Forced to Use AI: The Corporate Mandate Reshaping Every Career
Somewhere inside Amazon's sprawling corporate machine, a system called Clarity is watching. Not watching in the cinematic, red-blinking-eye sense, but in the quiet, spreadsheet-generating, dashboard-populating way that modern surveillance actually works. Clarity tracks which AI tools Amazon's developers use, how often they use them, and whether they are hitting the company's internal target: 80 per cent of developers using AI for coding at least once per week. Managers can see exactly who meets that benchmark. And who does not.
That data feeds directly into performance reviews, promotion evaluations, and career trajectory conversations. At Amazon, your relationship with artificial intelligence is no longer a matter of personal curiosity or professional preference. It is a metric. It is scored. And increasingly, it determines whether you move up, stay put, or find yourself on a performance improvement plan with the exits clearly marked.
Amazon is not alone. Across the corporate world, from Silicon Valley to the Big Four consulting firms, a new orthodoxy is taking hold: AI proficiency is no longer optional. It is the new literacy, the new typing speed, the new “must be proficient in Microsoft Office.” Except this time, the stakes are sharper, the surveillance more granular, and the consequences for non-compliance far more severe. Welcome to the era of the AI scorecard, where your career trajectory may depend less on what you know and more on how willing you are to let a machine help you do your job.
The Scorecard Economy
The shift did not happen overnight, but 2025 and early 2026 mark the inflection point when AI usage moved from encouraged to enforced. The companies leading this charge read like a who's who of global corporate power.
At Amazon, the performance review system known as Forte now integrates self-reported accomplishments with peer and supervisor feedback, producing an Overall Value (OV) score that influences raises, promotions, and the possibility of being placed on a performance improvement plan. The company recently mandated that its approximately 350,000 corporate employees provide detailed lists of their key accomplishments from the previous year. Managers use a three-tiered scale assessing how effectively employees demonstrate leadership principles alongside traditional measures of performance and potential. Matt Taddy, Amazon's Vice President overseeing supply chain optimisation technologies, framed the shift as a move away from measuring success by organisational growth, saying the company wants to “reward impact, execution, and individual productivity.” Within the Supply Chain Optimisation Technologies team, AI adoption is now a required evaluation category. Performance review questions ask employees how they used AI to drive innovation, improve operational efficiency, or enhance customer experience. Managers face even tougher scrutiny: they must show concrete examples of boosting results with AI without adding new hires.
Meta followed with its own declaration. Starting in 2026, “AI-driven impact” became a core expectation baked into every employee's performance review, regardless of role. Engineers, marketers, product managers, and designers are all evaluated on how effectively they use AI to deliver results. Janelle Gale, Meta's Head of People, communicated the change in an internal memo, underscoring CEO Mark Zuckerberg's vision of transforming Meta into an “AI-native” company where proficiency in artificial intelligence is essential for career progression. The company's biannual review platform, Checkpoint, now reassesses employee performance twice yearly rather than once, with AI-driven impact woven into each cycle.
Meta has even gamified the transition. An internal programme called “Level Up” rewards employees with badges as they hit milestones in AI tool experimentation, tracking their progress through dashboards that visualise adoption rates across teams. The company rolled out an AI Performance Assistant tool integrating its internal bot Metamate and Google's Gemini, giving employees multiple AI engines for review preparation. Some employees have already begun using Metamate to draft the very content used in the reviews themselves, a recursive loop that feels distinctly like the future eating its own tail. Meta has also indicated it will provide additional training resources for employees struggling to adapt, and has dangled performance bonuses amounting to up to 300 per cent of base pay for top performers.
Then there is Accenture, which took arguably the most direct approach. The Dublin-based consulting giant began collecting data on weekly logins to its AI platforms from senior staff and sent an internal email to managers and associate directors making it clear: moving into leadership requires “regular adoption” of artificial intelligence. Documents seen by the Financial Times confirmed that weekly login activity is being tracked on platforms including AI Refinery, Accenture's internal AI platform that CEO Julie Sweet has been heavily promoting to investors. Sweet herself warned last September that the company would be “exiting” staffers who could not be retrained, after the firm had already trained 550,000 of its roughly 780,000 employees to use generative AI. Investors, notably, reacted negatively to the aggressive AI adoption push, with Accenture's share price sliding following the policy announcements.
