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.

January 20, 2022 20 Jan'22
New U.S. Chamber of Commerce commission to promote AI

The bipartisan commission includes experts with a variety of technology backgrounds and is led by two former congressmen. A report by the commission is expected later this year.
January 12, 2022 12 Jan'22
Mostly AI plans to use $25M funding for responsible AI efforts

The synthetic data vendor works with customers in Europe and North America. Experts say more than half of AI projects will be created using synthetic models by 2024.
January 06, 2022 06 Jan'22
AI vendor Fractal kicks off 2022 with $360M fund raise

The AI vendor's fundraising success shows investors' willingness to pump money into vendors that are applying AI in vertical industries.
December 29, 2021 29 Dec'21
AI is the game-changer needed in customer service

The holiday season is a busy time for call center agents. However, problems in the CX industry are leading organizations to turn to machine learning for optimization.

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
The benefits of an AI-first strategy

Enterprises should put AI first in their business strategies by constantly collecting and using new data to power AI models, argues startup investor Ash Fontana. Continue Reading

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

How cloud RPA is key to automation's future

Companies have traditionally used robotic process automation (RPA) as on-premises software but are now embracing cloud RPA as its business benefits are outweighing the drawbacks. Continue Reading
Why TinyML use cases are taking off

TinyML technology can successfully collect and analyze data in real scenarios, as demonstrated in various use cases. Continue Reading
Agility and resiliency vital to digital-first business models

For enterprises transforming their business models, ServiceNow's Yoav Boaz explains how Disney and Peloton reinvented themselves and thrived during the COVID-19 pandemic. 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
AI and climate change: The mixed impact of machine learning

AI can both help and hurt the environment. While companies use artificial intelligence to increase factory efficiency and lower energy costs, training AI demands a lot of energy. 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