AI readiness for your business: It starts with leadership

AI is changing how work gets done. A lot of thought is being given to which AI tools to use and where to implement them to accelerate workflows. Efficiency is certainly important. But for business leaders to truly leverage the power of AI, they need bigger picture thinking.

AI readiness is about more than new technologies. It is about leadership behavior, organizational alignment and the ability to reimagine value.

Business leaders who want AI to deliver impactful growth need the proper mindset, governance and personnel strategies required to scale it responsibly. Without an AI mindset, businesses risk adopting AI in ways that create uneven productivity, unclear accountability, unmanaged cost and a workforce that is not prepared to work differently.

Keep reading to learn how to develop AI-empowered leadership and how it can help your business experience meaningful growth.

Is your business AI-ready?

To compete in today’s market, AI needs to be a part of your business strategy. But is your organization AI-ready?

To determine that, you and your fellow business leaders need to be answering these questions:

• What conversations are our leaders having about AI’s impact on their business and teams?
• Are our leaders coaching and upskilling employees to work differently with AI?
• How are leaders evaluating which activities should be automated versus those that require uniquely human strengths?

AI readiness depends on enterprise-wide buy-in. If AI remains a tool that individuals use in isolation, your organization is likely missing the broader leadership shift that AI requires.

What is an AI mindset for leadership?

Every time a leader with an AI mindset approaches a new task, strategy or business problem, their first question should be: “How can AI help with this?”

That does not mean you should value AI before people. But leaders do need to challenge old processes, assumptions or definitions of value to determine whether AI can produce better outcomes.

There are two levels to this question:

• Tactical questions: Can AI help us do this faster, better or more efficiently? These help identify practical opportunities.
• Strategic question: Does AI change what we are trying to do, how we create value or what is now possible? These questions are where transformation begins.

Answering the tactical questions can lead to efficiency gains. But answering the strategic question can reveal new operating models, identify services that create new revenue opportunities, improve customer experiences and uncover new ways to improve your business’s position in the market.

How can leaders move beyond an efficiency mindset and focus on true growth?

Many organizations use AI to speed up routine tasks like polishing emails, drafting marketing content, reviewing documents or automating administrative steps. Those are useful applications, but they do not necessarily create growth. It has only added a new layer of technology to old ways of operating.

Efficiency creates real value when the time saved with AI is put towards high-value work.

To capitalize on growth opportunities, leaders need to determine:

• What higher-value work employees should focus on if AI is handling routine work.
• The new services, products or customer experiences that are possible with AI.
• Any processes that should be redesigned to account for AI’s capabilities.
• Are there strategic problems that were previously too complex, slow or expensive that can now be addressed with AI?

These big-picture questions are especially important to evaluate the ROI of your AI investment. As AI models shift toward consumption-based pricing, leaders will need to understand where they’re producing measurable value and prioritize usage that improves throughput, quality, revenue and innovation.

AI training needs to be more than technical

AI training cannot just be a series of tutorials on how to open a tool, write a prompt or use a feature inside a platform. Those skills are necessary, but you should also craft a training program that encourages staff to truly develop and think differently about their roles. That means helping people understand:

• Where they add the most human value
• Which parts of their work can AI support, accelerate or perform
• How to use AI as a thought partner, not just a shortcut
• How to redesign their workflows instead of layering AI onto old processes
• How to apply judgment, context and accountability to AI-generated outputs

This is where leadership matters. If employees continue to do work manually that AI can do faster, cheaper or better, leaders need to examine whether they are coaching their teams effectively. To create AI readiness across the enterprise, leaders need to create expectations, provide development and help teams understand how their value is changing.

The 10x mindset

The 10x mindset forces leaders to think about big AI goals. Instead of thinking about how AI can make incremental improvements to existing processes, leaders ask themselves how they can achieve goals that are exponentially bigger.

For example:

• Instead of having AI help write job descriptions faster, ask how AI could redesign the hiring process so teams spend more time retaining mission-critical talent.
• Instead of asking how AI can analyze customer survey data, use it to create real-time visibility into customer sentiment and help leaders respond instantly.
• Instead of training sales teams to use an AI tool, create AI trainers to deliver entirely individualized, on-demand training at unlimited scale.

The 10x mindset will not always lead to a 10x outcome. The value is that aiming higher forces different questions. But large improvements to your operations will not be made through a series of small adjustments to old systems. Leaders need to set lofty goals to truly capitalize on an AI’s potential to transform workflows, roles, processes and business models.

