‘We Must Act Now’: What Leaders Should Actually Do Today

More than 200 economists, AI researchers and technology leaders recently signed the “We Must Act Now” statement, warning that artificial intelligence could reshape the global economy faster than the Industrial Revolution. While the statement calls for stronger institutions and guardrails, it also raises a more immediate question for executives: What should leaders actually do today?

Members of the Senior Executive AI Think Tank believe the answer extends well beyond adopting the latest AI tools. Drawing on decades of experience leading enterprise AI, cloud infrastructure, digital transformation and technology strategy, these experts argue that organizations must rethink work itself—redesigning processes, investing in people and establishing governance before AI scales across the enterprise.

Their advice comes as organizations accelerate adoption. According to McKinsey’s latest state of AI research, AI adoption continues to expand rapidly across industries, with organizations increasingly reporting measurable business impact. Yet the same research also highlights persistent challenges around governance, workforce readiness and risk management, reinforcing the need for thoughtful leadership rather than technology-first decision-making.

The following insights from members of the Senior Executive AI Think Tank reveal a consistent message: Organizations that treat AI as a catalyst for better decision-making, stronger employees and more resilient businesses—not simply a cost-cutting exercise—will be best positioned to thrive as AI transforms the economy.

“We should push back against the hype and the constant pressure to ‘act now before it’s too late.’”

– Paul Freeman, VP of AI and Strategic Intelligence at Test Rite Products

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Measured Adoption Beats Fear-Driven AI Deployment

Few technological revolutions have unfolded without uncertainty, but Paul Freeman, VP of AI and Strategic Intelligence at Test Rite Products, believes business leaders should resist making decisions based on fear or hype. Rather than racing to deploy AI everywhere, Freeman encourages organizations to embrace disciplined experimentation.

“The most important action business leaders should take right now is measured, informed adoption instead of rushing headlong into widespread implementation or heavy guardrails,” he says.

He argues that history provides a useful roadmap. The Industrial Revolution created significant disruption, but it also generated unprecedented economic opportunity because businesses were allowed to experiment, adapt and continuously improve.

“Markets allowed for experimentation, failure and quick adaptation. Companies and workers adjusted, new skills developed and productivity rose,” he says.

Freeman cautions that premature regulation or reactionary decision-making can unintentionally slow innovation, protecting incumbent players while delaying benefits for employees and customers alike.

Ultimately, Freeman believes leaders should tune out sensational headlines and instead build practical experience through controlled implementation.

“We should push back against the hype and the constant pressure to ‘act now before it’s too late.’ Patience and steady experimentation will serve organizations and society better in the long run.”

Values Must Come Before Guardrails

For Yogesh Malik, CEO of Way2Direct B.V., responsible AI begins well before organizations establish governance frameworks or compliance policies. Malik believes many organizations have reversed the proper sequence.

“The most important action is establishing human moral values as the foundation before any guardrail is built.”

Without that foundation, he argues, governance becomes fragile.

“Guardrails without values are just rules, and rules without values break at the edges.”

As enterprises rapidly deploy generative AI, Malik says observability has become just as important as implementation. Leaders need systems that continuously monitor whether AI outputs remain aligned with organizational values instead of assuming models will continue behaving as intended.

For Malik, governance ultimately serves a larger purpose than compliance.

“Improving human understanding, not just human efficiency, has to be the measure we optimize toward.”

Redesign Work Before You Automate It

According to Venkata Kondepati, Manager of Data Architecture and Engineering at Ascentt, the conversation around AI often starts in the wrong place: AI should improve work before it replaces it.

“The most important action business leaders should take today is to redesign work before they automate it,” Kondepati says.

Instead of viewing AI primarily as a cost-reduction tool, he encourages leaders to rethink how work is performed across the organization.

“AI should not simply be used to remove cost from existing processes,” he says. “Leaders should identify where AI can augment judgment, increase productivity and create new roles, then invest in reskilling, governance and measurement around those workflows.”

That redesign requires more than selecting the right technology.

“The companies that win will not be the ones that deploy the most AI tools. They will be the ones that pair automation with workforce transition plans, clear accountability and shared productivity gains.”

Ultimately, Kondepati believes AI’s long-term success depends on trust.

“If AI is treated only as a labor substitute, trust will collapse. If it is designed as a human multiplier, it can expand both business value and opportunity.”

“The most important thing business leaders can do today is commit to using AI to augment their people rather than replace them.”

