By Mitsunaga Kikuchi, CEO and Founder of Shirofune
Mitsunaga Kikuchi is the founder and CEO of Shirofune, the digital advertising management tool that automates ad campaigns through an easy-to-use interface for management, budgeting, monitorig and analytics. Mitsunaga recently won the 2025 ANA MarTech Gold Award for individual marketing solutions provider and the 2025 AdWeek TechStack Award.
A junior media buyer investigating an underperforming campaign is doing several things at once. They are checking targeting, reviewing conversion data, and deciding whether an ad deserves more budget. On top of that, they are also learning the nuances of marketing.
That education has long been built into the daily work of campaign management. A decision produces a result, a manager challenges an assumption, and an unexpected drop in performance sends someone looking for an explanation.
As AI takes over more bidding, budget allocation and campaign optimization, companies can remove hours of repetitive work. AI can also remove the critical experiences that build employee expertise. The savings appear immediately. The consequences for talent development may take years to become visible.
Gartner’s September 2026 research illustrates how quickly roles are changing. Among marketing leaders surveyed, 18% eliminated certain functional roles due to automation, while 16% created new roles and nearly one-third redesigned existing ones. These changes carry a practical responsibility: companies need to decide how employees will gain experience as their responsibilities evolve.
Much of the educational value of campaign work comes from investigating why something happened. A lower cost per acquisition might look like an improvement until someone discovers that the campaign is attracting customers who rarely return. A campaign with strong return on ad spend might rely heavily on existing customers who were already likely to purchase.
Understanding these situations requires familiarity with the business, the measurement system and the limits of the available data. Junior marketers build that familiarity by examining real decisions and discussing their consequences.
Manual repetition alone does little to develop those skills. Someone can spend years adjusting bids without understanding the commercial logic behind them. The useful part is the connection between an action, an expectation and an observed result. Automation changes where managers need to create that connection.
This becomes especially important when junior employees are asked to evaluate automated recommendations. Reviewing a proposed budget increase requires more than knowing where to click. The reviewer needs to understand what the system is optimizing, whether its conversion signals are reliable and what additional spending is likely to accomplish.
Consider an automated recommendation to move budget toward a campaign with stronger reported ROAS. Before accepting it, a marketer should examine whether the campaigns use comparable attribution settings, whether returns affect the revenue figures and whether the apparent winner is acquiring new customers. Customer lifetime value may also change the decision.
An employee who has only been trained to approve recommendations may struggle to investigate those questions. A polished explanation from an AI system can make the decision feel settled before the underlying assumptions have been examined.
Businesses already recognize a training challenge. In Marketing Week’s 2026 Career & Salary Survey, 66.5% of 2,350 respondents reported an AI skills gap within their teams. For media buying organizations, training should address how employees assess the decisions these systems make alongside how they operate the tools.
A useful starting point is to review the work being automated and identify the learning each task once supported. Compiling a report may teach little when it involves copying numbers between spreadsheets. Investigating a discrepancy between platform revenue and actual sales can teach a great deal. Managers can automate the compilation while assigning responsibility for explaining the discrepancy.
Campaign reviews can then become structured opportunities to practice judgment. Before examining an automated recommendation, ask a junior employee to propose an allocation using the available data. Have them explain their reasoning, identify uncertainties and describe what would change their mind. Compare that assessment with the system’s recommendation and discuss the differences.
The purpose is to make the employee’s thinking visible. A manager can correct a flawed assumption, explain a tradeoff, or point out missing information. Simply asking whether someone agrees with an automated recommendation provides much less insight into their understanding.
Teams can also give junior marketers responsibility for bounded experiments. An employee might propose a creative test or investigate a specific audience segment within an agreed budget. Before launch, they document the hypothesis and expected result. Afterward, they explain what happened and what remains uncertain.
That process creates firsthand experience while keeping financial exposure manageable. Responsibility can expand as employees demonstrate that they can interpret evidence and recognize when to seek help.
Senior marketers must make their own reasoning visible, too. When a manager overrides an automated recommendation, the explanation should become part of the review. Perhaps inventory is limited, margins have changed or the sales team has identified poor lead quality. These details teach employees how business context affects campaign decisions.
Managers should assess whether that learning transfers to unfamiliar situations. Can the employee identify a measurement problem without being prompted? Explain why a strong platform metric may conceal a weak business result? Recommend a next step and acknowledge its limitations?
Those abilities determine how effectively a team can use automation over time.
Companies should include employee development in the time they recover through AI. A portion of the hours saved can support campaign investigations, supervised experiments and substantive feedback. That investment gives junior marketers a path toward greater responsibility and gives the business people capable of managing increasingly automated advertising.
