Latest news with #TobiLütke
CTV News
a day ago
- Business
- CTV News
Shopify wins court battle against Canada Revenue Agency in merchant-data case
The Ottawa headquarters of Canadian e-commerce company Shopify are pictured on Wednesday, May 29, 2019. THE CANADIAN PRESS/Justin Tang Shopify Inc. has come out on top of a battle with the Canada Revenue Agency. A federal court order issued Thursday shows Judge Guy Régimbald sided with the Canadian tech company, which was fighting the CRA's attempt to get more than six years of Shopify records. The records were being sought in order to verify that Canadian merchants using Shopify software were obeying the Income Tax Act and the Excise Tax Act. The CRA wanted the names of individuals who own Shopify accounts, their birthdates, addresses, phone numbers and their bank transit, institution and account numbers. It also asked for their Shopify ID numbers, what type of store they ran, when their Shopify accounts were activated or closed and how many transactions and their value were made over the six-year period the CRA was interested in. Some of the information had been requested by the Australian Tax Office, which wanted to ensure Shopify merchants were complying with the country's laws. A separate case Judge Régimbald presided over saw the CRA ask for court permission to obtain and send the records to Australia. CRA spokesperson Sylvie Branch said the agency is aware of the courts decision and 'is currently analyzing the case details and associated information.' Shopify pointed The Canadian Press to a post on X from its CEO Tobi Lütke who shared the outcome of his company's court battle and called the CRA's behaviour 'blatant overreach.' Shopify fought the CRA in both cases when they were filed in 2023, insisting the group of merchants the agency wanted information for was 'overly broad and inconsistently defined.' The company also claimed a multilateral tax treaty being used to seek the information for Australia 'is without domestic force' when information about unnamed people is being requested. Régimbald ultimately decided not to order Shopify to turn over the records to the CRA because he found the tax agency had not outlined an identifiable group of individuals whose data it wanted. He said the court would not entertain a request to hand over information on unnamed parties 'that is unintelligible, incoherent, or otherwise beyond its understanding.' As part of his order, Régimbald requested the CRA pay legal costs of $45,000 in each case, bringing the government's bill to $90,000. This report by The Canadian Press was first published June 2, 2025.
Forbes
a day ago
- Business
- Forbes
3 AI Adoption Metrics That Really Matter
Coworkers using AI together in the office. 'Using AI effectively is now a fundamental expectation of everyone at Shopify,' Tobi Lütke, CEO of Shopify, said in his now viral AI memo on April 7th. But Shopify isn't just encouraging employees to use AI. They are measuring employee AI usage in performance reviews as well. 'We will add AI usage questions to our performance and peer review questionnaire,' Lütke wrote. Pushing for AI adoption in companies isn't brand new, although measuring employee performance based on AI usage is relatively new, and not universally defined yet. Defining how to measure AI usage in a meaningful way is critical to avoid AI performance theater in an era where leaders from startups to Fortune 500 companies are trying to accelerate AI adoption. In Fall 2024, McKinsey surveyed a group of 238 executives, and 46% said that one of the biggest challenges in employee AI adoption is dealing with AI skill gaps. Many want to understand what that means in terms of the outcomes in their companies, and how to power them with AI. Setting performance and usage goals around AI is a balance between understanding AI's capabilities and defining metrics that aren't performative. First, organizations need to define their goals and get a clearer picture of why they want their employees to adopt AI. Consulting companies such as McKinsey and PricewaterhouseCoopers have estimated productivity gains in the trillions based on use cases they evaluated, with PwC estimating up to '14% higher in 2030 as a result of AI – the equivalent of an additional $15.7 trillion.' But what this number means, how real it is, and how to get there is still pretty unclear. Defining how to get meaningful productivity increases from AI is something each company needs to define for themselves. A big thing to consider is that productivity gains from using AI don't show up as immediate ROI on the balance sheet. Being clear on the biggest area of impact AI can have on your business, and building goals around those is a good starting point to create expectations around using AI. Once expectations are set, defining how to measure employee AI usage is next. Stephen Weber, a Director of Engineering at an AI narrative intelligence startup, uses AI frequently and manages a team of engineers. He shared with me during an interview that he thinks about performance with AI through a lens of outcomes, process, and improvement over time. 'If someone is using AI to finish tasks faster without sacrificing quality, that's a good sign. If they're solving harder problems, automating repetitive tasks, or generating new ideas with AI, that's another strong signal,'he said. The double-edge of this AI sword is that some performance metrics can easily be manipulated. These performance metrics don't create value for the business or its customers. Weber explained that 'measuring output volume alone, like how many emails or reports they generate with AI.' Those kinds of performance metrics also can push employees to be overreliant on the use of AI, which according to joint research between Microsoft and Carnegie Melon University, 'can inhibit critical engagement with work and can potentially lead to long-term overreliance on the tool and diminished skill for independent problem-solving.' 'These numbers don't show whether the AI is being used well. They just show activity, not value,' Weber agreed. It's crucial to define expectations around AI usage that reflect its true capabilities while encouraging employees to push the limits of AI in their role. Reddit user Kevinlevin-11 complained that stakeholders in their organization had 'ridiculous' demands for using AI in software testing: 'One person says they don't want to maintain any test cases or code, and AI should be able to write, execute, and fix any failures on its own. Another person wants AI to decide on its own [how to test all business scenarios] .' It's important to have realistic expectations that all software requires some level of human oversight and occasional troubleshooting. Beyond understanding good and bad ways to measure AI usage, it's important to remember you can't always tell who is using AI, how much, or how they are using it without speaking to employees directly. When your organization is creating standards around AI adoption and usage, consider these heuristics. Good AI usage heuristics are: AI usage anti-heuristics are: As your organization considers measuring AI usage as part of employee performance, envisioning the desired value is foundational. Define how to measure it, ensure it's obtainable, mold it to fit different disciplines, and be specific while leaving room for creativity. Be intentional with AI performance measurements, because you will get exactly what you ask for, good or bad.