KPMG joined the movement too. Bloomberg reported that from 2026, the Big Four company would assess employees on how well they have met AI objectives during annual performance reviews. The firm had already been tracking how its workers handled AI data from tools like Microsoft Copilot. As Niale Cleobury, KPMG's global AI workforce lead, explained, the monitoring extends across the entire organisation, from senior partners to junior staff. Samantha Gloede, KPMG's global head of risk services, framed it as practical rather than punitive: “Monitoring is not for policing's sake. We need to make sure that all staff are using these tools because that is the best way to do the jobs.”
Even Microsoft, the company that arguably did more than any other to mainstream generative AI through its partnership with OpenAI, turned the lens inward. In June 2025, the company told employees that “using AI is no longer optional.” Managers were asked to include AI usage in performance reviews, and CEO Satya Nadella reportedly warned executives to leave if they did not support the company's AI plans. The message was unmistakable: if the company that built Copilot expects its own workforce to use AI or face consequences, every other company in the world is watching and taking notes.
The Numbers Behind the Pressure
The corporate urgency around AI adoption collides with a stubborn reality: most workers still are not using it.
Gallup's Q4 2025 workforce survey, published in January 2026, found that only 26 per cent of U.S. workers use AI at least a few times per week, while nearly half (49 per cent) report never using AI in their role at all. Daily usage sits at just 12 per cent, up from 10 per cent earlier in the year. The technology sector leads with 77 per cent total AI usage (31 per cent daily), but retail languishes at 33 per cent total adoption. Only 9 per cent of employees reported feeling “very comfortable” using AI tools, and just a quarter said their employer had clearly communicated how AI is supposed to be used in their work. Organisational AI adoption has not changed meaningfully either: only 38 per cent of employees said their organisation had integrated AI technology to improve productivity, while 41 per cent reported their employers had not integrated AI at all, and 21 per cent were unsure.
The divide between remote-capable and non-remote roles is also widening. Since the second quarter of 2023, total AI use among employees in remote-capable roles has increased from 28 per cent to 66 per cent, while frequent use has risen from 13 per cent to 40 per cent. Growth has been far slower in roles that are not remote-capable: AI use in those positions has increased from 15 per cent to just 32 per cent. Leadership also skews the numbers. In Q4 2025, 69 per cent of leaders said they use AI at least a few times a year, compared with 55 per cent of managers and only 40 per cent of individual contributors. The people most likely to set AI adoption policies are also the people most likely to already be using AI, creating a perception gap that colours every mandate they issue.
The gap between leadership enthusiasm and employee reality is equally stark at the strategic level. McKinsey's January 2025 “Superagency in the Workplace” report, based on surveys of 3,613 employees and 238 C-suite leaders, found that C-suite executives estimated only 4 per cent of employees use generative AI for at least 30 per cent of their daily work, when the real number was closer to 13 per cent. While only 20 per cent of C-suite leaders predicted employees would reach that level within a year, 47 per cent of employees said they already had or soon would. The report's bluntest finding: “The biggest barrier to scaling is not employees, who are ready to incorporate AI into their jobs, but leaders, who are not steering fast enough.”
Microsoft's 2025 Work Trend Index, conducted in partnership with LinkedIn and drawing on insights from 31,000 professionals across 31 countries, introduced the concept of the “Frontier Firm”: organisations with comprehensive AI deployment, high scores on a six-part AI Maturity Index, and active use of AI agents. The findings painted a compelling picture of divergence. At Frontier Firms, 71 per cent of workers reported their company was thriving (compared with 37 per cent globally), 55 per cent said they could take on more work (versus 20 per cent globally), and only 21 per cent feared AI would take their jobs (versus 38 per cent globally). The report also introduced the concept of the “Agent Boss,” describing a shift where employees build, delegate to, and manage AI tools to enhance productivity. Eighty-two per cent of leaders said 2025 was a pivotal year to rethink key aspects of strategy and operations, and 81 per cent expected agents to be moderately or extensively integrated into their company's AI strategy within 12 to 18 months.
