THE TRANSFORMER
3PLs cross-train warehouse labor to flex with the demands of the day. Today’s binary talent taxonomy of specialists and generalists is not elastic enough for what’s coming down the AI pipeline. Industry is swiftly moving toward a future state where AI-orchestrated supply chain processes—digital twins and Agentic AI—augment and eventually automate human decision-making based on specified parameters, what-if decision trees, business priorities, and countless other variables. With reinforced improvement, AI will understand past behaviors and outputs, sentiently making the right decisions. In addition to skillset diversity, future-fit logisticians will need a more well-rounded knowledge of Plan-Source- Make-Deliver to understand upstream and downstream trade-offs as they make more strategic decisions. BACK TO THE FUTURE OF AI The “versatilist” is not a novel concept, either. In the context of the workplace, Gartner coined the term nearly 20 years ago. 3 While there will still be demand for specialization, AI decision-making warrants a different kind of professional, shaped by new ways of working. P&G offers insight into this new reckoning. Apart from collaborating with OMP’s Unyson platform to pioneer touchless, AI-enabled supply chain planning, the CPG manufacturer is re-engineering processes and roles. At the 2026 Gartner Symposium/ Xpo, Daniela Cima, SVP One Supply Transformation, documented the step- change shift in thinking. P&G has streamlined 35 processes to 20, while creating two new planning roles—supply flow analyst and supply flow engineer. These positions have been redesigned around decision-making that requires human intervention, less so data manipulation. And, importantly, the analyst and engineer own the end-to-end executed plan instead of handing off to others. 4 While AI has demonstrated
CPG manufacturer Procter & Gamble is transforming its decision-making by re-engineering its supply chain, streamlining 35 processes down to 20 through a touchless, AI-enabled platform.
and translators? Keeping humans in the loop starts at the beginning of the AI feasibility process. As enterprises scope viable use cases, they should take a page out of Design Thinking and Agile playbooks.
productivity for specific roles and tasks, sustained value will only occur when work flows are redesigned across functions. In other words, AI is not a silver bullet solution—yet. Effort is required to re-engineer process frameworks, decision-making, and roles to optimize impact. It requires a more thoughtful, holistic, and strategic plan. This explains why only 36% of chief procurement officers (CPOs) are very confident in their ability to redesign roles and processes around AI, according to recent Gartner Research. 5 Accordingly, here are four ideas enterprises should consider as they conceive their AI scaling strategy: 1. Deconstruct and co-create. While it’s easy to get caught up in current FOMO regarding AI and other digital technologies, taking a measured approach is prudent. Tech laggards have one advantage over “bleeding edge” peers: there’s a wealth of literature and knowledge to learn from as they plan their AI journey. Sequencing innovation is an important consideration as companies kickstart workstreams. AI, like other digital technologies such as blockchain, requires a litany of prerequisites before supply chains can even entertain mainstream adoption: • How mature is the company’s data culture and governance? • Has there been a comprehensive data assessment to suss out latency and visibility gaps? • Is there a developmental pipeline for training analysts, engineers, scientists,
How well are current processes documented? Conduct an Agile
Retrospective with analysts and managers. The 4L model (Liked, Learned, Lacked, Longed for) is a good place to begin. Deconstruct the process into tasks and decision-making choices, keeping the human at the center of the conversation. What parts of the work are tedious, mundane, repetitive and can be automated? How can technology help the worker be more efficient and positively impact their quality of life? Process mining and task discovery tools provide valuable data and insights about work dynamics and opportunities to streamline actions. Some automation companies can even build digital twins of processes—a building block toward Agentic AI. Importantly, top-down mandates risk alienating employees, especially with AI. Leadership should empower bottom-up decision-making as well. Meeting in the middle and encouraging the rank and file to have a voice in co-creating new ways of working is a critical success factor in change management. Also, communication starts listening. It’s essential that HR and business stakeholders create feedback loops so learnings are captured, communicated, and acted upon as necessary. Everyone knows when something goes well. Rarely
July 2026 • Inbound Logistics 75
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