Forbes
4 days ago
- Business
- Forbes
10 Steps To Corporate AI Adoption
Flat illustration of business people carry AI processing chip embracing artificial intelligence for ... More work success Last week, Shopify CEO, Tobi Lütke sent an email to all staff ordering them to prove they "cannot get what they want done using AI" before asking for extra hires. The email, extolling the virtues of AI adoption, came on top of reported layoffs of 14% in 2022, a further 20% in 2023, and 2.5% in December 2024. It set out a clear "AI first" manifesto requiring that staff train and use LLMs (Large Learning Models like Chat GPT) in their daily work, put AI at the heart of software development, and have AI usage and competence included in performance reviews. No doubt, Tobi's intention was to set a new reality for the firm, encouraging people to embrace AI. But put yourself in the shoes of the average Shopify employee—how inspired and enthusiastic might you feel reading this email? When people have been thinking and doing things in a particular way over and over for years, organizational practices, cultural norms, processes, methods, and structures become deeply ingrained in their brains as part of their "mental model of the world." Neural pathways form and a sort of tribal addiction ensues. Brains crave safety and consistency because learning new ways is metabolically expensive, and the brain has plenty to do without managing yet another new challenge. So when the brain is confronted by dramatic change, its first reaction is to say "no" and try to avoid the change—because it's much easier to keep doing things the way they've always been done. Right now, most brains that read the press, engage with social media, or watch TV have been given the impression that AI is a worrisome technology that might even take their jobs away. So the brain's reaction is to reject and resist. I suspect the brains at Shopify will now be in total resistance mode because Tobi's email will have created an existential threat in the minds of company employees, making his laudable ambition much harder to achieve. In my article Corporate AI Adoption May Fail If Education Doesn't Keep Up, I suggested that AI isn't a technology challenge—it's a culture and behavioral challenge. In this article, I've set out 10 people-centric steps for achieving mass adoption of AI across an organization safely, securely, efficiently, and quickly. These steps help every brain come to terms with a new reality while generating enthusiasm and support. They're based on 40 years of managing changes in workplace behavior and a recent study I directed involving leading companies from various sectors that examined AI's impact on jobs and organizational structures. Miss out on any one of these steps, and the journey to maturity gets longer and trickier. Here they are: Get top leaders must come together to align on what AI is and what it means for the organization. They must define whether AI is primarily for competitive advantage, operational efficiency, customer experience, or workforce augmentation, and create a compelling vision for AI. Address fundamental questions about cultural fit, risk tolerance, and organizational values. This shared vision becomes the North Star that guides all subsequent decisions and communications. Make a credible leader responsible for AI transformation at the C-suite level. Develop a powerful steering group and provide a program management office with the skills and influence to build and manage a cross-disciplinary change program that addresses skills, attitudes, behaviors, education, processes, legal issues, risks, and security. Evaluate whether your culture supports the transparency, experimentation, and iterative learning that AI requires. Identify cultural and skills barriers that could undermine AI adoption and plan how to address them. This includes understanding existing skills, power structures, decision-making processes, and how change has been received historically. Assess current roles, organizational structures, and capabilities alongside potential AI applications. Identify which positions will be eliminated, transformed, or enhanced. Create honest, transparent and consistent messaging about AI's role in your organization's future. Address fears directly rather than avoiding them. Explain the "what," "why," "how," "when," and "who" of AI adoption and what it means for individual employees. Ensure all leaders undergo AI training and can communicate the same message to prevent confusion and mistrust. Develop a list of "tricky questions" with associated answers to address the awkward questions people will ask. Consistency, honesty, and fairness are key. Develop an engagement and change program that is the cornerstone of your change program not a bolt on PR campaign. Find employees across all levels of the organization who have the skills and enthusiasm to become AI advocates within their teams—then train and nurture them. These champions bridge the gap between leadership vision and ground-level reality. They're the people who will get asked the questions their colleagues don't dare ask those in power. Invest in their development, maintain them as a special community, and give them platforms to share