PwC's 2025 Global AI Jobs Barometer, analysing close to a billion job advertisements from six continents, added another dimension. Productivity growth in industries most exposed to AI had nearly quadrupled since 2022, rising from 7 per cent to 27 per cent. Jobs requiring AI skills offered a wage premium averaging 56 per cent, up from 25 per cent the year before. AI-exposed jobs were growing 3.5 times faster than all other occupations. The skills sought by employers were changing 66 per cent faster in AI-exposed occupations, up from 25 per cent the previous year. Perhaps most strikingly, jobs were growing in virtually every type of AI-exposed occupation, including highly automatable ones, suggesting that the story is more nuanced than a simple narrative of replacement.
These numbers create a powerful narrative for corporate leaders: AI adoption correlates with productivity, wage growth, and competitive advantage. But correlation is doing a lot of heavy lifting in that sentence, and the gap between a macro-economic trend and an individual employee's daily reality remains wide.
When the Score Becomes the Job
The most unsettling aspect of AI usage tracking is not that companies want employees to use new tools. Every technological transition involves some degree of mandated adoption. Organisations once required employees to learn email, to use enterprise software, to embrace cloud computing. What makes the current moment different is the granularity of the surveillance, the speed of enforcement, and the coupling of tool usage with career survival during a period of mass redundancies.
Consider the timing at Amazon. The company's intensified AI monitoring coincided with its largest workforce reduction in 30 years. Amazon cut approximately 14,000 jobs in October 2025, followed by an additional 16,000 in early 2026, bringing the total to roughly 30,000 positions eliminated, the largest in the company's 30-year history. These cuts represented nearly 10 per cent of its 350,000 corporate and technical workforce. CEO Andy Jassy framed the layoffs as a push to reduce bureaucracy and stay nimble, and said on the third-quarter earnings call that Amazon's rapid growth over the past decade had led to extra layers of management that slowed decision-making. Jassy also stated that efficiency gains from AI would “likely cause Amazon's corporate head count to fall in the coming years.” He noted: “We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs.” Meanwhile, the company announced capital expenditures expected to reach $125 billion for 2026, the highest spending forecast among the megacap companies, with much of that investment directed toward AI infrastructure.
Amazon's broader surveillance apparatus provides context for the AI tracking. The company had already introduced a manager dashboard aggregating employee attendance frequency, time spent in the office, and building locations in eight-week increments as part of its strengthened return-to-office policy. Those averaging less than four hours of daily office time are labelled “Low-Time Badgers,” while those with no building access records are classified as “Zero Badgers.” In the warehouse side of the business, the Associate Development and Performance Tracker (ADAPT) system monitors each worker's productivity in real time, tracking gaps in activity and issuing warnings for unexplained breaks, with automatic termination for unreasonable breaks of two hours or longer. The Clarity system for AI tracking, then, is not an isolated experiment. It is the latest extension of a corporate culture that has long believed in the power of measurement.
When AI usage becomes a performance metric in the wake of mass layoffs, the implicit message is impossible to miss: prove you can work with the machine, or you might be the next one replaced by it. Employees inside Amazon have confirmed that the pressure is real. Reports from inside the company describe a culture where “falling behind on AI means falling behind,” with staff interpreting the timing of AI adoption mandates alongside restructuring as a signal that leaner workforce structures are the goal.
The frustration runs deeper than mere anxiety. Some Amazon developers have expressed anger that the company prioritises its in-house AI coding assistant, Kiro, over external models like Anthropic's Claude Code. While Amazon sells access to Claude through its cloud business, internal staff are reportedly encouraged to rely on company-developed tools, particularly when AI usage metrics influence performance reviews. Critics argue that limiting tool choice undermines developer autonomy and could hurt productivity if employees are forced to use systems they consider less capable.