This is particularly important because AI’s technical capabilities are advancing faster than many organizations’ ability to reimagine how to use them. For many enterprises, the limiting factor won’t be technology, but leadership’s inability to imagine what is possible when AI is integrated into strategy.

What is the employee value gap that AI can create?

AI can create large productivity gaps between employees who use it well and employees who do not.

One employee may use AI to orchestrate multiple workstreams, accelerate analysis, draft materials, test ideas and produce higher-quality work at greater speed. Another employee with similar skills and experience may continue with their traditional working methods. The difference in output, speed and overall contribution can become dramatic fairly quickly.

For leaders, this creates a talent development challenge. AI readiness is not just about empowering early adopters. It is about raising the baseline across the organization, so employees understand how to use AI responsibly, strategically and consistently.

How can businesses prevent the value gap from getting worse?

Preventing the employee value gap starts with leadership alignment. The C-suite needs to set clear expectations around how AI should and should not be used, how employees will be trained and how leaders will be accountable for adoption.

Focus on these priorities to prevent the employee gap from widening:

• Create shared expectations: Leaders should define what responsible AI adoption looks like for the organization, including expectations for when and how employees at different levels should use AI.
• Build governance that reaches the individual level: Enterprise policies are important, but they only work if employees understand how to apply them in daily workflows.
• Develop leaders first: Leaders cannot coach employees to work differently with AI if they have not gone through that mindset shift themselves.
• Measure value, not just activity: Usage metrics matter, but organizations also need to evaluate whether AI is improving outcomes.
• Address resistance directly: Employee hesitation may come from fear, uncertainty, lack of confidence or unclear expectations. Leaders need to create space for those conversations.

Governance is especially important as AI becomes more embedded in work. Organizations need guardrails for data, security, privacy, compliance, quality control and accountability. Governance should not be treated as paperwork but as an operating capability that helps organizations scale AI safely and effectively.

How is AI changing work delegation?

Effectively delegating work has always been a key component of quality leadership. With AI now in play, leaders don’t just need to decide if they should delegate a task or project. Now the question is: Should I delegate it to a person, to AI or to a combination of both?

That shift requires leaders to think differently about capacity and how teams or departments are structured. Don’t view AI as just another piece of software. Look at it as a digital team member that needs direction, context and oversight. It must be onboarded with clear instructions, given relevant information, guided toward the right outcomes and checked regularly.

AI doesn’t replace the need for people. In many cases, the best workflow will include both AI and human involvement. AI may handle the first draft, analysis, categorization, synthesis or routine execution. A person may provide judgment, quality control, stakeholder context, ethical oversight or final decision-making.

Also consider how AI changes team-level workflows. If AI use is siloed to individual tasks, the organization may gain pockets of productivity but miss broader opportunities to redesign how work moves across the team. True AI readiness requires leaders to think beyond individual productivity and consider how humans and AI can work together to improve processes across entire departments or organizations.

What should be done by AI and what should be done by humans?

We’re at a point now where most businesses have AI tools and humans working together. To get the most out of this combo, you need to understand the strengths of each.

AI is increasingly strong at complicated cognitive tasks, including:

• Analysis
• Coding
• Forecasting models
• Content generation
• Pattern recognition
• Summarization
• Classification
• Repeatable decision support

These are areas organizations have historically treated as high-skill work. AI can accelerate these workflows, freeing up your skilled staff to focus more on the highest-value work.

Humans need to remain the driving force in complex, relational and judgment-heavy work, including:

• Critical thinking
• Empathy
• Judgment
• Communication
• Relationship building
• Conflict resolution
• Ethical reasoning
• Contextual decision-making
• Accountability

For years, many of these capabilities were labeled “soft skills.” In an AI-enabled workplace, they are becoming core leadership skills.

Not every organization will craft the same roles for AI and humans. Some businesses may be comfortable using AI in customer-facing processes. Others may decide that certain client, patient, employee or stakeholder interactions require a human touch, even if AI could technically perform part of the task.

You also need to remember that accountability remains human. AI can produce an inaccurate, biased or low-quality output, and you need human oversight to catch it. As such, we can’t simply plop something into AI and say, “Copilot did it.” Just as if you were leading a team of people executing the work, the buck stops with you.

How does Wipfli help?

Wipfli helps organizations approach AI readiness as both a technology challenge and a people challenge. Those two sides need to move together. A company cannot implement AI tools and expect transformation if leaders and employees do not change how they work.

Wipfli can help your business prepare, plan and implement a successful AI strategy. Start a conversation.

Develop an AI leadership plan

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