experiences and address concerns from their peers. For those with a technical bent, give them deeper "prompt engineering" skills and knowledge so they can become "super users" who train colleagues and new recruits and develop local apps to address micro-process challenges. Computer, black woman and manager training intern or coaching employee and helping with project, ... More work or collaboration. Mentor, corporate and talking about a question, error or pc learning in Nigeria McKinseys "Generative AI and the future of work in America" report suggests that by 2030 as many as 30% of the hours worked by US workers will be replaced by AI. It's critical therefore that training and support is provided to upskill every employee so they understand what AI is (and isn't) and feel confident and competent using core AI tools like LLMs (Large Language Models like ChatGPT, Gemini, Claude, etc.). By doing this, you remove some of the fear as people become familiar and skilled. New roles in IT, HR, and Legal will need to be defined, and new career development routes will need to be considered in an AI-enhanced organizational model. Use champion networks and teams to identify "use cases"—situations where AI can be applied to deliver immediate improvements in effectiveness. Start with AI applications that clearly augment rather than replace human capabilities. Choose initial projects that solve real problems employees face and make their work more interesting or valuable. Success here builds acceptance and enthusiasm for more advanced implementations. Create policies that ensure AI systems remain transparent, ethical, and under human control. Include diverse perspectives in governance decisions, especially from employees who will work directly with AI systems. This framework must address both technical and cultural concerns about AI decision-making and bias. Launch carefully selected pilot projects that demonstrate AI value while building organizational capability. Combine technical implementation with intensive change management support, putting people at the heart of the program. Create feedback mechanisms that capture both technical performance and human experience. Make sure you document these with diaries and videos, and create video testimonials based on outcomes. Expand AI implementation based on lessons learned. Continue investing in workforce development and cultural adaptation. Regularly reassess and adjust your approach based on organizational learning and evolving AI capabilities. Remember that unlike other change programs you may have been involved in, this one doesn't have an end because AI is constantly evolving. New developments will come quickly that may make your existing implementations redundant faster than you'd imagine. Perpetual change management is the new name of the game. By adopting these 10 steps, you'll have a greater chance of harnessing the energies and enthusiasm of your people, removing misunderstandings, and working with the brain's natural resistance to change. While AI adoption is underpinned by deep technology, the rewards will only be reaped by treating it as an organizational and behavioral change program.
Time of India
15-05-2025
- Entertainment
- Time of India
Elon Musk's dancing robot Optimus impresses Shopify CEO but internet's worried about dish-washing
Elon Musk 's Optimus robot is back in the spotlight — and this time, it's dancing like nobody's watching. A video of the humanoid robot grooving to upbeat music has left the internet awestruck, questioning whether it's real or a next-level AI illusion. It isn't. It's very real, and even Shopify CEO Tobi Lütke couldn't help but react. While many praised the robot's smooth moves, others had more pressing questions — from retail inventory capabilities to whether robots will soon need branded shoes. The viral video shows the robot effortlessly pulling off choreographed steps, prompting many to marvel at the tech behind it. Tobi Lütke, CEO of Shopify, reacted with awe, suggesting we've reached a significant milestone in tech evolution. — tobi (@tobi) by Taboola by Taboola Sponsored Links Sponsored Links Promoted Links Promoted Links You May Like The Cost Of Amusement Park Equipment From Mexico Might Surprise You Amusement Park Equipment | search ads Click Here Undo The internet's mind was blown The internet was collectively stunned by the fluidity of Tesla's Optimus robot's dance moves. One simply noted that the robot dances better than most people. A Tesla AI engineer pointed out the technical difficulty behind robot dancing, explaining how it challenges precision, timing, and real-time AI inference. Some users looked ahead, predicting an exciting decade for robotics. But beyond the applause, curious minds had funny questions. One user asked the robot to "go wash the dishes" while another wondered if the robot's abilities could extend beyond the dance floor—perhaps to complex tasks like managing retail inventory of oddly shaped packages, joking that they'd need to know their savings goal accordingly. Others had quirky realizations—like the fact that robots might soon need shoes, and perhaps even have brand preferences, sparking a humorous thought: will we one day need to budget for our robot's wardrobe?
