From Operator to Supervisor: How AI Changes Your Job from Doing the Work to Approving It

Executive Summary

AI is rapidly transforming knowledge work by automating routine tasks and generating first drafts or analyses, thereby shifting human roles from performing the work to supervising and refining it. Instead of spending days on manual execution, professionals increasingly find themselves approving, guiding, and correcting AI-generated outputs. This evolution in roles promises significant gains in productivity and efficiency while allowing employees to focus on high-level decisions and creative problem-solving.

Early data indicate that adopting AI tools can significantly enhance productivity and reduce tedious tasks. For example, in multiple studies, generative AI increased users’ work output by an average of 66%, with customer support agents handling ~14% more inquiries per hour and business professionals producing 59% more documents per hour. Positive effects, including increased employee satisfaction, accompany these productivity gains. In one survey, 81% of workers using AI reported that it improves their productivity, and they experienced 22% higher overall job satisfaction compared to non-users. By offloading tedious tasks, AI gives employees more breathing room and reduces stress, which can lower burnout rates over time.

This shift from “operator” to “supervisor” is especially evident in office-based and professional services roles. Marketing teams use AI to draft content and analyze data, accountants rely on AI for bookkeeping and compliance checks, lawyers leverage AI for document review and research, and customer support reps employ AI chatbots as a first line of response. Across these fields, the human worker’s job is evolving into oversight, quality control, and strategy. For instance, nearly 90% of marketers have now utilized generative AI tools, and 85% of those report that it has increased their productivity. In law firms, up to 74% of billable tasks (e.g., information gathering, drafting) are identified as automatable by AI, allowing lawyers to focus on high-level advisory work and client relationships.

The Rise of AI Means Business Leaders Must Rethink How Work Gets Done – Immediately. Old assumptions that more hours equal more output are being upended. AI can produce results in seconds that used to take employees days. Nearly all executives (99%) plan to invest in AI within the next year, and 97% feel a sense of urgency to integrate AI into their operations. CEOs and owners should recognize that competitive advantage now lies in how effectively they empower their teams with AI and redesign workflows to leverage AI supervision. Lagging behind isn’t an option; failing to embrace this change could leave your company at a serious productivity and cost disadvantage.

Mid-sized firms often lack the in-house talent to develop custom AI tools; therefore, leaders should be proactive in selecting the right partners to build these solutions. This means finding AI vendors or consultants who understand your industry and can tailor systems to your unique processes (a *“tailor-made” solution rather than one-size-fits-all). The right AI partner will possess deep technical expertise, a proven track record of successful implementations, and a collaborative approach that incorporates your team’s input. By partnering strategically, CEOs can quickly pilot AI in critical areas and scale successful solutions company-wide.

Introduction: From Doing the Work to Overseeing the Work

Artificial intelligence has moved into the workplace as a powerful “co-worker” that can handle an increasing share of routine knowledge tasks. Rather than replacing humans outright, today’s AI excels at executing specific tasks, such as drafting reports, sorting data, and answering common questions, at superhuman speed and scale, albeit with human direction. This creates a new dynamic where the AI does the heavy lifting and the human provides guidance, oversight, and final approval. For example, a manager might have an AI generate a first draft of a proposal or code, then spend their time reviewing and refining it for accuracy and tone.

The fundamental nature of many jobs is shifting from operator (hands-on task doer) to supervisor (quality controller and decision-maker). Employees are increasingly becoming AI supervisors – whether that means a marketer curating AI-generated ad copy, an analyst validating AI-produced insights, or an attorney checking an AI’s first draft of a contract. This oversight role leverages human strengths (judgment, ethics, creativity) while allowing the AI to handle repetitive or brute-force work. One survey of AI researchers noted this complementary pattern: pairing human decision-making with AI execution yields a “productivity amplification” effect rather than a replacement dynamic, meaning humans and AI together outperform what either could do alone.

