Baidu and Google are spending millions in AI to solve fundamental computing issues like speech recognition. They compete to make AI products to revolutionize their businesses.

Here is a 4-step process for utilizing Lean Startup techniques to produce value in AI projects regularly:

Step 1 — Hypothesize the company value from results

Since every AI business applications use controlled learning, you can hypothesize possible effects.

The goal is to answer. Do we have a company case for AI?

It is the lowest-risk (and most important) initial step. Just eliminate if the answer is "no."

For example, CapitalOne can recognize fraud alerts based on previous customer purchasing exercise.

Data are the questionable purchase, past practice, and other signals. Outputs are the alerts.

Step 2 — Run data science experiments

AI products live or die based on information.

Is the Input data associated with our desired Output?

Do we have enough Input information?

Are the results great enough to generate market value?

Data scientists can use regression and clustering analysis to answer the question, Does our data support our company hypothesis?

Machine learning will not work if the Input data doesn't foretell the result.

Step 3 — Go live with standard machine learning algorithms

Here's a useful heuristic:

Start with the most straightforward algorithm, which creates results.

Traditional machine learning techniques, like linear regression models, need fewer data and are simpler to implement.

Moreover, engineers can troubleshoot issues by generating visual graphs of the output.

Traditional machine learning methods tell whether AI is feasible. Can we dynamically create models that generate results from Inputs?

In many applications like Stripe's fraud system, conventional machine learning techniques work just accurate.

Step 4 — Move to deep learning at the right time

Deep learning models use extensive neural networks on high-performance machines.

Deep learning is creating so much attention because results keep enhancing as more information is added.

Sadly, this performance comes at a high price of data, complexity, and processing power.

Start by adding more data and examining the performance of neural networks to solve the question, Does adding more Input information to improve our Output? 

AI is still in its start, and we're all collectively acquiring various new applications. Lean Startup methods can decrease your adoption risk and fulfill early wins to support continued financing.