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.

August 05, 2022 05 Aug'22
Abbyy aims intelligent document processing at business users

Since its birth three decades ago, Abbyy's trajectory has shifted from old-line OCR tech to the forefront of the fast-growing intelligent document processing sector.
August 05, 2022 05 Aug'22
New AI ethics advisory board will deal with challenges

Created by the Institute for Experiential AI at Northeastern University, the board will help organizations without internal audit boards but will face some challenges.
August 03, 2022 03 Aug'22
How the economic downturn is affecting the AI sector

Enterprise budget cutting is slowing AI projects. Vendors may not feel the impact now but likely won't be spared. Meanwhile, venture capitalists have cut back investments.
July 21, 2022 21 Jul'22
DataRobot CEO resigns amid turmoil

DataRobot CEO Dan Wright resigned amid turmoil surrounding 'immoral' stock sales and layoffs. His departure follows the protest resignation of DataRobot's chief AI evangelist last week.

Bring yourself up to speed with our introductory content

The white-box model approach aims for interpretable AI

The white-box model approach to machine learning makes AI interpretable since algorithms are easy to understand. Ajay Thampi, author of 'Interpretable AI,' explains this approach. Continue Reading
AI winter

AI winter is a quiet period for artificial intelligence research and development. Continue Reading
How hybrid chatbots improve customer experience

Hybrid chatbots combine human intelligence with AI used in standard chatbots to improve customer experience. Learn how industries are using them to engage with customers. Continue Reading

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

Why banks need MLOps for digital transformation

Financial institutions should look to MLOps to ease the development, deployment and management of machine learning models. MLOps is often ignored, yet banking will benefit from it. Continue Reading
The future of data science: Career outlook and industry trends

The future of data science as a profession is unclear, as new technologies change the responsibilities of data scientists. It may also soon change the nature of the job. Continue Reading
Hybrid AI examples demonstrate its business value

As businesses weigh the potential benefits of implementing AI systems, hybrid AI examples demonstrate the technology's practical value for businesses. Continue Reading

Learn to apply best practices and optimize your operations.

AI chatbots don't need to be sentient, they just need to work

Chatbot users, vendors and ex-Google engineer Blake Lemoine discuss what is needed to make current chatbot tech more effective for customer service organizations. Continue Reading
How AI governance and data privacy go hand in hand

Given instances where AI compromise data privacy and security, it's imperative that organizations understand both AI and data privacy can coexist in their AI governance frameworks. Continue Reading
Real-world hyperautomation examples show AI's business value

Hyperautomation examples in the real world help businesses automate as many of their processes as possible and achieve their strategic goals. AI is instrumental in these efforts. 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