For C-level executives, this transition presents an opportunity to increase the output of their teams without proportional increases in headcount. AI doesn’t need breaks and can work 24/7 on well-defined tasks, allowing projects to move faster. Meanwhile, your talented employees can focus on what really drives value – innovation, client service, strategic planning – instead of getting bogged down in busywork. The oversight model also means higher consistency and fewer errors, as AI can be trained to follow best practices and humans catch any mistakes it misses. In short, AI enables businesses to accomplish more with less and do it better by reallocating human effort to where it has the most impact.

AI as a Productivity Booster (and Burnout Antidote)

Multiple recent studies show remarkable productivity gains when employees leverage AI. In controlled experiments, providing workers with access to generative AI (such as ChatGPT) has significantly reduced the time required to complete tasks and improved the quality of output. In one example, customer support agents assisted by an AI chatbot resolved nearly 14% more inquiries per hour than their peers, without sacrificing quality. Across various professions, similar results are observed: business professionals produce 59% more written content per hour with the aid of AI, and software developers using AI coding assistants complete over twice as many tasks in the same amount of time. These numbers translate to huge efficiency boosts for organizations, effectively doing in minutes what might have taken hours, which is especially valuable for mid-sized firms looking to maximize output from lean teams.

A less quantifiable but critical aspect of AI-driven efficiency is how it frees employees from mundane tasks, allowing them to redirect their time to more meaningful activities. In a recent workforce survey, over half of employees (51%) expressed excitement about AI’s potential to automate routine, time-consuming tasks, and 44% were excited about AI’s ability to reduce their overall workload. When asked what they would do if AI saved them time, 49% of workers said they would invest the freed-up time in their own wellness or work-life balance activities. Others said they’d focus on skill development (56%) or tackle more high-value projects (53%) that they currently can’t get to. This suggests that AI can mitigate burnout by alleviating the grind: employees can spend more time on rewarding, strategic, or creative work and less on mentally draining busywork.

Early evidence suggests that when implemented thoughtfully, AI tools can indeed reduce workplace stress rather than exacerbate it. Workers who use AI report higher job satisfaction and engagement on average. Two-thirds of U.S. employees agree that using AI tools will improve their work-life balance by handling tasks that often spill into overtime. Generative AI has even been noted to help reduce cognitive load – for instance, drafting emails or summarizing reports, so employees don’t have to juggle as much low-level detail mentally. By serving as a digital assistant, AI enables people to leave work on time more frequently and expend less energy on tedious tasks, which in turn mitigates the risk of burnout. (Notably, employees are somewhat divided on this issue – in one poll, 38% believed AI would decrease workloads and prevent burnout, while 45% feared it might increase workloads if misused. This highlights that the outcome depends on implementation: companies that integrate AI to genuinely lighten employee workloads and train staff properly are likely to see stress go down, whereas dumping AI on workers without support could have the opposite effect.)

Another way AI can reduce stress is by catching errors and automating monitoring tasks. AI systems can watch data or processes in real-time and flag issues, essentially acting as tireless sentinels. In fields such as finance or IT, AI tools can continuously monitor for anomalies (such as fraudulent transactions and security threats), ensuring that fewer crises are presented to employees at the last minute. By improving accuracy and consistency in routine work – for example, using AI to check data entry or proofread documents – employees experience less of the “fire-fighting” mode of fixing mistakes after the fact. This preventative assistance means a calmer, more controlled workload. In short, AI provides your team with both a safety net and an accelerant: work gets done faster and with fewer human errors, which reduces the last-minute scrambles and long nights that often lead to burnout.

Roles and Sectors Most Affected by the AI Oversight Shift

Nearly every knowledge-based role is touched in some way by AI’s rise, but certain office-related and professional service jobs are seeing especially big shifts. In the U.S. and globally, the adoption of AI is widespread in roles that involve extensive information processing, communication, and documentation – the core of office work. As of 2024, about 20–40% of U.S. workers are already using some form of AI on the job (with adoption skyrocketing). Below, we highlight a few key domains and how AI-driven task automation is changing the daily work in each:

• Marketing and Creative Services: Marketing teams have embraced AI to supercharge content creation, research, and campaign management. Generative AI tools are writing social media posts, drafting blog articles, creating basic designs, and even generating video drafts. Adoption is very high – a late-2024 survey by the American Marketing Association found nearly 90% of marketing professionals have used generative AI, with 71% using it at least weekly and 19% using it daily. Crucially, marketers say this is making them more effective: 85% of marketers who use AI report that it has increased their productivity, often by handling the first draft of copy or automating A/B testing, allowing them to focus on strategy and creative refinement. For example, instead of manually writing dozens of product descriptions, a marketing manager can have an AI generate them, and then spend her time curating the best ones and ensuring the brand's voice, significantly speeding up the content pipeline. Creative agencies similarly use AI tools for initial design concepts or image generation, with designers then iterating and approving the final output. The result is a shift towards a supervisory creative director role: the human sets guidelines, the AI generates options, and the human editor selects or refines the winning idea.

• Accounting and Finance: In accounting departments and firms, AI is reducing time spent on data entry, reconciliation, and number-crunching analysis. Machine learning algorithms can automatically categorize expenses, detect anomalies in financial statements, and even draft portions of audit reports. For instance, big accounting firms have developed AI assistants (like Deloitte’s “DARTbot”) to scan contracts or financial documents at lightning speed, extracting key terms or flagging compliance issues for human accountants to review. This means an accountant’s job is evolving: less calculator and spreadsheet work, more oversight and advisory work. They review AI-generated analytics for accuracy and focus on interpreting the results for business insights. The volume of transactions one accountant can handle increases, which is a boon for mid-sized companies with lean finance teams. Moreover, AI-driven fraud detection and compliance monitoring run continuously in the background, so finance professionals can proactively address issues rather than react after a quarterly review. In practical terms, an accounting manager might spend mornings reviewing automatically generated cash flow analysis and afternoons strategizing on budget decisions – a far cry from the old days of poring over receipts. By minimizing grunt work, AI enables finance teams to focus on strategic forecasting, risk management, and providing business advice to clients or executives.

• Legal Services: The legal sector provides a clear example of the transition from operator to supervisor. AI tools are now capable of reviewing large volumes of legal documents (contracts, case law, evidence) much faster than junior lawyers or paralegals. Over the past year, AI adoption in the legal industry has surged – one industry report shows that the usage of AI by legal professionals increased from 19% in 2023 to 79% in 2024. Why the surge? AI legal assistants can identify relevant case precedents, suggest contract clauses, and verify for inconsistencies or risks in documents in a fraction of the time it would take a human to do so. The Clio Legal Trends Report noted that nearly three-quarters of a law firm’s billable tasks are potentially automatable with AI, particularly routine tasks such as information gathering and initial drafting. Lawyers are thereby freed up to focus on the parts of legal work that truly require human expertise – negotiating deals, formulating legal strategies, appearing in court, and providing clients with personalized counsel. In effect, attorneys become managers of AI outputs: for example, an AI might produce a first draft of a contract, and the attorney’s role is to carefully review that draft, apply judgment on what clauses to tweak, and then approve the final document for the client. The Clio report emphasizes this point, stating that automation gives firms “space to focus on tasks that require a human touch – like high-level legal work, advocacy, and client relationships”. The upshot is a more efficient legal practice: lawyers can handle more cases or clients because their time is no longer eaten up by low-level paperwork. Importantly, clients are also on board with this shift (in the survey, 70% of legal clients were neutral or preferred firms that use AI), as long as it means faster service and maintains quality. For law firm leaders, this trend necessitates rethinking traditional billable-hour models, as AI-powered efficiency can reduce the hours required per task, indicating that value in legal services will be measured by expertise and outcomes, rather than hours logged.

