Rewrite with 0% plagiarism as a tech journalist for Forbes of all the sections in the passage in less than 200 words, "Most common AI implementation mistakes: 
It is essential to adopt AI, but it is also equally important to do it rightly. Here are a few common mistakes that businesses need to avoid:

Unclear goals and KPIs: 
Starting without a clear understanding of the business goals is a sure-shot way of getting confused along the AI adoption process. Having defined KPIs that you can measure and clear, measurable, and achievable goals is necessary to define the project’s scope and calculate its impact on the business.
Here are a few questions that you must ask before starting:
What real-life challenge are you trying to solve?
Have you foreseen how the end product will look post-AI implementation?
How long will it take to get ROI?
What is the expected cost of the project?

It is always advised to get an expert consultation if you are unclear about the details. Visualizing the end product and its business impact can lead to a clear product roadmap with the least resource wastage.
Starting late:
A matured architecture may not be well suited to AI. This may lead to spending a good amount of resources to manage arising tech issues during implementation. The AI algorithms built on such architecture may result in substandard results or complete failures.
On the other hand, you can build AI algorithms easier, cheaper, and faster if you start early. It is much easier to plan and add AI capabilities to future product feature rollouts.
Isolated POCs:
Another common mistake is to focus on standalone proofs-of-concept. These POCs work perfectly in a stable test environment where the data is controlled but can fail in a natural production environment where the information is unpredictable.
So focus should be on production-ready POCs. A quick POC that doesn’t last more than two months would be worth the trial to bring confidence. It is advisable not to be aggressive at this stage, as AI problems take a toll on parameter tuning, resource optimization, and performance.
Insufficient data:
There is a strong correlation between the success of the AI implementation and the quantity of quality data pipeline used for model training and improvement. Without a proper data pipeline, AI models cannot generate accurate results.
A data generation mechanism is required to build a healthy pipeline if a proper data pipeline is unavailable.
Insufficient skills:
AI implementation requires considerable investment in the team. The team typically consists of data engineers, data scientists & domain experts to build good mathematical algorithms. These algorithms are translated into software solutions by product development teams.

Lack of experience or absence of the right talent in the team is one of the most common reasons for AI implementation failures, and it is also the major cost factor in the entire project budget.

It is advisable to have a team where data scientists can easily connect with the product engineering teams to avoid confusion in interpreting mathematical algorithms and speed up the implementation process.

Tools & Platforms used in AI implementation 
Here, we listed down some of the primary tools and frameworks you can leverage to implement AI in your business. 

Scikit Learn
TensorFlow
Theano
Caffe
MxNet
Keras
PyTorch
CNTK
Auto ML
OpenNN
H20: Open Source AI Platform
Google ML Kit
Final Words 
AI is transforming almost all sectors, and various fast-growing tech companies and enterprises are implementing it to power their products and services with intelligent computational power of AI. This article has tried to explain multiple use cases of implementing AI across industries. We also discussed the use cases of implementing different AI technologies like Machine Learning, NLP, Deep Learning, and Computer Vision.

Multiple perquisites impact the success of AI implementation, primarily the availability of labeled data, a good data pipeline, a good selection of models & the right talent to build the AI solution finally. Once these perquisites are met, a step-by-step process can be followed to create effective AI models accurately."