Forbes
23-04-2025
- Business
- Forbes
The Leadership Challenge Of AI: How Leaders Can Address Fears Of AI
AI And The Leadership Challenge: How Leaders Can Address Fears Of AI The impact of AI continues to be felt in the workplace. Much of the attention has been on either the 170 new jobs created or the 92 million jobs displaced by AI by in 2030. While understanding the impact of AI on the future of work is important, leaders also need to understand the human side of AI. This means listening to the concerns and fears of workers as AI expands across the organization. It is these human challenges which change workflows, job expectations, the composition of work teams, and are causing fears and concerns among workers as AI expands in the workplace. I recently spoke at the Ecosistema Formazione Italia – EFI in Rome, a conference bringing together 2,000 senior leaders in HR, Talent, and Training from European countries, to share their views on innovations in the workplace. It did not take long before the discussion focused on how workers and leaders were adapting to the expansion of generative AI at work. Five themes emerged which focus on the importance of developing a human centered AI adoption strategy where leaders understand worker concerns and take action to create an optimal human and AI collaboration. Attendees at the Ecosistema Formazione Italia (EFI) expressed concerns about the implications of using AI at work. One of the biggest concerns around using AI in your work is identifying how much of your job can be automated and how this might result in job loss. This fear of being replaced by AI is behind the Slack research of 5,000 knowledge workers found that 20% were using AI at work but not disclosing this to their team or manager. Around 55% use AI at least a couple of times a week, but 74% don't actively share about their use or encourage others to use AI. The reason: fear of being displaced by AI once your manager sees how much of your job can be automated by AI. While one could argue this fear may not be based on reality, it is a human reaction to exponential change in the workplace and must be addressed by leaders. To successfully manage how humans and AI work together, leaders need to understand the cultural change, and emotional intelligence of the workforce. The second fear discussed was the possible consequences of not using AI and how that may lead to either stalled careers or can impact one's performance review. In the case of Shopify, the expectation of using AI daily is a baseline expectation at the company. A recent memo Shopify CEO Tobi Lütke sent to the Shopify workforce asks this question of his workforce: 'What would your function look like if autonomous AI agents were already part of the team?' Lütke elaborates by saying that before asking for more headcount or resources, teams must show why they cannot get what they want done using AI. Finally, Lütke shared that AI usage will be factored into all performance reviews at the Shopify. By doing this, Shopify is moving from encouraging employees to 'tinker with AI,' to changing how they work in partnership with AI. What are your strengths in a world where AI is universally available? This was an interesting question posed at the Ecosistema Formazione Italia (EFI) and very relevant for those in the content creation/communications area. What if your strengths are writing clearly and concisely? If AI creates your first draft, what strengths do you develop to 'stand out' on your team? Using generative AI to create a first draft is now so pervasive that it's made its way into AI products, including as core feature in Copilot for Microsoft 365. In a world where AI is universally available and there is an expectation for daily AI usage, workers need to focus on developing skills in human creativity, emotional nuance, strategic thinking, and importantly, AI oversight of the output. Shopify CEO Tobi Lütke not only talks about AI in weekly videos, town halls, and podcasts, but last year he used AI agents to create a talk and then presented about his experience. This needs to happen for all CEOs: usage by example and then share the results. Since usage of AI is the most rapid shift to how work is done, this is a skill that all workers need to develop and that will only happen if it becomes integrated into the workflow. Building transparency is key to widespread adoption of AI. While understanding how to use AI involves self-learning, leaders must create a culture of sharing back what you learned, and how this impacts your job, your team, and the organization. Leaders can do this by including AI integration into monthly business reviews and product life cycles. Employees should be encouraged to share their best practice use cases on Slack and Teams. As trust is a key barrier to widespread adoption, leaders will need to empower their teams to learn when to trust AI and how to validate or challenge outputs to ensure final decisions align with the strategic goals of the organization. In addition, transparency of using AI needs to be celebrated for all employees, starting with the CEO and leadership team. The speed at which today's technology is changing means that leaders will have to consistently re-evaluate and optimize what can best be accomplished by humans, by AI agents, and by humans and AI working together. Finally, one of the interesting questions for leaders is how will the partnership between HR and IT evolve as generative AI expands across enterprise? Moderna is taking this to a new level by integrating IT into the HR function and re-framing its top HR leader as Chief People and Digital Technology Officer. This is an early example of how AI is starting to be viewed not just as a technology tool but as a co-worker where humans and AI evolve their work together. What is your company doing so humans and AI collaborate in as seamless a way as possible?
