Data are the medium through which individuals' identities and experiences are filtered in contemporary states and systems, and AI is increasingly the layer mediating between people, data, and decisions. The history of data and AI is often one of disability exclusion, oppression, and the reduction of disabled experience; left unchallenged, the current proliferation of AI and data systems thus risks further automating ableism behind the veneer of algorithmic neutrality. However, exclusionary histories do not preclude inclusive futures, and disability-led visions can chart new paths for collective action to achieve futures founded in disability justice. This chapter brings together four academics and disability advocates working at the nexus of disability, data, and AI, to describe achievable imaginaries for artificial intelligence and disability data justice. Reflecting diverse contexts, disciplinary perspectives, and personal experiences, we draw out the shape, actors, and goals of imagined future systems where data and AI support movement towards disability justice.

Today, interactions matter more than ever. According to data compiled by NICE, once a consumer makes a buying decision for a product or service, 80% of their decision to keep doing business with that brand hinges on the quality of their customer service experience, according to NICE research. "I think AI is becoming a really integral part of every business today because it is finding that sweet spot in allowing businesses to grow while finding key efficiencies to manage that bottom line and really do that at scale," says vice president of product marketing at NICE, Andy Traba. When many think of AI and customer experiences, chatbots that give customers more headaches than help often come to mind. However, emerging AI use cases are enabling greater efficiencies than ever.

Hosted by The Alan Turing Institute, AI UK is a yearly event that brings together representatives from government, academia and industry to showcase data science and AI research and innovation in the UK. This year, the two-day conference featured talks, panel discussions, and hands-on workshops, and participants could attend in-person or remotely. One of the sessions focussed on an on-going case study in a London borough whereby the local council is using data and AI to help inform their decision making, and to improve what they do. Tariq set the scene by describing the borough of Camden, an area that not only houses institutions such as University College London and the Francis Crick Institute, and companies such as Google, but also some of the poorest communities in Europe. The council wants to tackle inequality and sees the use of data as one potential avenue.

But true data intelligence is about more than establishing the right data foundation. Organizations are also wrestling with how to overcome dependence on highly technical staff and create frameworks for data privacy and organizational control when using generative AI. Specifically, they are looking to enable all employees to use natural language to glean actionable insight from the company's own data; to leverage that data at scale to train, build, deploy, and tune their own secure large language models (LLMs); and to infuse intelligence about the company's data into every business process. In this next frontier of data intelligence, organizations will maximize value by democratizing AI while differentiating through their people, processes, and technology within their industry context. Based on a global, cross-industry survey of 600 technology leaders as well as in-depth interviews with technology leaders, this report explores the foundations being built and leveraged across industries to democratize data and AI.

Data and AI technology for manufacturing is having a moment. As AI has expanded its lengthy reach into manufacturing, there have been several purpose-built releases lately from companies like Nvidia and Databricks that are helping companies make sense of the deluge of data collected on everything from physical operations to the supply chain. Snowflake is now part of this action with the debut of its Manufacturing Data Cloud. The company says this new offering will enable companies in the automotive, technology, energy, and industrial sectors to tap into the value of siloed industrial data by leveraging Snowflake's data platform, partner solutions, and industry-specific datasets. The Snowflake Data Cloud provides a platform for data warehousing, SQL analytics, machine learning, data engineering, and monetization of third-party data.

Even in today's changing business climate, our customers' needs have never been more clear: They want to reduce operating costs, boost revenue, and transform customer experiences. Today, at our third annual Google Data Cloud & AI Summit, we are announcing new product innovations and partner offerings that can optimize price-performance, help you take advantage of open ecosystems, securely set data standards, and bring the magic of AI and ML to existing data, while embracing a vibrant partner ecosystem. In the face of fast-changing market conditions, organizations need smarter systems that provide the required efficiency and flexibility to adapt. That is why today, we're excited to introduce new BigQuery pricing editions along with innovations for autoscaling and a new compressed storage billing model. BigQuery editions provide more choice and flexibility for you to select the right feature set for various workload requirements.

If you want to explore this area further, here's everything you need to know about what data and AI can mean for your future energy costs and how these trends will impact your utility bill over time! The average American home uses more than one trillion kilowatt-hours of electricity annually, or enough to power 3 million homes. Nearly half of that is used by appliances. The good news is that there are several ways to save on energy costs without compromising comfort or convenience. For example, LED bulbs can last up to 50 times longer than traditional incandescent bulbs and use 75 percent less electricity; smart thermostats can automatically adjust the temperature settings in your home based on where people are located, saving you an average of 10% on heating and cooling costs. Data and artificial intelligence (AI) fundamentally change how we consume, manage, produce and distribute energy.

Thank you for joining us on "The cloud hub: From cloud chaos to clarity." Technologies powered by data and AI can be game changers for retailers to enhance customer experience. But they must overcome the associated challenges to reap the benefits.

Editor's note: Michael Sanky is global industry lead of healthcare and life sciences at Databricks, an enterprise software company. As pervasive health disparities in the U.S. continue to widen, data and artificial intelligence offer the potential to help close that gap. New technologies can analyze large, diverse data sets, informing the work of researchers, decision makers and policymakers across healthcare. If done correctly, AI can ultimately improve care delivery, advance proactive healthcare planning and predictive treatments, reduce clinician burnout and drive better patient outcomes. To this end, we've already seen groundbreaking advancements that move the needle in healthcare.

As the CTO of a major financial institution, it is crucial to stay informed about the latest trends in data and AI in the financial services industry in order to prepare for the future and remain competitive. While there are many vendor platforms and systems available on the market to help decision-makers solve their challenges initially, the true value varies based on your organization's readiness to implement. In the next five to 10 years, there are several key trends expected to shape the financial services industry. Banks are increasingly leveraging cloud-based solutions to store, process and analyze large amounts of data, as well as to improve scalability and reduce costs. This can help them gain insights into customer behavior and market trends.