This tension surfaced dramatically when Kiro itself caused problems. In December 2025, engineers allowed the agentic coding tool to make changes that sparked a 13-hour disruption to Amazon Web Services. The AI had decided to “delete and recreate the environment.” It was the second AI-caused incident in months, raising questions about whether the pressure to use internal AI tools might be creating risks rather than mitigating them.
The Autonomy Problem
The research on workplace surveillance is unambiguous about its effects on human behaviour and well-being, and the findings are not encouraging for the AI-scoring model.
A policy primer published in the journal PLOS ONE, examining AI worker surveillance and productivity scoring tools, found that pervasive monitoring reduces worker autonomy, increases stress, and raises the risk of psychological harm. The authors noted that surveillance “works to discipline workers to conform to expected behaviour which can be measured,” and that when workers' autonomy and agency are reduced, so is their capacity for creativity. The paper argued that “the organisation sends a message to its workers simply by the tasks it chooses to monitor,” a point that lands with particular force when the monitored task is AI usage itself. By choosing to track how often someone logs into an AI platform, rather than the quality of the work that platform produces, companies are signalling what they truly value: compliance over competence.
The European Trade Union Confederation (ETUC) has been equally pointed. In its analysis of AI in the workplace, the ETUC warned that AI-driven automation may cause, without appropriate regulations, “job displacement, deskilling, and precarious employment, threatening wages and job autonomy.” The confederation called for trade unions to be empowered to negotiate AI deployment strategies that enhance job quality and productivity while ensuring fairness, worker autonomy, and collective decision-making.
The UC Berkeley Labor Center's research on data and algorithms at work reinforced these concerns. Their analysis found that integration of AI and algorithmic management tools is changing the experience of work across different sectors, with increasing employer capacity to surveil and collect data on workers leading to a growing number of unfair labour practice charges and worker complaints. The report noted that the “almost complete lack of regulation means there are strong incentives for employers to use digital technologies at will, in ways that can harm workers.” Developers are largely free to sell untested systems, the researchers warned, exacerbating harms that “can take the form of work intensification, deskilling, hazardous conditions, loss of autonomy and privacy, discrimination, and suppression of the right to organise.”
There is a deeper philosophical tension here. The entire premise of AI in the workplace is that it should augment human capability, freeing people to do more creative, strategic, and meaningful work. But when AI usage itself becomes the metric, the tool stops being a means to an end and becomes an end in itself. Employees are not being evaluated on the quality of their output or the creativity of their solutions. They are being evaluated on how frequently they log into a platform. The distinction matters enormously. A developer who writes elegant, efficient code without AI assistance is, under these systems, rated lower than a developer who produces mediocre work while dutifully clicking through an AI dashboard.
The confidence dimension matters too. Research has shown that confidence in AI varies significantly across demographics. Baby boomer confidence in AI has dropped 35 per cent, while Generation X confidence fell 25 per cent, according to survey data referenced in reporting on Accenture's policy. The workers most likely to be penalised by AI adoption mandates are precisely those with the most experience and institutional knowledge.
The Legislative Scramble
Legislators are beginning to notice, though the regulatory response remains fragmented and, in many cases, several steps behind the corporate reality.
In the United States, a patchwork of state and federal proposals is taking shape. In Michigan, State Representative Penelope Tsernoglou introduced a bill that would regulate companies' use of artificial intelligence to monitor employees, requiring notification when tracking occurs and limiting certain forms of data gathering. California lawmakers are considering multiple bills, including AB 1883 on workplace surveillance tools and SB 947 on worker protections regarding AI and automated decision systems. Rhode Island's H 7767 proposes a comprehensive statutory framework addressing AI in the workplace, while New York's A 10251 would limit the use of automated decision systems in connection with employment.
At the federal level, the bipartisan AI-Related Job Impacts Clarity Act, introduced by Senators Josh Hawley and Mark Warner, would require certain companies to regularly report on personnel decisions affected by AI. The No Robot Bosses Act, introduced by Senators Bob Casey and Brian Schatz, would prohibit employers from solely using automated decision systems to make employment-related decisions and would require regular testing for discrimination and biases. Casey and Schatz also joined Senator Cory Booker in introducing the Exploitative Workplace Surveillance and Technologies Task Force Act, which would create a task force to study the use and impact of automated decision systems and workplace surveillance.
