Harvard Business Review
MARCH 26, 2024
While CDOs and data leaders are excited about generative AI, they have much work to do to get ready for it. Despite excitement, companies have yet to see clear value from generative AI and need to do significant work to prepare their data.
TechEmpower Innovation
MARCH 4, 2024
Generative AI is revolutionizing how corporations operate by enhancing efficiency and innovation across various functions. Focusing on generative AI applications in a select few corporate functions can contribute to a significant portion of the technology's overall impact.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Paul Hobcraft
NOVEMBER 21, 2023
Innovation is undergoing a radical change, in opening up to technology, collaborative thinking and the value of generative AI thinking. For me, ecosystem innovation and generative AI have arrived at that pivotal point to significantly influence future innovation design. Innovation needs reinventing.
Harvard Business Review
NOVEMBER 8, 2023
This calls for a rethink of the incentives and economics surrounding human-generated content. The real bottleneck in generative AI might not be computation capacity or model parameters, but our unique human touch. Yet, we are on the brink of a digital world that is increasingly filled with AI-generated clutter.
Advertisement
Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success?
Harvard Business Review
NOVEMBER 7, 2023
Generative AI can be a boon for knowledge work, but only if you use it in the right way. New generative AI-enabled tools are rapidly emerging to assist and transform knowledge work in industries ranging from education and finance to law and medicine. However, there is no need to wait for these externally-imposed changes.
TechEmpower Innovation
OCTOBER 9, 2023
Artificial Intelligence (AI), and particularly Large Language Models (LLMs), have significantly transformed the search engine as we’ve known it. With Generative AI and LLMs, new avenues for improving operational efficiency and user satisfaction are emerging every day.
Advertisement
In the rapidly evolving healthcare industry, delivering data insights to end users or customers can be a significant challenge for product managers, product owners, and application team developers. The complexity of healthcare data, the need for real-time analytics, and the demand for user-friendly interfaces can often seem overwhelming.
Advertisement
In the fast-moving manufacturing sector, delivering mission-critical data insights to empower your end users or customers can be a challenge. Traditional BI tools can be cumbersome and difficult to integrate - but it doesn't have to be this way.
Advertisement
Organizations look to embedded analytics to provide greater self-service for users, introduce AI capabilities, offer better insight into data, and provide customizable dashboards that present data in a visually pleasing, easy-to-access format.
Advertisement
The complexity of financial data, the need for real-time insight, and the demand for user-friendly visualizations can seem daunting when it comes to analytics - but there is an easier way. Together, we can overcome these hurdles and empower your users with the data they need to drive success.
Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage
When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes.
Speaker: Dr. Greg Loughnane and Chris Alexiuk
Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.
Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase
However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.
Speaker: Maher Hanafi, VP of Engineering at Betterworks & Tony Karrer, CTO at Aggregage
Executive leaders and board members are pushing their teams to adopt Generative AI to gain a competitive edge, save money, and otherwise take advantage of the promise of this new era of artificial intelligence.
Expert insights. Personalized for you.
135 
Let's personalize your content