• Customer Service and Support: Perhaps no office role has felt the impact of AI more quickly than customer support. AI chatbots and virtual assistants now handle a substantial portion of frontline customer inquiries across various industries. Whether it’s a tech company’s help center or a bank’s customer hotline, AI-driven agents address common questions, process simple requests, and even triage issues by collecting details before a human steps in. The result is that human support representatives primarily deal with more complex or sensitive cases, and even then, they often have AI tools providing them with suggested responses or knowledge base articles in real-time. Studies show this AI augmentation can dramatically raise a support team’s throughput. One field experiment at a Fortune 500 company revealed that novice customer service agents became as effective as more experienced agents when they had an AI assistant guiding their responses – they solved issues faster and with higher customer satisfaction. In general, when AI handles routine FAQs, human agents have more time to empathize and solve tough problems for customers, improving service quality. The oversight theme also emerges here: a support manager might supervise an AI chatbot’s performance, reviewing transcripts of bot-human interactions and tweaking the bot’s behavior or knowledge base when the AI provides an unsatisfactory answer. This is a new kind of supervisory responsibility that didn’t exist a few years ago. Additionally, customer support managers can utilize AI analytics to track overall service trends (e.g., identifying a surge in a specific complaint type) and proactively address product issues or training needs. The big picture is that AI is taking on the “level 1” support tasks, elevating human support roles to orchestrate the customer service experience, handle exceptions, and ensure that the AI meets quality standards.

• Other Professional Services: Beyond these examples, virtually all consulting and professional advisory fields are seeing similar shifts. Management consultants utilize AI to analyze large datasets or conduct industry research in minutes, allowing them to spend more time crafting recommendations and interacting with clients. Real estate firms use AI to analyze property data and valuations, enabling agents to focus on client relationships and negotiations. HR departments deploy AI for initial résumé screening and even preliminary interviews via AI avatars – thus, HR staff focus on final-round interviews and cultural fit assessments. Even fields like architecture and engineering are leveraging AI for drafting designs or running simulations, with professionals then adjusting and approving the results. In all cases, the pattern is consistent: AI handles time-consuming sub-tasks (often in the background or as an assistant), and the human worker’s role is elevated to planner, reviewer, and strategist. This is occurring in mid-sized U.S. companies, just as it does in large enterprises. Crucially, it’s not limited to tech companies – traditional sectors (from law to marketing to finance) are all on this trajectory, meaning everybusiness leader in professional services should be aware of how their people’s day-to-day jobs could be redesigned around AI collaboration.

The CEO Mindset Shift: Embracing AI-Augmented Workflows Urgently

For CEOs and business owners, the advent of AI in the workplace isn’t just a tech upgrade – it’s a paradigm shift in how to think about work and productivity. Leaders must pivot from the classic mindset of human labor as the default unit of work to a new mindset where human-AI collaboration is the norm. This means recognizing that many tasks should no longer be done manually from scratch. Instead, the question becomes “Has AI done an initial pass on this?”or “Which parts of this project can we automate or accelerate with AI?”before a human touches it. As one example, consider content creation: a traditional mindset might assign a team member to write a 10-page report over two weeks; an AI-augmented mindset asks the AI to draft the report in minutes, then has the team member spend a couple of days editing and refining it. The new approach fundamentally redefines job roles, timelines, and resource allocation.

A critical mindset shift is moving away from measuring work by hours spent or keystrokes made. AI can accomplish specific outputs with minimal human input, so the value contribution of employees will be measured more by outcomes and oversight quality than by sheer effort. For leaders, this might feel like a loss of control at first (since you can’t “see” the AI working like you see a person at a desk), but it requires trust in the technology and new metrics for performance. For instance, a client report produced primarily by AI in 2 hours could be just as valuable (or more so) than one a consultant toiled over for 2 days – the difference is that the consultant’s expertise was applied in guiding the AI and polishing the result. CEOs should start redefining KPIs and incentives to account for productivity that is achieved in partnership with AI. This could involve tracking how effectively teams integrate AI into projects, or measuring the quality of final deliverables and client satisfaction, rather than focusing on time spent. In essence, leaders must reward smart leverage of AI, not just manual effort.

