In 2025, at least one ecommerce leader acknowledged that he wasn’t going to increase headcount anymore before considering if AI could fill the gap first. His message reflected a growing interest among C-suite executives in using AI as a capacity-expanding tool.This doesn’t mean that they’re reducing their workforces, though. On the contrary, many organizations are happy to keep the teams they have. However, some organizations may be reluctant to add employees when AI could help meet their capacity needs. Or, rather, sophisticated AI systems that can do more than just fetch information or provide a quick summary of a long-winded email.Indeed, many senior managers are realizing that generative AI tools were just the beginning. Are those tools useful? Yes. Yet today’s advanced AI tools can automate processes like data analysis, create predictive reporting models, forecast trends, and provide operational decision-making recommendations. And they can often do much of this behind the scenes with limited human intervention.Again, this isn’t a doomsday warning that AI systems will take all people’s jobs. Consequently, the job market is more likely to change than come to a halt. One report suggested that 92 million roles would disappear by 2030, but that 170 million new ones would take their place. Consequently, the job market is unlikely to come to a halt. Rather, people and their companies may need to redirect their core abilities and talents toward tasks that AI does not perform as well.How are leaders staying ahead of the move toward bumping up the bandwidth of their teams with AI? Some are exploring several AI-based opportunities.1. Proprietary AI agentic systemsAI agents often communicate with one another, creating a network of intelligent, automated bots working in the background. And a growing number of AI agent software systems have arrived in the past few years. That said, many organizations are opting to work with companies that can help them construct their own agentic AI systems.One reason organizations may invest in proprietary AI systems is that generic AI tools often lack access to company-specific knowledge. Companies like DeepAuto.AI address this by connecting disparate enterprise data sources and training AI agents on that information, allowing employees to work with AI systems that understand company-specific processes, documentation and historical knowledge. This can make AI more useful for day-to-day operations than a standalone chatbot.Although creating a proprietary ground-up agentic AI system can take months, it may be well worth the effort. Plus, when employees see it as a tool custom-created to help them, it can reduce resistance or may encourage adoption.2. Targeted AI tool training sessionsOne frequently overlooked roadblock to AI adoption across organizations is a lack of employee knowledge of how to use AI tools effectively. Even if employees are given the go-ahead to experiment with AI, they might not all know how to best use AI to assist them. As a result, the tools may provide fewer productivity benefits than expected.Research cited by an AI training provider shows how this conundrum plays out in workplaces. Although about three-quarters of executives believe their organizations have successfully adopted AI, fewer than half of employees agree.One way to address this issue is for leaders to make sure team members are trained on the AI they’re expected to use. Leaders shouldn’t assume that employees naturally use AI at home. Some do, of course. Yet others might not. Alternatively, they might be using basic AI tools and not be aware of the full range of AI capabilities available to them.3. Workforce upskillingMany people can be retrained and upskilled to move out of roles that could be performed by AI. Companies that assess their employees for competency may find that paying for upskilling allows those employees to move into different functions or departments.This is a strategic reallocation of the existing team members in an organization. Instead of hiring more people or losing great ones, leaders can start to put the company’s money toward making the most of the talent they already have in place.Does retraining take time? Yes, because learning new skills generally takes time. Will upskilling turn away some people? Of course, since not every employee will want to change positions or responsibilities. However, if the majority of team members decide it’s worth their while to get more training (particularly if their employer is paying), the outcome could benefit both them and the organization.Many organizations aren’t going to push to add more people if they can do more work with AI software and systems. Yet that doesn’t mean that all employees are at risk of being downsized. In fact, many leaders are retaining their current teams and using AI to extend human capability, not replace it. VentureBeat ntewsroom and editorial staff were not involved in the creation of this content.
Why the next competitive advantage may be better AI systems, not more employees
In 2025, at least one ecommerce leader acknowledged that he wasn’t going to increase headcount anymore before considering if AI could fill the gap first. His message reflected a growing interest among C-suite executives in using AI as a capacity-expanding tool.This doesn’t mean that they’re reducing their workforces, though. On the contrary, many organizations are happy to keep the teams they have. However, some organizations may be reluctant to add employees when AI could help meet their capacity needs. Or, rather, sophisticated AI systems that can do more than just fetch information or provide a quick summary of a long-winded email.Indeed, many senior managers are realizing that generative AI tools were just the beginning. Are those tools useful? Yes. Yet today’s advanced AI tools can automate processes like data analysis, create predictive reporting models, forecast trends, and provide operational decision-making recommendations. And they can often do much of this behind the scenes with limited human intervention.Again, this isn’t a doomsday warning that AI systems will take all people’s jobs. Consequently, the job market is more likely to change than come to a halt. One report suggested that 92 million roles would disappear by 2030, but that 170 million new ones would take their place. Consequently, the job market is unlikely to come to a halt. Rather, people and their companies may need to redirect their core abilities and talents toward tasks that AI does not perform as well.How are leaders staying ahead of the move toward bumping up the bandwidth of their teams with AI? Some are exploring several AI-based opportunities.1. Proprietary AI agentic systemsAI agents often communicate with one another, creating a network of intelligent, automated bots working in the background. And a growing number of AI agent software systems have arrived in the past few years. That said, many organizations are opting to work with companies that can help them construct their own agentic AI systems.One reason organizations may invest in proprietary AI systems is that generic AI tools often lack access to company-specific knowledge. Companies like DeepAuto.AI address this by connecting disparate enterprise data sources and training AI agents on that information, allowing employees to work with AI systems that understand company-specific processes, documentation and historical knowledge. This can make AI more useful for day-to-day operations than a standalone chatbot.Although creating a proprietary ground-up agentic AI system can take months, it may be well worth the effort. Plus, when employees see it as a tool custom-created to help them, it can reduce resistance or may encourage adoption.2. Targeted AI tool training sessionsOne frequently overlooked roadblock to AI adoption across organizations is a lack of employee knowledge of how to use AI tools effectively. Even if employees are given the go-ahead to experiment with AI, they might not all know how to best use AI to assist them. As a result, the tools may provide fewer productivity benefits than expected.Research cited by an AI training provider shows how this conundrum plays out in workplaces. Although about three-quarters of executives believe their organizations have successfully adopted AI, fewer than half of employees agree.One way to address this issue is for leaders to make sure team members are trained on the AI they’re expected to use. Leaders shouldn’t assume that employees naturally use AI at home. Some do, of course. Yet others might not. Alternatively, they might be using basic AI tools and not be aware of the full range of AI capabilities available to them.3. Workforce upskillingMany people can be retrained and upskilled to move out of roles that could be performed by AI. Companies that assess their employees for competency may find that paying for upskilling allows those employees to move into different functions or departments.This is a strategic reallocation of the existing team members in an organization. Instead of hiring more people or losing great ones, leaders can start to put the company’s money toward making the most of the talent they already have in place.Does retraining take time? Yes, because learning new skills generally takes time. Will upskilling turn away some people? Of course, since not every employee will want to change positions or responsibilities. However, if the majority of team members decide it’s worth their while to get more training (particularly if their employer is paying), the outcome could benefit both them and the organization.Many organizations aren’t going to push to add more people if they can do more work with AI software and systems. Yet that doesn’t mean that all employees are at risk of being downsized. In fact, many leaders are retaining their current teams and using AI to extend human capability, not replace it. VentureBeat ntewsroom and editorial staff were not involved in the creation of this content.
C-suite executives prioritize proprietary AI agentic systems over hiring to automate data analysis, forecasting, and operational decision-making. For tech managers, competitive advantage shifts from team size to AI capability—requiring investment in custom systems and workforce upskilling.