– Jim Liddle, Entrepreneur, Investor, Advisor and Enterprise AI Strategist

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Invest in People Before Eliminating Positions

Jim Liddle, Entrepreneur, Investor, Advisor and Enterprise AI Strategist, has spent more than two decades building and scaling technology companies from startup through successful exit. His recommendation to executives is straightforward: Invest in people before using AI to reduce headcount.

“The most important thing business leaders can do today is commit to using AI to augment their people rather than replace them.”

For Liddle, that commitment should be demonstrated through action rather than messaging.

“Back that up by funding AI retraining before cutting a single role.”

He notes that many organizations have underestimated the value of institutional knowledge when automating work. In some cases, companies have eliminated experienced employees only to discover their expertise could not be replicated by AI systems.

“There have been too many examples of how companies have let institutional knowledge walk out the door that has not been adequately replaced by AI, to the extent that companies have had to rehire.”

Rather than measuring AI initiatives solely by labor savings, Liddle believes the objective should remain simple.

“Companies should focus on automating the task and investing in the people.”

Make AI Workforce Stewardship a Board-Level Priority

Will Conaway, President of Tuxedo Cat Consulting, believes AI governance should move out of the IT department and into the boardroom.

“Business leaders should make AI workforce stewardship a board-level operating discipline, not a side project,” he says.

In highly regulated industries such as healthcare, Conaway says every AI initiative should be evaluated against multiple dimensions before deployment.

“Map every AI use case to patient safety, clinician workload, privacy, bias and measurable job impact before deployment.”

The same principle extends beyond healthcare. Organizations in every sector should establish clear accountability for AI systems, continuously evaluate performance and ensure employees understand how AI decisions are made and escalated.

“Leaders should fund reskilling as aggressively as automation, redesign roles so AI removes administrative friction rather than clinical judgment and publish clear accountability for model performance, escalation and harm prevention,” he says.

Conaway believes organizations that invest in governance early will create sustainable competitive advantage.

“Companies that build guardrails now can increase productivity, improve care access and retain trust. Those that wait risk unsafe workflows, workforce backlash and value captured by the technology rather than shared with people.”

Treat AI as a Workforce Transformation Strategy

For Dileep Rai, Manager of Oracle Cloud Technology at Hachette Book Group (HBG), AI should never be viewed as simply another enterprise technology project.

“The most important action business leaders should take today is to treat AI as a workforce transformation strategy, not just a technology investment.”

Rather than concentrating solely on automation, Rai encourages executives to redesign roles so employees and AI complement one another.

“The organizations that thrive will redesign work so humans and AI complement each other while continuously reskilling employees for higher-value roles.”

He also stresses that trust depends on transparency. Governance, accountability and clearly communicated AI policies should be embedded into organizational culture from the outset.

“Leaders must establish clear governance, accountability and transparent AI policies to build trust with employees and customers,” he says.

Looking to history, Rai sees a familiar pattern.

“Technology creates lasting value when institutions, skills and incentives evolve alongside it. Companies that invest equally in people, processes and AI will be more resilient, innovative and competitive than those focused solely on automation.”

Build Accountability Into AI Before It Goes Live

Goran Paun, Principal and Creative Director at ArtVersion, approaches AI through the lens of user experience, digital strategy and human-centered design. Before approving any AI initiative, Paun recommends documenting who is accountable when AI produces an unexpected or incorrect outcome.

“I would start one step earlier than most companies do,” he says. “Write down who makes the call when the answer is unclear or the system gets it wrong.”

From there, leaders should redesign work around AI rather than expecting employees to adapt after deployment.

“Determine which tasks can be automated, how the surrounding jobs will change and what employees need to learn.”

Paun notes that many organizations launch AI solutions while leaving reporting structures, escalation paths and performance measures unchanged. Although this may generate short-term efficiencies, it often creates confusion when employees encounter situations requiring human judgment.

“The real guardrail is not another policy document. It is knowing where human judgment matters and building the workflow around it,” he says.

“Be honest about what you’re actually doing with the productivity gains.”

– Sathish Anumula, Enterprise and Business Architect at IBM Corporation

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Match Productivity Gains With Honest Workforce Plans

Sathish Anumula, Enterprise and Business Architect at IBM Corporation, has spent his career modernizing complex manufacturing environments through digital transformation, observability, AIOps and enterprise architecture. His advice centers on one often-overlooked issue: organizational trust.

“Be honest about what you’re actually doing with the productivity gains.”

Anumula argues that many executives publicly promise AI will augment employees while privately budgeting for workforce reductions. That disconnect, he says, is what undermines employee confidence.

