Is your AI strategy leaving your frontline teams behind?

Many AI transformations underperform

Most companies are not struggling with AI because the technology is weak. They are struggling because they are applying it to the wrong problems.

Many AI programs begin with broad automation projects. Companies introduce chatbots, automate document processing, or simplify internal workflows. These initiatives can reduce costs, but they rarely change how the business competes. If your competitors have access to the same AI models and software, they can achieve similar efficiency gains.

The bigger opportunity is with the people who make the decisions that customers actually experience. These are the frontline employees who negotiate contracts, solve customer problems, manage important accounts, operate stores, or provide technical expertise. Every better decision they make can directly influence revenue, customer loyalty, and profitability.

This is where many AI strategies fall short. Organizations often spend millions of dollars on AI platforms while the employees creating the greatest business value receive little support. At the same time, these employees are asking understandable questions about how AI will affect their work. If they do not see practical benefits, they naturally become cautious about the transformation.

The data reflects this disconnect. According to a Bain survey of 100 CEOs, around 80% said they were dissatisfied with the progress of their AI programs. At the employee level, ADP’s Today at Work 2026 report found that only 18% of skilled task workers felt their jobs were safe. Those two findings point to the same issue. Leadership is not seeing the expected business results, while employees remain uncertain about AI’s role in their future.

For executives, this should change the way success is measured. The objective should not simply be deploying AI across the organization. The objective is improving business performance. That means asking different questions. Which employees create the most value? Which decisions have the biggest financial impact? Where can AI help experienced people consistently perform at a higher level?

Companies that answer those questions well will move beyond automation. They will build organizations where AI strengthens human capability instead of operating alongside it.

AI-driven augmentation of frontline roles

Automation is becoming standard. Every major organization can buy similar AI software, access similar large language models, and automate many routine activities. Over time, those capabilities become expected rather than exceptional.

Competitive advantage comes from something different. It comes from combining AI with your own people, your own data, your own processes, and your own customer relationships.

Depending on the industry, frontline employees may be key account managers in consumer products, store managers in retail, or field service engineers and technical sales teams in industrial companies. These roles depend on judgment, experience, and trusted relationships. AI does not replace those qualities. It helps people apply them faster and with better information.

Imagine a key account manager preparing for a negotiation. Instead of spending hours collecting information from different systems, AI can surface customer history, pricing trends, retailer margin pressures, inventory data, and category performance in minutes. The employee still makes the decision, but the quality of that decision improves because the relevant information is immediately available.

That distinction matters. Companies often measure AI by the number of hours saved. Time savings are valuable, but they are only one part of the equation. Better pricing decisions, stronger customer relationships, faster responses, and more effective negotiations create much greater economic value. Those outcomes are also much harder for competitors to copy because they depend on proprietary data, established workflows, and experienced employees.

This is where executives should focus their attention. Buying standard AI features inside CRM or ERP platforms is often the right decision for routine business processes. Building proprietary AI capabilities around the work of your highest-value employees is different. Those investments create intellectual property inside the business. As the AI learns from company-specific data and employees refine their workflows, the advantage compounds over time.

Many organizations are still racing to automate activities. That is a reasonable first step, but it is unlikely to define market leadership. Companies that invest in making their frontline employees substantially better at serving customers, making decisions, and generating revenue have the opportunity to build capabilities that competitors cannot easily reproduce. That is where long-term differentiation begins.

Concentrating AI investments on a handful of high-impact roles is more effective

One of the biggest mistakes companies make is trying to deploy AI everywhere at once. The result is often dozens of separate projects that each deliver modest improvements but never change overall business performance.

It is common to see organizations introducing AI-powered chatbots, document summarizers, meeting assistants, or isolated workflow tools across different departments. Each project may generate measurable efficiency gains, but together they often fail to create meaningful competitive advantage. The business becomes slightly faster without becoming significantly stronger.

A better approach is to identify a small number of roles where AI can directly influence growth, profitability, or customer experience. These are positions where better decisions consistently produce better business outcomes.

For example, in consumer products, key account managers influence pricing, promotions, retailer relationships, and revenue growth. Giving these employees AI-powered insights helps them prepare more effectively, understand customer priorities more quickly, and negotiate with greater confidence. The value comes from improving the entire decision process.

This requires redesigning how these employees work from beginning to end. AI should support preparation, planning, execution, follow-up, and continuous improvement. When every stage of the workflow becomes more intelligent, performance improves in ways that isolated software tools cannot achieve.

For executives, prioritization becomes one of the most important strategic decisions. AI budgets are always limited, regardless of company size. Investing in a few high-impact transformations usually produces stronger financial returns than distributing resources across many independent initiatives with limited business impact.

