AI business strategies

Artificial intelligence software is changing how enterprises conduct business. Machine learning supports data-driven decisions, customer service chatbots follow up on leads, robots in manufacturing boost productivity -- the list goes on. Get advice for building and implementing AI business strategies.

October 08, 2021 08 Oct'21
Examining both sides of the AI regulation debate

Some government and business leaders want more AI regulation to ensure that systems don't discriminate against or harm others. Others say too much regulation will stifle innovation.
October 05, 2021 05 Oct'21
Mailchimp AI tool targets small-business email marketing

The veteran email marketing vendor's new tool, the result of its purchase of martech vendor Inspector 6, is aimed at boosting small businesses' email campaigns with AI.
October 01, 2021 01 Oct'21
Experts cautious about Apple's mood-detecting AI research

The tech giant is working with UCLA to develop technology that uses facial recognition, sleep, typing patterns and other data points to detect emotion. But some are cautious.
September 21, 2021 21 Sep'21
Deloitte AI Academy: A training ground for AI talent

The global consulting giant's new AI Academy will provide supplemental education that closes the gap between the knowledge of AI tools and domain experiences and applications.

Bring yourself up to speed with our introductory content

robot economy

The robot economy is a scenario in which most of the labor required to sustain human life is automated. Continue Reading
Why transparency in AI matters for businesses

To ensure model accuracy, businesses need to understand why their machine learning models make their decisions. Certain tools and techniques can help with that. Continue Reading
What is artificial intelligence?

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. Continue Reading

Weigh the pros and cons of technologies, products and projects you are considering.

Why companies should use AI for fraud management, detection

AI is involved in many cybersecurity processes. Now it's making inroads in fraud management and detection. The benefits, however, are not without AI's nagging bias challenge. Continue Reading
Why algorithmic auditing can't fully cope with AI bias in hiring

Critics say you can't trust AI yet with diversity and inclusion in hiring -- or the elaborate audits meant to root out AI bias. Hiring software vendors beg to differ. Continue Reading
Solving the AI black box problem through transparency

Ethical AI black box problems complicate user trust in the decision-making of algorithms. As AI looks to the future, experts urge developers to take a glass box approach. Continue Reading

Learn to apply best practices and optimize your operations.

Capitalizing on the many artificial neural network uses

Neural networks have many use cases. Businesses interested in using AI should consider both the challenges and potential gains of deploying neural nets. Continue Reading
AI carbon footprint: Helping and hurting the environment

Companies can use AI to help the environment, including by using it to prevent forest fires and reduce factory waste. At the same time, AI has its own carbon footprint. Continue Reading
Trust but verify: Digging into audits for AI algorithm bias

TechTarget editors discuss efforts to remove bias from AI and why algorithm auditing could be a fix -- provided challenges related to trust and standardization can be overcome. Continue Reading

We’ve gathered up expert advice and tips from professionals like you so that the answers you need are always available.

Expanding explainable AI examples key for the industry

Improving AI explainability and interpretability are keys to building consumer trust and furthering the technology's success. Continue Reading
Combating racial bias in AI

By employing a diverse team to work on AI models, using large, diverse training sets, and keeping a sharp eye out, enterprises can root out bias in their AI models. Continue Reading
Tackling the AI bias problem at the origin: Training data

Though data bias may seem like a back-end issue, the enterprise implications of an AI software using biased data can derail model implementation. Continue Reading