“Every leader says AI will ‘augment, not replace.’ Very few have written down what that means for their own headcount plan next year,” he says.

Instead of leaving those questions unanswered, leaders should explicitly determine how AI-generated capacity will be used.

“Does it fund new work, shorter cycles, better margins on existing headcount? Or does it fund a smaller team? Both are legitimate business choices. Pretending you haven’t made one isn’t.”

He also believes reskilling must begin long before organizations face workforce disruption.

“Invest in the retraining before you need it, not as severance-adjacent theater afterward.”

For Anumula, transparent planning—not optimistic messaging—is what ultimately earns employee trust during AI transformation.

Design Work So AI Strengthens Human Judgment

Rishi Katdare, Senior Technology Executive at Amazon Web Services (AWS), believes AI will only enhance human work if leaders intentionally redesign operating models around it.

“AI will not complement human labor by default. Leaders have to redesign work so it does.”

Katdare encourages executives to answer several fundamental questions before automating any workflow: Which decisions should remain human? Which tasks are appropriate for AI? How will accountability change if an AI system produces an incorrect recommendation?

“Before automating a process, leaders should decide which judgments remain human, which tasks move to machines, how roles and handoffs change, what new skills are required and who owns the outcome when the system is wrong.”

Reskilling, he adds, should be “funded against that future operating model, not offered as a generic program.”

Success should also be measured differently. Instead of focusing only on labor savings, organizations should evaluate whether AI improves customer outcomes, decision quality and employee mobility.

Katdare summarizes the challenge succinctly.

“The most important guardrail is work design that makes human judgment and accountability explicit.”

Lead With Adaptability Instead of Certainty

Manpinder Singh Panesar, Senior Solutions Architect at Amazon Web Services (AWS), believes leaders should avoid pretending they can accurately predict how the technology will reshape work.

“The most important action is not to pretend we can predict exactly how AI will reshape work.”

The pace of change is simply too fast, he says, for anyone to forecast every outcome.

“Most leaders will either overestimate or underestimate the pace because they do not fully understand the technology—and very few people truly can.”

Instead, leadership should focus on adaptability. Organizations that stay connected to employees and customers while continuously investing in skills and governance will be better positioned to respond as AI evolves.

“Leadership has never been only about knowing what will happen. It is about knowing how to operate when the situation changes.”

That means maintaining clear priorities regardless of how the technology changes.

“Leaders should act with conviction, stay close to employees and customers, invest in skills and guardrails and remove politics from the response.”

Panesar believes AI should ultimately be evaluated by its contribution to society rather than its novelty.

“The intent should remain simple: Use AI to improve productivity, dignity of work and, ultimately, quality of life.”

The Bottom Line for Business Leaders

• Adopt AI deliberately instead of reactively. Pilot high-value use cases, learn from measured experimentation and avoid letting hype dictate investment decisions.
• Define organizational values before defining AI rules. Governance frameworks are most effective when they reflect clearly articulated human values and desired outcomes.
• Redesign work before automating it. Identify where AI can amplify human judgment, create new opportunities and improve workflows instead of simply replacing existing tasks.
• Fund retraining before reducing headcount. Protect institutional knowledge by helping employees transition into higher-value work as automation expands.
• Elevate AI governance to the board level. Treat workforce impact, accountability, privacy, bias and performance as executive responsibilities rather than technology projects.
• View AI as a business transformation strategy. Invest equally in people, processes and technology to create sustainable competitive advantage.
• Establish accountability before deployment. Clearly define who makes decisions when AI systems encounter ambiguity or produce incorrect outputs.
• Be transparent about productivity gains. Explain how AI-created capacity will be used and align workforce planning with organizational messaging.
• Measure success beyond cost savings. Evaluate AI by improvements in decision quality, customer outcomes, workforce mobility and long-term business value.
• Lead with adaptability. Build organizations that can continuously learn, reskill and evolve as AI technologies change.

The Legacy Leaders Will Leave

The economists behind the “We Must Act Now” statement are right about one thing: AI is advancing at extraordinary speed. In a few years, executives probably won’t be asked whether they adopted AI. That will be assumed. The more revealing question will be what kind of organization they built because of it.

Did AI create a workplace where employees made better decisions, learned new skills and solved bigger problems? Or did it simply make existing processes faster? The technology itself won’t answer those questions. Leadership will. As AI becomes embedded in every business function, the organizations that stand apart will be those that treat human potential as something to amplify—not something to optimize away.