The competitive landscape is shifting fast. Nearly all executives now say they are “all in” on AI, with 99% of surveyed executives planning to invest in AI in the coming year and the vast majority prioritizing significant investments. This overwhelming consensus among business leaders underscores that AI adoption is no longer a “nice to have,” but an imperative. Moreover, the timeline is critical – those who move faster can gain an edge in efficiency and cost structure over those who lag behind. Executives are even ranking AI innovation as their top concern, ahead of economic or political issues. The message is clear: if you, as a CEO, don’t embrace AI-driven work models, your competitors will, and they will be able to deliver faster, cheaper, and possibly at higher quality. We are at a juncture similar to the early days of the Internet or the mobile revolution. Except this time, the change is happening on an even faster scale, with some surveys showing global AI adoption in workplaces doubling within a year. U.S. businesses, in particular, have no time to waste, given the rapid adoption of generative AI tools across the economy in 2024 and 2025.

Changing your mindset is one thing; bringing your organization along is another. Leaders should anticipate some employee skepticism or anxiety around AI (e.g., fears of job loss, or workers unsure how to use it). It’s the CEO’s job to set the tone that AI is an enabler, not a threat – a tool that will make the team more competitive and work lives better, not a replacement for their jobs. Part of the mindset shift involves a cultural change: encouraging experimentation with AI, celebrating wins where AI has helped achieve a goal, and explicitly stating that using AI is expected and valued. Interestingly, surveys find that many workers are actually eager to become more skilled in AI; 76% of desk workers reported feeling a sense of urgency to become proficient in AI tools. However, other findings show that nearly half of workers would be uncomfortable admitting to their boss that they used AI for a task, fearing it might be seen as cheating or laziness. This reveals a potential stigma that leadership must erase. Executives need to normalize AI usage by framing it as the modern way of working (just like using computers or the internet is not optional). When the CEO openly advocates for AI-assisted workflows and provides training, it empowers employees to adopt these tools without fear. In summary, an urgent mindset shift means leading from the front: embracing AI in your workflow, communicating a bold vision of an AI-augmented organization, and rapidly adjusting policies to support that vision.

Ultimately, leaders should be prepared to redesign specific roles and teams as AI becomes more prevalent. If AI can handle 50% of what a junior analyst used to do, how will you redeploy that analyst’s talent? Perhaps they can now manage two projects simultaneously, or focus on client interactions while AI handles data work. You may find you need new roles – for example, an “AI systems manager” or “automation lead” in each department, who oversees the AI tools and coordinates between tech teams and business teams. Departments might collaborate differently (e.g., marketing and IT working closely to fine-tune an AI that personalizes customer outreach). Being open to reorganizing and reskilling staff is part of the mindset change. The end goal for the CEO is a nimble, AI-empowered organization where people and machines each do what they do best. Getting there will require decisiveness, investment, and the willingness to break old molds, which is why the CEO’s mindset and sense of urgency are so crucial right now.

Finding the Right Partners to Build Custom AI Solutions

Mid-market professional service companies ($5–$ 50M in revenue) often have limited internal AI expertise. While off-the-shelf AI tools (for text generation, chatbots, etc.) can be a good start, gaining a competitive edge may require custom AI solutions tailored to your specific business processes or client offerings. This is where partnering with outside experts becomes invaluable. The first step for leaders is to identify the use cases that could most benefit from AI in their business – for example, automating a particular workflow unique to their operations or creating an AI-enhanced service for their clients. Once you have a vision (even a rough one) of what you need, it’s wise to seek out partners who can design and implement that AI solution efficiently. Don’t try to do everything in-house if you don’t have the requisite talent; a failed DIY AI project can waste time and resources. Instead, tap into the growing ecosystem of AI development firms, consultants, and vendors who specialize in bringing AI projects to life for businesses like yours.

Not all AI vendors or consultants are equal; choosing the right partner is crucial. Here are the key criteria and tips for vetting potential AI partners:

• Domain Understanding: Prefer partners who have a deep understanding of your industry or the specific problem area you are addressing. If you run an accounting consultancy, an AI firm with experience in financial data or compliance projects will ramp up faster than one that’s never worked in finance. They should be able to grasp your business objectives, not just the technical aspects. During initial talks, notice if they speak your language and ask insightful questions about your workflow – that’s a sign they can build a relevant solution.