The question should not be, “Where can we use AI?” The better question is, “Where will AI create the greatest increase in business value?” Those answers rarely involve every department at the same time. They usually begin with a small number of roles that have an outsized influence on customers, revenue, and competitive position.

Successful AI transformation requires technology, workflow redesign, and workforce capability

Technology alone does not transform a business. Real value appears when technology becomes part of how people work every day.

Many AI programs begin by purchasing software and expecting employees to adapt on their own. That rarely produces lasting results. People continue using familiar processes, AI remains underused, and expected productivity gains never fully materialize.

Three areas that should develop together.

First, organizations need AI tools that are integrated into daily work and supported by high-quality, proprietary data. Generic AI can answer general questions, but company-specific data allows employees to receive recommendations that reflect the realities of their customers, products, pricing, and operations.

Second, workflows should be redesigned instead of simply digitized. AI should support employees throughout the complete process, from preparation and analysis to execution and follow-up. Redesigning workflows ensures AI becomes part of decision-making rather than an additional application employees must remember to use.

Third, companies need to invest in their people. Employees should understand how to operate AI tools and how to evaluate AI-generated recommendations, recognize limitations, and combine machine-generated insights with professional judgment. Training should focus on improving decision quality rather than only teaching software features.

Start with the organization’s highest-performing employees. These individuals already understand what successful execution looks like. Equipping them with better data, integrated workflows, and AI support helps identify practices that consistently produce stronger outcomes. Once those practices have been validated, they can be standardized and extended across the broader workforce.

For executives, this approach reduces implementation risk while increasing organizational learning. Instead of attempting a company-wide transformation immediately, leadership can refine processes, measure results, and build confidence before expanding AI adoption.

The most successful AI transformations are not driven by software deployment alone. They combine capable technology, redesigned business processes, and employees who know how to use AI to improve decisions. When these three elements reinforce one another, organizations create improvements that continue to grow long after the initial implementation.

A global technology company showed that treating AI as a growth strategy

Many organizations still evaluate AI through the lens of cost reduction. Lower operating expenses and higher productivity are worthwhile outcomes. The larger opportunity is using AI to generate more revenue, improve customer engagement, and strengthen competitive position.

A global technology company that approached AI with this broader objective. Instead of limiting AI to automating routine work, the company focused on one of its most important value-creating functions: its sales organization. The goal was clear. Increase customer-facing time and improve the productivity of every sales representative.

The company redesigned how sales teams prepared for meetings and accessed product information. AI reduced the time spent searching for sales materials by 25%, allowing representatives to spend more time engaging with customers. Better preparation also meant faster responses and stronger conversations, increasing the likelihood of winning new business.

The results extended beyond time savings. Combined with other AI-enabled improvements across the sales process, the company increased revenue per headcount by more than 50%. During the first two years of its AI transformation, those gains contributed to a 30% increase in earnings per share. The original ambition was to double customer-facing time while increasing revenue per representative by 30%, demonstrating that the company defined success in terms of business growth rather than software deployment.

This example illustrates an important principle for executives. AI should not be measured only by operational metrics such as hours saved or tasks automated. Those indicators are useful, but they do not fully capture business value. Revenue growth, customer acquisition, pricing performance, sales effectiveness, and profitability provide a more complete picture of AI’s strategic impact.

Another important lesson is that successful AI programs often begin with a business objective rather than a technology objective. The company did not start by asking where AI could be installed. It started by identifying a critical business function where stronger performance would create meaningful financial results. AI then became a practical tool for achieving those outcomes.

This approach also creates stronger organizational support. Employees are more likely to embrace AI when they see it helping them close more deals, serve customers better, and perform at a higher level. Business leaders gain confidence because investments produce measurable commercial results instead of isolated productivity improvements.

AI strategy should reflect the company’s competitive strategy

There is no single AI strategy that works for every organization. The right approach depends on how a company creates value, competes in its market, and serves its customers.

Some industries benefit more from enterprise-wide automation than from frontline augmentation. Banking is presented as an example where simplifying large-scale processes can generate substantial value because many activities are highly standardized, regulated, and operationally intensive. Companies pursuing a low-cost leadership strategy may also prioritize automation to improve efficiency and reduce operating expenses.

Other organizations compete primarily through customer relationships, specialized expertise, product knowledge, or service quality. For these businesses, investing in AI that strengthens frontline decision-making can create greater long-term value than focusing exclusively on operational efficiency.

This distinction influences another critical decision: whether to build proprietary AI capabilities or purchase commercial solutions.

