You will run into multiple problems trying to start an AI solution on a generic corporate network. These include I/O limitations, data models too large to process, and processing that’s too slow to generate real value. Come and start the AI discussion for your business. Talk to our team at App Maisters to learn how!

1. Implementing AI – What You
Need to Know Beforehand

2. AI implementation is rising as the technology offers greater value to businesses
than ever before.
In fact, a study by Narrative Science shows that AI implementation has grown by
60% over the year 2017. The fact is that the majority of businesses are now using
some form of AI – often in the form of off-the-shelf IT automation software.
However, not all businesses are seeing the returns they expect out of AI. In fact,
according to the same report, less than one-third of respondents are even tracking
the ROI of their AI investments.
Introduction

3. This pattern is likely to continue, and it falls on executives and business decision-
makers to plan their strategy for implementing AI in a value-oriented way.
Introduction (Continued)

4. There is a great deal of misinformation and hype surrounding the capabilities of AI
in the workplace. This leads to a situation where company executives will explore
AI options based on rumors and hearsay. Typically, the situation runs something
like this:
Executives get excited about the potential profitability of implementing AI, and will
find someone in the company – usually in the IT department – who is somewhat
knowledgeable about AI and enthusiastic to try it. Then, they create a small team to
explore possibilities and opportunities.
Where Companies Go Wrong When Implementing AI

5. In the end, the most enthusiastic AI evangelist will dominate the discussion and
point the company in a good direction, but lack the experience, tools, and
components to implement the solution successfully. The truth is that you can’t
implement an AI solution on just any infrastructure – you need a complete
hardware and software solution that addresses the unique challenges AI
represents.
Where Companies Go Wrong When Implementing AI
(Continued)

6. You will run into multiple problems trying to start an AI solution on a generic
corporate network. These include I/O limitations, data models too large to process,
and processing that’s too slow to generate real value.
Fortunately, there are some easy ways to address infrastructural concerns when
considering AI. The following five rules should guide the AI discussion from start to
finish:
● Use public cloud services instead of in-house network infrastructure so that
your AI solution is scalable.
Avoid Trial-and-Error - Go Directly for a Scalable AI
Solution

7. ● Build your team around the requirements of your AI project – not the other
around.
● Bring in experienced AI consultants who have worked in your field before.
● Talk to your current software vendors and find out if any of your applications
are already AI-enabled.
● Structure your project in tiers. For instance, prioritize prediction-level results
over full automation in the first phase, then scale that solution before moving to
the next phase.
Avoid Trial-and-Error - Go Directly for a Scalable AI
Solution (Continued)

8. If you open your AI project with these guidelines in mind, you will be better-
positioned to generate value while reducing wasteful trial-and-error exercises
throughout the entire project. These rules will help you remain focused on the
tangible values that AI can generate for your company.
Ready to start the AI discussion for your business? Talk to our team at App
Maisters to learn how!
Avoid Trial-and-Error - Go Directly for a Scalable AI
Solution (Continued)

9. Thank You