The rise of Product Management and scalable Agile delivery practices (e.g. SAFe, DAD, …) have led to diluting (or even deferring) “strategic planning” and “design thinking” enabled by architecture. Instead, architecture has been reduced to a project level support role to get “tech teams” to help launch new products.
The opportunity for Enterprise and Business Architecture is to stitch building blocks and siloed solutions together to form a tapestry that reflects the goal, vision and mission of the over-arching business.
Companies often forget that while technology may be crucial to their success, they are in fact not built or intended to work like modern day software technology companies such as Facebook, Amazon, etc. but rather businesses that serve to support our society and economy via Manufacturing, Aerospace, Retail, Healthcare and Pharma (to name a few).
We need to refresh the conversation about the role, purpose and value of Enterprise AND Business Architecture if we are to have a chance to genuinely be competitive and innovative over the long term rather than reactively adopt emerging technology without a sound game plan.
Click the link to read the advertised article by Anthony Hill that explores this topic further.
‘Conversational AI’ refers to the use of messaging apps, speech-based assistants and chatbots to automate communication and simulate conversations to create personalized customer experiences at scale.
Both the terms ‘Chatbot’ and ‘Conversational AI’ have the same meaning. ‘Conversational AI’, however, is more inclusive of all the technology that falls under the bot umbrella like voice bots and voice + text assistants, whereas ‘chatbots’ have a more limited ‘text-only’ connotation.
Businesses can use ‘Conversational AI’ to automate customer-facing touchpoints everywhere – on social media platforms like Facebook & Twitter, on their website, their app or even on voice assistants like Alexa & Google Home.
NVIDIA’s ‘Conversational AI’ becomes the first platform to train one of the most advanced AI language models – BERT, within an hour and complete AI inference in just over 2 milliseconds. This trailblazing level of performance makes it possible for developers to leverage state-of-the-art language understanding for large-scale applications they can make accessible to hundreds of millions of consumers globally.
The world of AI has just taken a major step forward… thanks to #NVIDIA !
Click on link to read the NVIDIA article for more details.
#ai #emergingtech #conversationalai #dataops #analytics #voicebots #assistants #bert
Do you still carry cash in your wallet? Do you actually have a wallet? Or have you transitioned to e-wallets and digital currency?
The future of money and the analysis of the development of the cashless society which includes the analysis of the means to achieve it and the analysis of the challenges and benefits it can bring, have been studied and discussed by academia on a regular basis in Europe since the early 2010s.
New payment solutions as disruptive technologies, emerging payment technologies, emerging payment business models, biometric payments, integrity, and privacy, and the design of new payments and technologies continue emerging.
Sweden goes from being the first in adopting banknotes in Europe in 1661 to introducing its own digital currency in 2021, and becoming the first world’s cashless society in 2023.
India could be a ‘cashless model’ for the world .. Growing smartphone use and crashing data costs have helped cashless economy to grow immensely.
Cashless society is happening. There’s no turning back !
Click link to read more in this BCG post.
#Cashless #DigitalTransformation #automation #contactless #epayments
Success of AI / ML adoption requires alignment of multidisciplinary teams, collaboration practices (such as DevOps + DataOps) and access to large volumes of (relatively) clean (curated) data.
AI requires selecting platforms, tools, and infrastructure that needs to be ramped up and down as experiments are conducted. Teams can consider a multitude of platforms (TensorFlow, Keras, PyTorch, Caffe), cloud provider (AWS, Azure, Bluemix, Google), and a growing number of collaboration platforms (Dataiku, H20.ai, Databricks, Anodot, Clusterone and others) as part of their AI and machine learning environment. The job of architecture and product management teams.
With data ready, teams need a working process for running experiments using Agile practices and governance mechanisms to capture metadata and results – for analysis and review – to determine success levels and what follow-on experiments to prioritise. This involves a level of strategic thinking and portfolio planning.
When results yield satisfactory results, teams need to determine how to establish a production process to run new data through the AI models.
The article references tangible success stories.
#dataops #devops #enterprisearchitecture #productmanagement #emergingtech #analytics #AI #ML #Agile