Unlocking AI’s Promise: A Practical Playbook to Unlock AI's True Potential

AI is no longer a novelty. Leaders must unlock AI's true potential and translate tools into real business value. This post explains what executives should change in operating models, how to prepare people, how safety and ethics shape adoption, and why environmental risks now belong in strategic planning. The goal is to make AI adoption concrete, practical, and resilient.

## Why leaders must unlock AI's true potential

Transforming technology without changing the operating model is like buying an engine and leaving it in the trunk. IBM senior VP Ana Paula Assis argues that businesses must rethink operating models to realize AI’s full value. Therefore, leaders should move beyond proof-of-concept pilots and embed AI into workflows, governance, and financial incentives. Additionally, this means aligning incentives so teams measure outcomes, not just models built. For example, product, data, and operations teams need shared KPIs that reward safe, repeatable value delivery.

However, this is not only a tech or IT problem. It is a strategic change that touches hiring, procurement, and risk processes. Companies should start by mapping high-value use cases and then redesign processes around them. Furthermore, investment decisions should be staged: build foundational data and tooling, then layer governance and domain-specific models. This approach reduces wasted spend and speeds time to measurable results.

Impact is clear: companies that change operating models can convert experimental AI into regular earnings and improved customer outcomes. Over the next few years, this shift will separate companies that merely adopt AI from those that capture its full benefits.

Source: Fortune

How to unlock AI's true potential for people at work

AI adoption often raises fears about deskilling. However, Boston Consulting Group’s chief AI ethics officer, Steven Mills, points to a different outcome: a “virtuous cycle” where AI can boost job satisfaction and efficiency. Therefore, companies that focus on augmenting human work tend to see better employee outcomes. Additionally, Mills emphasizes education: “We also think it’s really important to educate people about the tech.” Training helps workers trust tools and use them effectively.

Practically, organizations should invest in role-specific learning and change management. Start by teaching employees what AI can and cannot do, and then pair training with hands-on projects where teams co-design AI-augmented workflows. This builds trust and reveals real improvements in day-to-day work. Moreover, measurement matters: track both productivity gains and employee sentiment to ensure adoption is humane and sustainable.

From an enterprise perspective, this human-centered approach reduces resistance and improves ROI. Companies will see faster uptake when they treat AI as a partner rather than a replacement. Therefore, investing in education, clear governance, and participatory design is a short-term cost with long-term benefits. The outlook is hopeful: AI can improve jobs, not just replace tasks, if organizations commit to learning and ethical deployment.

Source: Fortune

Hiring for what helps unlock AI's true potential

In a world of AI-curated resumes, Jeff Bezos’ favorite interview question highlights what machines struggle to copy: entrepreneurship and innovation. Therefore, hiring should prioritize traits that resist automation. Candidates who can invent, take initiative, and navigate ambiguity are harder to replace with models. Additionally, these traits help teams turn AI tooling into differentiated products and services.

Practically, recruiters and managers can design interviews and assessments that surface agentic behavior: ask about self-started projects, examples of testing new approaches, and lessons learned from failed experiments. Moreover, performance frameworks should reward initiative and invention, not just process adherence. This encourages employees to use AI as a lever for new ideas rather than just a productivity hack.

However, organizations must balance this with reskilling. Not everyone will arrive with entrepreneurial experience. Therefore, offer programs that teach creative problem solving, cross-functional collaboration, and rapid prototyping. Over time, this combination of selective hiring and broad reskilling will create teams that both adopt AI tools and invent new uses for them.

Impact: companies that hire and grow creative talent will extract more strategic value from AI. In short, prioritize people who can ask the right questions and build what AI can’t yet foresee.

Source: Fortune

Autonomy, safety, and where AI fits in mobility

Autonomous driving firms face a core truth: perfection is impossible, but large safety gains are achievable. WeRide’s CEO Tony Han says self-driving systems cannot guarantee 100% safety—but they could be 10x safer than human drivers within the decade. Therefore, companies planning mobility services should expect a gradual transition: incremental deployments, strong monitoring, and layered safety measures.

For enterprise leaders, this has several implications. First, regulatory and insurance landscapes will evolve; plan for shifting compliance needs and capture opportunities in fleet optimization and route efficiency. Second, operational models must prioritize staged rollouts with human oversight, redundancy, and continuous learning loops. Additionally, partners and vendors will matter: choose suppliers who demonstrate safety-tested data and transparent validation.

However, enterprises should not wait for perfect autonomy. Instead, they can pilot semi-autonomous features that already improve safety and costs. Over time, these steps will build data and trust needed for broader deployments. Therefore, mobility leaders should combine engineering rigor with practical deployment strategies to capture early benefits while managing risk.

In sum, autonomy will reshape transportation economics, but only firms that integrate safety-first design and phased adoption will profit without overexposing themselves to regulatory or reputational damage.

Source: Fortune

Climate costs, health risks, and why enterprises must price environmental impact

A new study warns that deaths from air pollution could cost Southeast Asia nearly $600 billion by 2050, and climate change may be partly to blame. Therefore, executives with supply chains, customers, or operations in the region must reassess risk exposure. Additionally, this is not just a social issue; it is a material business risk that can influence labor availability, productivity, and regional demand.

Companies should begin by mapping geographic risk to operations and workforce health. For instance, plants in high-pollution areas may face higher absenteeism and hiring challenges. Moreover, investors and customers increasingly expect companies to disclose environmental impacts and to integrate climate resilience into strategy. Therefore, factoring pollution and climate-driven health costs into scenario planning will lead to better capital allocation and continuity planning.

However, action can also create advantage. Firms that invest in cleaner operations, community health programs, or resilient logistics can reduce long-term costs and build local goodwill. In short, treating air pollution and climate change as strategic risks—rather than externalities—helps leaders protect both people and profit.

Source: Fortune

Final Reflection: A Unified Playbook for the AI Era

These five stories point to a single conclusion: unlocking AI's promise requires simultaneous work on models, people, safety, and planetary risk. Therefore, executives should treat AI adoption as a full transformation—one that changes operating models, reskilling plans, hiring practices, and risk management. Additionally, ethical and educational investments will determine whether AI becomes a tool for uplift or a source of disruption.

Over the next decade, leaders who align incentives, train their workforce, and integrate safety and environmental risks into planning will capture outsized value. However, this requires patience, clear governance, and a willingness to redesign old processes. The good news is that practical steps are available now: prioritize high-value use cases, invest in people, pilot safely, and price systemic risks. Ultimately, companies that act will not only unlock AI's true potential—they will also build more resilient, humane, and sustainable businesses.