Adaptive Organizations Embrace Reinvention To Leverage AI
In a Forbes Council column, Steven Kawasumi argues that organizations must redesign workflows and operating models to capture AI's value. Kawasumi frames this moment as different because AI simultaneously alters technology stacks, decision authority, job design and customer expectations. He warns that common upskilling programs have limited impact when underlying workflows remain unchanged, and uses customer support examples where generative AI improves agent productivity but not case resolution unless workflow logic is redesigned. The piece cites historical reinventions by Amazon and Netflix as precedents for strategic reinvention and recommends organizations align operating model, technology and measurement to realize AI benefits.
What happened
In a Forbes Council column, Steven Kawasumi argues that AI requires organizations to rethink workflows and operating models rather than only adding tools or training. The article cites Amazon moving from bookselling into logistics and cloud, and Netflix shifting from DVD-by-mail to streaming, as historical examples of companies that reinvented early. Kawasumi documents that AI is changing multiple layers at once: technology architecture, workflow design, handoffs, role boundaries, customer expectations and employee perceptions about skills and career stability.
Editorial analysis - technical context
Kawasumi uses the example of a large customer support organization to illustrate the mechanics: adding generative AI to draft responses and summarize cases can raise adoption and productivity signals, yet resolution quality may not improve if the underlying workflow logic is unchanged. This framing highlights a common technical gap between model capability and end-to-end system outcomes, where an inserted model improves intermediate metrics but not the final KPI.
Context and significance
Companies that successfully extract business value from AI generally integrate model outputs into redesigned decision flows, measurement frameworks and automation boundaries. Kawasumis column places emphasis on aligning operating model, tooling and incentives; this mirrors trends seen in enterprise AI literature and practitioner reports about productionizing ML at scale.
What to watch
For practitioners: monitor whether AI initiatives move beyond point solutions to changes in handoffs, routing logic and success metrics. Observers should watch for shifts in technology architecture that make model outputs first-class inputs to orchestration layers and for leadership metrics that tie AI deployment to end-to-end outcomes rather than intermediate usage statistics.
Reported limitations
The Forbes column is prescriptive commentary and does not present original empirical measurements of large cross-industry deployments. Kawasumi does not provide specific quantitative benchmarks or a step-by-step implementation template in the piece.
Scoring Rationale
The piece highlights an important practical gap between deploying models and redesigning end-to-end workflows, which matters for practitioners operationalizing AI. It is a strategy-level column rather than new technical research, so its impact is notable but not transformational.
Practice interview problems based on real data
1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with.
Try 250 free problems