In Europe, the situation is more advanced but still contested. The ETUC strongly condemned the European Commission's February 2025 decision to withdraw the AI Liability Directive, arguing that without clear liability rules, workers affected by AI-driven decisions would face greater difficulty seeking redress. Colorado's Artificial Intelligence Act, delayed until mid-2026, introduces a risk-based framework in which employment-related AI systems are classified as “high risk,” and it is widely viewed as a bellwether for other states considering similar approaches.
The International AI Safety Report 2026 noted that AI systems can negatively impact human autonomy in several ways, including effects on cognitive skills, how humans develop beliefs and preferences, and how they make and act on decisions. Around 60 per cent of jobs in advanced economies and 40 per cent in emerging economies are exposed to general-purpose AI, though the report stressed that the impacts will depend on how AI capabilities develop, how quickly workers and firms adopt AI, and how institutions respond.
Notably, staff in 12 European countries are exempt from Accenture's policy of factoring AI usage into promotions, as are employees working on U.S. federal government contracts and some specific joint ventures. The geographic variation highlights an uncomfortable reality: the degree to which your AI usage can be tracked and used against you depends in part on where you happen to work and which jurisdiction's labour laws apply.
The Training Gap That Nobody Wants to Talk About
If companies are going to grade employees on AI proficiency, the logical prerequisite is ensuring those employees actually know how to use AI effectively. The data suggests this is not happening at anywhere near the required scale.
McKinsey's “Superagency” report found that 48 per cent of employees ranked training as the most important factor for AI adoption, but nearly half reported receiving minimal or no training. More than a fifth of employees reported receiving minimal to no support whatsoever. The disconnect is striking: organisations are building scoring systems for a competency they have not adequately taught.
Gallup's data reinforced the point. Only 25 per cent of workers said their employer had clearly communicated how AI was supposed to be used in their work. Just 30 per cent reported that their manager provides support for AI usage, yet employees who strongly agreed their manager supported AI use were more than twice as likely to use it frequently. Gallup argued that the growing divide between AI users and non-users points to a “use-case problem,” noting that “lack of utility is the most common barrier to individual AI use.” The issue, in other words, is not that workers are stubborn. It is that many simply have not been shown how AI is relevant to the specific work they do every day.
The McKinsey report identified four employee attitude archetypes toward AI: Bloomers (39 per cent, AI optimists who want to collaborate with companies on responsible solutions), Gloomers (37 per cent, more sceptical and wanting extensive top-down regulation), Zoomers (20 per cent, wanting rapid deployment with few guardrails), and Doomers (4 per cent, fundamentally negative about AI). Even among the sceptics, familiarity was high: 94 per cent of Gloomers and 71 per cent of Doomers reported some familiarity with generative AI tools, and approximately 80 per cent of Gloomers said they were comfortable using generative AI at work. Interestingly, employees outside the United States appeared more encouraged to use AI tools by their organisations. Respondents in India, Singapore, Australia, New Zealand, and the United Kingdom were all more likely than those in the U.S. to report being encouraged by managers, C-suite leaders, and peers to adopt AI.
The problem is not resistance. The problem is infrastructure. When nearly half your workforce reports receiving minimal or no training, and then you tie their career prospects to AI usage metrics, you have not created a meritocracy of machine collaboration. You have created a system that rewards those with prior advantages (technical backgrounds, access to better tools, supportive managers) and penalises those without them.
The gender dimension adds another layer. PwC's AI Jobs Barometer found that in every country analysed, more women than men work in AI-exposed roles, suggesting the skills pressure facing women will be disproportionately higher. If training is inadequate and AI proficiency becomes a promotion criterion, the risks of widening existing workplace inequalities are substantial. The barometer also found that job cuts were more pronounced in larger corporations, affecting mostly entry-level employees. Smaller companies with fewer than 49 employees showed the highest staff retention with a 4 per cent net gain in positions, while larger firms with 501 to 1,000 employees cut 15 per cent of positions.