• Technical Expertise and Track Record: Ensure the partner has a strong technical team with a proven track record in AI/ML projects. Ask about similar projects they’ve done. Successful AI implementation requires more than just data scientists – it also needs software engineers, UX designers, and often expertise in cloud infrastructure. One of the most important aspects is the depth of their technical knowledge and experience with the AI technologies in question. Do they have experience with the latest generative AI models or relevant machine learning frameworks? Can they handle your data securely and at scale? Don’t hesitate to request case studies or client references. A credible partner will be proud to showcase past successes.

• Customization and Flexibility: Opt for solutions that fit your needs. You want a partner who will tailor the AI to your business, rather than force-fitting your processes into a generic tool. In other words, look for a tailor-made philosophy. This approach emphasizes creating customized solutions tailored to your specific needs, rather than relying solely on templates or off-the-shelf models. During proposal discussions, determine if they are willing to customize and integrate with your existing systems (e.g., CRM, ERP). A good partner will offer to pilot the solution with your data and iterate based on your feedback, instead of insisting on a black-box product.

• Collaboration and Training: The best AI partners treat engagements as collaborations, not just transactions. They should involve your team in the development process to capture tacit knowledge and ensure the AI system actually fits your users. Additionally, pay attention to whether they provide training and change management support – deploying an AI tool without preparing your staff to use it effectively can doom the project. Top-tier partners often include user training sessions, documentation, and even help in promoting user adoption. Remember, your goal is not just to build a tool, but to have your people use it and trust it. A partner who sticks around to tweak the system and support your team post-launch is highly valuable.

• Ethics and Security: Given the sensitivity of data in professional services (client financials, legal documents, etc.), ensure any AI partner follows strong data security and ethical guidelines. Verify that they have policies for data privacy (especially if using client data to train models), that they avoid biases in AI outputs, and that they can explain how their AI makes decisions (as much as possible). This is both to protect your firm and to ensure you can explain AI-assisted outcomes to clients or regulators if needed. A serious AI partner will be able to articulate how they address these concerns in their development process.

• Scalability and Ongoing Support: Finally, consider the future – can this partner scale the solution if your needs grow? Will they hand over the keys so your internal team can maintain the AI system, or are they offering ongoing support? Clarify whether you will need them for updates or if they will train your IT staff to handle the system. Depending on your preference, you may want a long-term partner who manages the AI as a service, or you may prefer full ownership after the initial build (e.g., the “build once, own forever” approach). Make sure the engagement model and long-term costs align with your strategy.

• Leverage Ecosystems and Alliances: In addition to individual partners, consider tapping into AI innovation ecosystems and alliances. For example, in many U.S. cities, there are AI innovation hubs, partnerships, or incubators (Austin, Texas – where Axiacore is based – has an AI Alliance bringing together businesses and AI experts). By getting involved, you can find trustworthy partners and even collaborate with peer companies on non-competitive AI infrastructure. Mid-sized firms sometimes partner up to co-develop an AI tool that benefits both (sharing costs and data securely). As a CEO, you should encourage your tech leads to network in these circles. Pooling resources or knowledge can help mid-sized businesses compete with larger players in AI capabilities.

• Pilot, Then Scale: When you’ve chosen a partner, start with a well-defined pilot project. Identify a use case with a clear return on investment (ROI), establish success metrics (e.g., reducing processing time by 50% or improving the customer response rate by X%), and conduct a pilot for a few months. Keep the scope manageable. A good partner will help define this. After a successful pilot, you can work with them to scale up the solution or extend AI to other areas of the business. This phased approach ensures that you achieve quick wins and learn lessons before rolling out the broader rollout. Throughout this process, maintain executive involvement – your engagement signals to the partner and your team that this initiative is a priority.

• Contractual and Cost Considerations: Finally, treat the partnership like any strategic investment. Negotiate clear terms on intellectual property (do you own the custom AI code/model?), data usage rights, and performance guarantees, if possible. Pricing can vary widely – some partners charge a flat project fee, others a monthly subscription for an AI platform. Ensure the costs align with your budget and that there is flexibility in case the project scope changes. It’s also wise to include an exit clause or at least avoid getting too locked in, in case the partnership doesn’t meet expectations. The goal is a win-win relationship: the partner helps drive your AI success, and in return, they get a great case study and ongoing business.