Off-the-shelf AI tools embedded within enterprise platforms such as CRM and ERP systems are often the right choice for standard business functions. These products continue to improve rapidly, require less implementation effort, and allow organizations to modernize common workflows without significant development costs.

However, standardized software is unlikely to become a lasting source of competitive advantage because competitors can purchase the same capabilities.

Proprietary AI deserves investment when it directly supports activities that differentiate the business. Custom workflows, company-specific knowledge, internal data, and specialized decision-support systems can become strategic assets that improve over time as they are refined through everyday operations. The more these systems reflect the organization’s unique expertise and customer relationships, the more difficult they become for competitors to replicate.

For executives, this means AI investment decisions should begin with strategic priorities rather than technology preferences. Before deciding whether to build or buy, leadership should evaluate where sustainable differentiation can realistically be created. Not every workflow requires customization, and not every competitive advantage depends on proprietary software. The goal is to concentrate internal investment where it generates the greatest long-term business value while using commercial technology where it provides sufficient capability.

An effective AI strategy therefore combines both approaches. Companies can rely on proven commercial platforms for standardized processes while selectively developing proprietary AI capabilities in the areas that define how they compete. This balance allows organizations to control costs, accelerate implementation, and continue building unique strengths that competitors cannot easily reproduce.

Reducing AI-related employee anxiety

Every major technology shift creates uncertainty. AI is no different. Employees across industries are asking how it will affect their responsibilities, career growth, and long-term job security. These concerns should not be dismissed. They influence how quickly people adopt new tools and whether AI investments ultimately deliver value.

Organizations should address these concerns through practical experience rather than broad reassurance. Employees are more likely to embrace AI when they see it helping them make better decisions, complete work more efficiently, and achieve stronger results. Demonstrating clear benefits in day-to-day work builds credibility in a way that general communication alone cannot.

This is particularly important for frontline managers and specialists whose performance depends on judgment, customer relationships, and industry expertise. If AI reduces administrative work, provides faster access to relevant information, and offers useful recommendations, employees can spend more time on activities that create value. They remain responsible for decisions while gaining better tools to support those decisions.

Organizations should also recognize that successful AI adoption is a leadership challenge as much as a technology challenge. Employees need to understand why AI is being introduced, how it supports business objectives, and what new skills will help them succeed. Regular communication, practical training, and visible executive sponsorship all contribute to greater confidence during the transition.

Leaders should also set realistic expectations. AI systems are powerful, but they are not perfect. Employees should be encouraged to apply professional judgment, verify important outputs, and understand where human expertise remains essential. Building trust in AI does not require presenting it as infallible. Trust grows when people understand both its strengths and its limitations.

Another important consideration is how organizations define success. If employees believe AI exists only to reduce headcount, adoption will naturally slow. If they see AI helping them solve customer problems faster, improve decision quality, increase sales, or strengthen operational performance, they are more likely to view it as an investment in their effectiveness.

Companies that succeed with AI usually create an environment where learning continues after deployment. Employees share successful practices, managers identify new opportunities for improvement, and AI capabilities evolve alongside business needs. This continuous feedback strengthens both workforce capability and the value generated by AI over time.

For executives, the message is straightforward. AI transformation is not complete when the technology is deployed. It is complete when employees actively use it to improve business outcomes. Organizations that help their people become more capable through AI will generally achieve stronger adoption, better commercial results, and a more sustainable competitive position than those that focus only on implementing new technology.

Final thoughts

AI is moving quickly, but speed alone will not determine who leads. The companies that create lasting value will be the ones that make better strategic choices about where AI is applied and how it changes the way people work.

For many organizations, the next competitive advantage will not come from adding another AI tool or automating another internal process. It will come from enabling the people who make the decisions that matter most. When frontline employees have faster access to relevant information, stronger insights, and workflows designed around better decision-making, the business becomes more responsive, more productive, and better positioned to grow.

This also requires a shift in how success is measured. Instead of focusing primarily on deployment milestones or productivity metrics, leaders should ask whether AI is improving customer outcomes, increasing revenue, strengthening margins, and helping employees perform at a higher level. Those are the indicators that reflect genuine business transformation.

There is no universal AI playbook. Every organization must decide where automation creates efficiency, where augmentation creates differentiation, and where proprietary capabilities deserve investment. The strongest strategies combine both approaches with clear business objectives and disciplined execution.

The companies that move decisively today have an opportunity to build advantages that become more valuable over time. AI is no longer simply a technology initiative. It is becoming a core part of business strategy, competitive positioning, and long-term growth. Leaders who focus on strengthening their people, not just their processes, will be better prepared for what comes next.