The Productivity Paradox
There is a final, uncomfortable question that hovers over the entire AI-scoring movement: does mandating AI usage actually improve outcomes?
The evidence is mixed. PwC's data on macro-level productivity gains is compelling, showing industries most exposed to AI experiencing nearly four times the productivity growth of less-exposed industries. Morgan Stanley's survey found productivity increased 11.5 per cent on average across regions and industries. But these aggregate numbers obscure enormous variation at the individual and organisational level.
A survey of 6,000 executives, referenced in reporting on Amazon's internal debates, found that over 80 per cent of companies reported no measurable productivity gains from AI despite billions in investment. McKinsey's report noted that 92 per cent of companies plan to increase AI investments, yet only 1 per cent of leaders describe their companies as “mature” in AI deployment, meaning AI is fully integrated into workflows and drives substantial business outcomes. Forty-seven per cent of C-suite executives surveyed said their organisations were moving too slowly, while 45 per cent felt they were moving at roughly the right pace. The gap between aspiration and achievement is vast.
Some Accenture employees have offered particularly blunt assessments of the tools they are being graded on, calling them unreliable “broken slop generators.” When the tools themselves are imperfect, tracking whether employees use them tells you something about compliance but very little about competence, creativity, or genuine productivity. The security dimension compounds the problem: Worklytics data shows that 57 per cent of employees are pasting sensitive company data into public AI tools, creating unprecedented compliance and data protection risks. Monitoring AI adoption without controlling how AI is used can introduce as many problems as it solves.
Amazon's own experience with Kiro illustrates the risk. The tool caused multiple AWS outages, yet the company continues to push developers toward it and away from potentially more capable external alternatives. The metric, in this case, appears to be serving corporate strategy (promoting internal products, reducing dependency on competitors) rather than employee effectiveness.
This creates a perverse dynamic. If AI tools are genuinely useful, employees will adopt them without coercion because useful tools tend to spread organically. If the tools are not yet useful enough to drive voluntary adoption, forcing employees to use them and then grading them on usage frequency does not make the tools better. It simply creates a compliance regime dressed up as innovation.
What Comes Next
The trajectory is clear, even if the destination remains uncertain. More companies will track AI usage. More performance reviews will include AI proficiency metrics. More promotions will hinge on demonstrated machine collaboration. The question is whether this transition will be managed with the nuance and investment it requires, or whether it will become another blunt instrument of corporate control.
Microsoft's “Frontier Firm” research offers one version of the optimistic case. At companies that have truly integrated AI into their operations, workers report higher satisfaction, more meaningful work, less fear of job displacement, and greater capacity to take on new challenges. The key distinction is between companies that have built genuine AI maturity, including training, clear communication, appropriate tooling, and supportive management, versus companies that have simply added an AI usage checkbox to the performance review form.
The McKinsey report's central insight bears repeating: the biggest barrier to AI's potential is not employee resistance but leadership failure. When 92 per cent of companies plan to increase AI investments but only 1 per cent have achieved meaningful integration, the problem is clearly not that workers refuse to adapt. The problem is that organisations have not created the conditions for successful adaptation. As the report put it, “the issue is not a technological one, but one of governance.”
For individual workers, the immediate calculus is straightforward. Learn to use AI tools. Document your usage. Highlight AI-driven accomplishments in self-reviews. The career risk of being perceived as an AI laggard is real and growing. But the longer-term question, the one that should concern everyone from boardrooms to legislative chambers, is whether we are building a workplace culture that uses AI to genuinely empower human capability, or one that simply measures obedience to a new set of digital overseers.
Nearly half of U.S. workers have never used AI in their jobs. Nearly half report receiving minimal or no training. And yet the companies at the top of the global economy are now tying promotions, bonuses, and job security to AI adoption metrics. The gap between expectation and preparation is not a detail. It is the defining feature of this moment.
The machines are not coming for your job. But the scorecard tracking how well you collaborate with them just might.
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Tim Green UK-based Systems Theorist & Independent Technology Writer
Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.
His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.
ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk
