This action research study focuses on the integration of "AI assistants" in two Agile software development meetings: the Daily Scrum and a feature refinement, a planning meeting that is part of an in-house Scaled Agile framework. We discuss the critical drivers of success, and establish a link between the use of AI and team collaboration dynamics. We conclude with a list of lessons learnt during the interventions in an industrial context, and provide a assessment checklist for companies and teams to reflect on their readiness level. This paper is thus a road-map to facilitate the integration of AI tools in Agile setups.

The widespread availability of Large Language Models (LLMs) within Integrated Development Environments (IDEs) has led to their speedy adoption. Conversational interactions with LLMs enable programmers to obtain natural language explanations for various software development tasks. However, LLMs often leap to action without sufficient context, giving rise to implicit assumptions and inaccurate responses. Conversations between developers and LLMs are primarily structured as question-answer pairs, where the developer is responsible for asking the the right questions and sustaining conversations across multiple turns. In this paper, we draw inspiration from interaction patterns and conversation analysis -- to design Robin, an enhanced conversational AI-assistant for debugging. Through a within-subjects user study with 12 industry professionals, we find that equipping the LLM to -- (1) leverage the insert expansion interaction pattern, (2) facilitate turn-taking, and (3) utilize debugging workflows -- leads to lowered conversation barriers, effective fault localization, and 5x improvement in bug resolution rates.

Chatbots and AI-agents have become widespread in customer service and in applications like knowledge management, recommender systems, and help desks. Businesses increasingly want to benefit from the capabilities of large language models like OpenAI's GPT-4 and applications powered by such models. Nevertheless, the use of generative AI by companies has been seriously slowed down by concerns about data protection and by the fact that generative AI is known to sometimes make things up - create "hallucinations" as it is often called. Even if an answer does not contain hallucinated information, it may still suffer from incompleteness or misleadingly connected pieces of information. However, companies that want to use AI-agents in non-trivial circumstances need to be able to control them, in particular in customer-facing applications. It would be very unfortunate if it misinforms customers about the company's products or prices. It should also stick very closely to the intended marketing messages. While there is a lot of discussion about "safe AI", "reliable AI", "trustworthy AI", "explainable AI" (XAI) etc., the question of "controllable AI" is rarely discussed. However, as stated above, it is very often crucial that enterprises cannot just rely on, but are in fact able to control an AI system (more precisely, exercise control at design time how the system will behave at runtime).

Human-AI collaboration for decision-making strives to achieve team performance that exceeds the performance of humans or AI alone. However, many factors can impact success of Human-AI teams, including a user's domain expertise, mental models of an AI system, trust in recommendations, and more. This work examines users' interaction with three simulated algorithmic models, all with similar accuracy but different tuning on their true positive and true negative rates. Our study examined user performance in a non-trivial blood vessel labeling task where participants indicated whether a given blood vessel was flowing or stalled. Our results show that while recommendations from an AI-Assistant can aid user decision making, factors such as users' baseline performance relative to the AI and complementary tuning of AI error types significantly impact overall team performance. Novice users improved, but not to the accuracy level of the AI. Highly proficient users were generally able to discern when they should follow the AI recommendation and typically maintained or improved their performance. Mid-performers, who had a similar level of accuracy to the AI, were most variable in terms of whether the AI recommendations helped or hurt their performance. In addition, we found that users' perception of the AI's performance relative on their own also had a significant impact on whether their accuracy improved when given AI recommendations. This work provides insights on the complexity of factors related to Human-AI collaboration and provides recommendations on how to develop human-centered AI algorithms to complement users in decision-making tasks.

Artificial intelligence is frequently discussed yet it's too early to show real gains. AI's major headwind is the cost of the investment, which will skew returns in the short-term. When the turnaround occurs, however, companies who are making the investment can expect to be rewarded disproportionately with a wide performance gap. In a recent report, McKinsey predicts AI leaders will see up to double the cash flow. We can see some evidence of this in Alphabet's revenue segment, Other Bets, which includes many AI projects with a loss of $3.35 billion in 2018.

Artificial intelligence is frequently discussed yet it's too early to show real gains. AI's major headwind is the cost of the investment, which will skew returns in the short-term. When the turnaround occurs, however, companies who are making the investment can expect to be rewarded disproportionately with a wide performance gap. In a recent report, McKinsey predicts AI leaders will see up to double the cash flow. We can see some evidence of this in Alphabet's revenue segment, Other Bets, which includes many AI projects with a loss of $3.35 billion in 2018.

Amazon's Alexa needs its own robotic body to reach its full potential, creators of the smart assistant say. It may mean future versions of the popular gadget are able to follow their owners around the house and listen to conversations with greater ease. Rohit Prasad, head scientist at Amazon's Alexa division, claims the only way to rid the AI-assistant of its shackles is to'give it eyes and let it explore the world'. He said this would be the only way for the devices to better understand the ambiguity and complexity of human language. Rohit Prasad, head scientists at Amazon's Alexa division, claims the only way to rid the AI-assistant of its shackles is to give it'eyes to explore the world'.

Google's Home speaker range can now connect to any Bluetooth speaker in your house. The addition means Google Home users can turn any speaker into a voice-controlled sound system - though they will have to bark all commands through the AI-assistant. In a blog post on the update Google said the extra connectivity allows Home users to'amp up the sound' of the compact gadgets. Google's Home speaker range, including its Home Mini (pictured), can now connect to any Bluetooth speaker in your house. 'We brought this feature to life after hearing how much you wanted to amp up the sound with your Google Home Mini,' the company said.

Rolls-Royce has unveiled the concept for its next Vision Vehicle, named103EX, which will be a driverless vehicle with its own AI-powered assistant/chauffeur called Eleanor. The Rolls Royce 103EX was unveiled at an event in London, and its unique design has received some mixed response. As this Washington Post article someone called the car'butt-ugly,' while another review called it a'sublimely crazy' idea. Rolls-Royce 103EX will actually be launching in the market somewhere around 2040s, and as this Guardian article points, the company has not revealed exactly how the car is powered, even though it is claiming zero-emissions from its new concept vehicle. The car has quite an intriguing design nonetheless with "28-inch tall, narrow wheels upon which the car glides are each hand-built from 65 individual pieces of aluminium", a luggage compartment that is right next to the front wheels, and also opens and closes automatically at the start and end of a journey.