Applied Artificial Intelligence: An Introduction For Business Leaders

by Mariya Yao

an analytical culture is required to succeed in AI initiatives since accurate, centralized data is the foundation for developing effective machine learning solutions.

In such cases, you may need to take a step back and concentrate on earlier, more foundational

aspects of building an enterprise-wide ...

Data: What Happened?

This requires you to have collected the right kinds of data.

Information and Knowledge: Why Did It Happen?

While some knowledge can be

encoded, others will require you to augment your quantitative analysis with qualitative interviews and external research.

Intelligence: What Wi...

If you have data but not the domain expertise to interpret that data, you are at risk of feeding the wrong assumptions into your intelligent systems, which will invariably produce the wrong results.

Insights: What’s the Best That Could Happen?

Machine learning can also be used to discover opportunities you weren’t aware of, such as new customer segments you can target, more effective messaging and processes for your sales and marketing functions...

Change and Impact: How Can We Automate Continuous Transformation?

The ultimate outcome for an analytics practice is to tighten and automate the feedback loop between data, insights, action, and results.

Artificial intelligence techniques are most economically used to automate problems that are time-consuming, repetitive, and simple in scope. Most machine learning approaches

also require large quantities of clean, trainable data. Just because a problem can be automated does not mean that an AI solution is appropriate.

Here are key questions to ask when evaluating whether your problem needs an AI solution:

Is this a process that can be solved using machine learning?

Is it suitable for machine learning?

Machine learning is best used to replicate human decisions for tasks where correct answers are clear and measurable.

Is data available?

Should you invest in your technical capabilities and turn your organization into a technology company like Google and Amazon?

Or should you stick to your core business expertise and find third-party solutions for your AI needs? Here are some criteria that will guide your decision-making process:

First, determine whether your technology is a core functionality of your business.

Second, evaluate the availability of in-house talent.

Third, set a timeline for deployment.

Fourth, assess the availability of data for your project. Having tons of data doesn’t mean that you have the right data.

Finally, total ownership cost is a key factor when deciding whether to build or buy.

To differentiate between value and hype, be sure to probe any prospective solution provider on the following:

Access to Data

Many companies are developing machine learning algorithms, but those with access to large volumes of prop...

Domain Specificity

Greater domain knowledge in a field allows for faster integration at lower cost.

Team Talent

ROI Metrics

When choosing between potential partners, evaluate how they measure successful outcomes.

Client Experience

Ease of Integration

Pricing

Find a company that has a pricing model aligned with your business goals.

Security

Data Connection

Does the prospective product offer seamless connections with the other enterprise tools on which you depend, such as your data and analytics provider or CRM system?

Language Support

Professional Support

Regulatory Requirements

FDA

General Data Protection Regulation (GDPR)

The GDPR also provides EU citizens with the "right to explanation," in which they have the option of reviewing the decision of a particular algorithm.(69)

Limits of Use