By carefully selecting the right AI partner(s), business leaders can accelerate their AI journey and avoid common pitfalls, ensuring a smooth, effective, and tailored transition from “operator” to “supervisor” within their company.

Conclusion: Thriving in an AI-Supervised Future (A Cross-Industry Outlook)

The transition from doing the work to approving the work is not a distant future vision – it’s happening now across industries. U.S.-based professional service companies that adapt to this shift will gain a competitive edge in productivity, agility, and talent retention, while those that resist risk falling behind. The evidence is mounting that AI, when integrated thoughtfully, can be a powerful force multiplier: employees become moreproductive and more satisfied, companies deliver faster and at lower cost, and new levels of innovation become possible by redeploying human creativity where it matters most.

To navigate this change, C-level executives should keep these points front and center:

• AI is your ally, not your enemy – use it to augment your team, not replace it. Shift people into roles where they supervise AI outputs, inject judgment, and handle exceptions. This will maximize output and maintain quality.

• Data and early adopters prove the upside – from 13%+ gains in support productivity to nearly double output in content creation, the wins are real. Additionally, employees are often happier and less burned out when freed from mundane tasks. Communicate these benefits internally to build buy-in.

• All professional domains are affected – whether it’s marketing, accounting, legal, or customer service, expect AI to transform workflows. Look at peers in your industry who are already using AI (the majority likely are, as seen with 79% of legal professionals now using AI or 90% of marketers adopting generative AI). Learn from their examples. The shift is economy-wide.

• Champion a culture of innovation and learning – encourage your team to experiment with AI tools and share knowledge. Provide training so that everyone from executives to new hires is AI-literate and comfortable collaborating with technology. This cultural adaptation is as important as the tech itself.

• Act with urgency and strategic vision – don’t wait to craft an AI strategy. Set clear objectives (e.g., automate X process, improve Y metric by Z% with AI) and pursue them. Nearly every CEO is investing heavily in AI now; being late to this game isn’t an option. At the same time, align AI projects with your business goals and customer needs – use AI where it amplifies your differentiators or fixes your pain points.

• Leverage external expertise – you don’t have to do it alone. Identify the right tech partners or solution providers to accelerate your AI initiatives, especially for custom needs. A well-chosen partner can deliver a solution in months that might take years for your in-house team to figure out. This can leapfrog you ahead of competitors.

As AI continues to advance, we can expect even more tasks to be handled by machines, and the human role will continue to evolve towards higher-level orchestration, relationship-building, and creative design of business strategies. In this journey from operator to supervisor, the companies that thrive will be those that recognize the strengths of both humans and AI and redesign work to capitalize on both. The executives who lead this change proactively, with optimism and responsible implementation, will not only see productivity soar but also build workplaces where top talent wants to stay (because their skills are augmented, not wasted, by technology). In a very real sense, AI is poised to liberate us from the grindstone of routine execution and usher in a new era of innovation and growth, ifleaders have the courage and foresight to adapt. The call to action is clear: it’s time to upgrade your mindset, invest in your organization’s AI capabilities, and guide your team into this new supervisory era of work. Those who do so will approve more work – and better work – than they could ever do on their own, reaping the rewards in the form of a competitive advantage and a healthier, happier workforce.

External sources

Experimental evidence on the productivity effects of generative artificial intelligence

Despite AI enthusiasm, Workforce Index reveals workers aren’t yet unlocking its benefits

Generative AI Takes Off with Marketers

AI Adoption By Legal Professionals Jumps from 19% to 79% In One Year, Clio Study Finds

The Fall 2024 Workforce Index Shows Executives and Employees Investing in AI, but Uncertainty Holding Back Adoption

AI Outsourcing: Tips for Choosing the Right Technology Partner

The productivity effects of generative AI (ChatGPT)

New Research Shows Employees Divided on AI's Impact

The Fed - Measuring AI Uptake in the Workplace

How Midsize Companies Can Compete in AI