Design Guide for Cisco ACI with Avi Vantage

whitePaper | May 31, 2022

End-to-End (E2E) testing has traditionally been seen as a ‘final frontier’ of sorts in software development.

whitePaper | December 22, 2022

Contact centres are often viewed as cost drivers rather than business drivers. This is understandable, when you consider that differentiation efforts in this space have often struggled to be sustainable from a cost or staffing perspective. Figuring out the formula of investment to increased income is helped when you have an experienced guide to shorten your journey. Read on to get the benefit of our two companies’ experience, multiplied by hundreds of clients.

whitePaper | October 18, 2022

We first published this index five years ago. The world was different then. Governments were dealing with a landscape defined by benign growth, relative global stability, the optimism of an emergent technology. In 2022, it feels like everything has changed. It looks like we are entering a global recession, war has returned to Europe and AI breaks new ground every week. Just the last month has brought advances in art (Stable Diffusion), chat (ChatGPT) and negotiation (CICERO).

whitePaper | August 5, 2020

Your cloud-enabled future loomed bright. Major budgets were allocated. Agonising hours were spent herding stakeholders. You’re now using cloud, but your teams and the wider business aren’t reaping the expected benefits. Even with the best intentions, tech leaders are struggling to fill those skills gaps, particularly when it comes to cloud implementation, migration and net new deployments. As a result, the business is held back – from capitalising on the cloud’s potential to deliver value internally (boosting efficiency and agility) and externally (delivering products and services that boost revenue).

whitePaper | October 20, 2022

Telecoms need to be able to incorporate new technologies and next-generation connectivity such as 5G to customers and end users.

whitePaper | July 20, 2022

The growing diversity of actors and circumstances involved in AI/ML deployment makes establishing a set of ethical participatory practices especially difficult for this field. Even among practitioners working at the same organization, there can be substantial differences in both their knowledge about how AI/ML systems were created or will be used and their ability to incorporate inclusive practices. Additionally, the greater availability of AI development platforms, including “no-code” platforms, means that algorithmic models can be deployed without having deep expertise, expanding the number of circumstances automated systems are deployed in. This means many more instances where automated systems are being deployed without consideration of how the algorithms were developed, the provenance of the datasets and nature of the bias on which they were trained and tested, and the ethical implications of their development and deployment.