feasible and valuable).

Always Keep in Mind

  • Data privacy will be key consideration during adopting the AI strategy.
  • Make sure that AI is free of bias and discrimination.
  • AI should be use for the benefit of a company,customers and its employees.
  • Never adopt the adverse uses of AI.

 Common Pitfalls

Don’t

  • Expect AI to solve everything
  • Hire 2-3 ML engineers and count solely on them to return up with use cases..
  • Expect the AI project to figure the primary time.
  • Expect traditional planning process to apply without changes.
  • Think you would like superstar AI engineers before you’ll do anything.

Do 

  • Be realistic about what AI can and can not do given limitations of technology, data, and engineering resources.
  • Pair engineering talent with business talent and work cross functionally to seek out feasible and val.
  • Plan for AI development to be on iterative process with multiple attempts needed to succeed.
  • Work with AI team to determine timeline estimates, milestones, KPIs,etc. 

Take your first step

  • Get friends to learn about AI
  • Start brain storming projects
  • Hire a couple of machine learning/data science people to assist ..
  • Hire/appoint an AI leader (Vice President AI, Chief AI Officer)
  • Discuss with CEO about possibilities of AI transformation

Get started with a small team

  • One software Engineer
  • One machine learning/ Data Scientist
  • Nobody but yourself

Building AI projects:

  • How to choose an AI project?
  • Must keep in mind the AI knowledge and domain knowledge.
  • Brain storming frame works means group discussion to produce ideas or solve problems.
  • Automate tasks rather than job
  • Main drivers of business value
  • What are the pain points in your business

Is it always necessary to have big data?

  • Having more data is good 
  • With small data sets we can make progress
  • 10,100 or 1000 data points can be a good start

Technical diligence

  • Can AI team/system meet desired performance
  • How much data is needed
  • Engineering timeline

Business diligence

  • Lower cost
  • Increase revenue
  • Launch new product or business

Build Vs Buy

  • Machine learning projects can be housed or out sourced.
  • Data Science projects are generally in housed.
  • Buy industry standard, only build specialized products.

Working with an AI team

  • Specify your acceptance criteria (Goal: detect defects with 95% accuracy)
  • Training, validation and test data set
  • Don’t expect 100%  accuracy (Limitations of machine learning, insufficient data, mislabeled data, ambiguous labels

AI technical Tools

  • Machine learning frame works for writing software ( Tensor flow, Pytorch, Keras, MX Net, CN TK Caffe, Paddle Paddle, SciKit-learn, R, Weka)
  • Research publications (Arxiv)
  • Open Source Repositories (Git hub)

Leave a Comment