Electric vehicles are expected to significantly contribute to CO2-eq. emissions reduction, but the increasing number of EVs also introduces chal-lenges to the energy system, and to what extent it contributes to achieving cli-mate goals remains unknown. Static modeling and assumption-based simula-tions have been used for such investigation, but they cannot capture the realistic ecosystem dynamics. To fill the gap, this paper investigates the impacts of two adoption curves of private EVs on the electricity distribution grids and national climate goals. This paper develops a multi-agent based simulation with two adoption curves, the Traditional EV charging strategy, various EV models, driv-ing patterns, and CO2-eq. emission data to capture the full ecosystem dynamics during a long-term period from 2020 to 2032. The Danish 2030 climate goal and a Danish distribution network with 126 residential consumers are chosen as the case study. The results show that both EV adoption curves of 1 million and 775k EVs by 2030 will not satisfy the Danish climate goal of reducing transport sector emissions by 30% by 2030. The results also show that the current resi-dential electricity distribution grids cannot handle the load from increasing EVs. The first grid overload will occur in 2031 (around 16 and 24 months later for the 1 million and 775k EVs adopted by 2030) with a 67% share of EVs in the grid.

A new study from Accenture says just 12% of firms have figured out how to deploy AI to "achieve superior growth and business transformation." In other words, there's quite a bit of work yet to be done when it comes to AI success. The correlation between AI use and business achievement is still positive. As far back as 2019, Accenture noted that top AI achievers see 50% better revenue growth than their peers who are not AI experts. No one is calling for an AI rethink, yet progress seems to be coming painfully slowly in the real-world branch of the AI discussion.

For those who are unfamiliar, the concept of the technology adoption curve was popularized by Everett Rogers in his book Diffusion of Innovations. Although some HR tech writers have joked that the overall HR technology adoption curve looks markedly different, with a majority of HR tech buyers disproportionately disposed to the far left of the curve, the true speed of adoption of AI in talent acquisition tracks closely to the standard curve. Innovators and early adopters are exposed to the greatest amount of risk from adopting new often unproven technologies. However, due to the considerable competitive advantages that may be realized by implementing breakthrough technology, they also stand to benefit the most. While the innovators and adopters have a greater appetite for and ability to manage risk, for the majority of companies shooting to be part of the early majority is the most strategically advantageous way to implement new technology.

As excitement (and panic) about the opportunities and threats of emerging technologies spreads into boardrooms across the world, it is worth considering that it is not just the technologies themselves which are disruptive. Indeed the bursting of the tech bubble in 2000 taught us that the technologies themselves are vulnerable to issues of scaling and adoption. Furthermore, of the key emerging technologies we have identified: 3D Printing, Advanced Robotics, Artificial Intelligence, Internet of Things and Virtual Reality, some of them have been around in some shape or form for some time. What is different now to 2000, and means the impact of technology is truly disruptive on a large and wide scale, is that we now have the business model enablers to drive the technology through a business. These business model enablers mean we now have access to the funding, platforms, processing power, software, and data to turn the technology into useful, scalable solutions.

This post originally appeared on the PullString blog. Unless you've been living under a rock for the last twelve months, you've heard about voice assistants. They go by the name of Alexa, Google Assistant or Siri and can give you information about your commute or the weather, turn lights on and off, book flights, and order groceries. A line of questioning we hear a lot from CMOs in North America and Europe is: should they even care about this latest technology trend, does voice deserve its own strategy, does it warrant attention at their level of the organization? We believe there are four strategic reasons why every CMO should start thinking about what voice assistant customer engagement means for their digital transformation strategy.

As of last month Alphabet Inc.'s AI division, Google DeepMind, has open-sourced their new machine learning platform DeepMind Lab. Artificial Intelligence is the technology of the moment, constantly debated and attracting massive attention from investors. Despite warnings from influential figures including Professor Stephen Hawking, Google's decision to open up their software to other developers is part of a mass movement to advance the capabilities of AI. Facebook open sourced its own deep learning software last year, and Elon Musk's non-profit organisation OpenAI recently released Universe, an open software platform that can be used to train AI systems. So, why have Google, OpenAI and others made these platforms public, and how will this affect the adoption of Artificial Intelligence and machine learning as a whole?

As excitement (and panic) about the opportunities and threats of emerging technologies spreads into boardrooms across the world, it is worth considering that it is not just the technologies themselves which are disruptive. Indeed the bursting of the tech bubble in 2000 taught us that the technologies themselves are vulnerable to issues of scaling and adoption. Furthermore, of the key emerging technologies we have identified: 3D Printing, Advanced Robotics, Artificial Intelligence, Internet of Things and Virtual Reality, some of them have been around in some shape or form for some time. What is different now to 2000, and means the impact of technology is truly disruptive on a large and wide scale, is that we now have the business model enablers to drive the technology through a business. These business model enablers mean we now have access to the funding, platforms, processing power, software, and data to turn the technology into useful, scalable solutions.

As of last month Alphabet Inc.'s AI division, Google DeepMind, has open-sourced their new machine learning platform DeepMind Lab. Artificial Intelligence is the technology of the moment, constantly debated and attracting massive attention from investors. Despite warnings from influential figures including Professor Stephen Hawking, Google's decision to open up their software to other developers is part of a mass movement to advance the capabilities of AI. Facebook open sourced its own deep learning software last year, and Elon Musk's non-profit organisation OpenAI recently released Universe, an open software platform that can be used to train AI systems. So, why have Google, OpenAI and others made these platforms public, and how will this affect the adoption of Artificial Intelligence and machine learning as a whole?