Seagate’s IT team had 90 days to replace a global IT service platform running for 10 years, covering 30,000 employees. The normal move would’ve been to lift the old configurations and dump them into a new system, then fix the mess later. That’s what most companies do—and that’s exactly why their AI projects usually fail. Instead, Seagate’s team rebuilt everything from scratch: rewrote the service catalog, standardized SLAs across regions, and eliminated years of buried tech debt. The result? A platform clean enough for AI tools to actually deliver value instead of just spinning their wheels.

Freshworks CEO Dennis Woodside told Fortune the difference between companies that win with AI and those that don’t usually comes down to how they handle their data. He’s seen teams spend months migrating old systems only to realize their AI models can’t pull clean data because the foundation was garbage. “If you start with a messy basement, no amount of fancy AI paint will fix it,” Woodside said. Seagate’s approach proves his point: they didn’t just move the furniture—they gutted the house first.

Why most AI projects stall before they even start

The problem isn’t usually the AI itself. It’s the data feeding it. Old IT systems pile up years of inconsistent tickets, duplicated entries, and conflicting service levels. When AI tries to learn from that junk, it gets trained on noise instead of signal. That’s why Woodside says the fastest way to AI failure is assuming a simple migration will work. “Companies think they’re saving time by not rebuilding,” he said. “But they’re really just buying a faster route to failure.”

Seagate’s team didn’t have a choice—they had to finish in 90 days or risk losing the contract. But that tight deadline forced them to ignore the usual shortcuts. They stripped out regional quirks in their service levels, rebuilt the catalog to match real workflows, and set up fresh monitoring from day one. Within weeks, their AI tools could reliably predict outages and route tickets to the right teams. That kind of clean data is what makes AI useful in the first place.

The hidden cost of “good enough” migrations

Most companies treat IT migrations like a plumbing job: get the water flowing again and move on. But Woodside argues that mindset cripples AI projects before they begin. He points to teams that spend six months migrating a ticketing system only to realize their AI models can’t pull clean data because the new system still carries old inconsistencies. “You end up with AI that’s as smart as a toaster,” he said. “It can heat up a slice, but it won’t bake a cake.”

The Seagate team’s rebuild cost more upfront—time, effort, and budget—but it paid off in months. Their AI tools now cut mean time to resolution by nearly 30%, according to internal metrics. That’s the kind of ROI Woodside says separates agile enterprises from the rest. “Speed matters,” he said. “But speed without a clean foundation is just speeding toward a cliff.”

What happens next for Seagate—and others watching

Seagate isn’t the only company racing to rebuild for AI. Woodside says Freshworks is seeing a surge in customers ditching old migrations for full rebuilds. Some cite Seagate’s results; others just can’t afford another failed AI pilot. The message is spreading: if you want AI to work, you’ve got to fix the foundation first.

For Seagate, the next step is scaling the new platform across more regions and adding predictive AI to handle routine fixes before users even notice a problem. They’re also sharing lessons with partners in storage and infrastructure to push the industry toward cleaner data from the start. It’s a rare case where doing things the hard way actually paid off—twice.

What You Need to Know

  • Source: Fortune
  • Published: May 17, 2026 at 12:30 UTC
  • Category: Business
  • Topics: #fortune · #business · #economy · #freshworks · #seagate · #seagate-it-rebuild

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Curated by GlobalBR News · May 17, 2026

🇧🇷 Resumo em Português

O Brasil, que já se consolidou como um dos maiores mercados de tecnologia da América Latina, agora assiste a uma corrida acelerada pela inteligência artificial, onde as empresas ágeis estão levando vantagem — e a lição pode ser crucial para os negócios locais. Segundo o CEO da Freshworks, Girish Mathrubootham, a diferença entre as empresas que avançam no uso da IA e as que ficam para trás está justamente na capacidade de se adaptar rapidamente, reconstruindo processos e estruturas tecnológicas em tempo recorde. Um exemplo emblemático vem da Seagate, que, em apenas 90 dias, reformulou sua plataforma de serviços para viabilizar a adoção de soluções inteligentes — um movimento que, no contexto brasileiro, poderia significar a sobrevivência ou a estagnação de muitas organizações.

No cenário nacional, onde a digitalização ainda enfrenta desafios como infraestrutura limitada e mão de obra especializada escassa, a estratégia de reinventar sistemas em prazos apertados soa como um alerta para os executivos brasileiros. Mathrubootham destaca que o sucesso não depende apenas de investimentos milionários, mas de uma cultura organizacional ágil, capaz de integrar IA sem amarras burocráticas. Para um país onde médias e pequenas empresas representam cerca de 30% do PIB, a lição é clara: quem não se mexer rápido, ficará para trás na disputa por eficiência e inovação. Afinal, a IA não é mais um diferencial, mas uma necessidade urgente.

A pergunta que fica é: como os empresários brasileiros irão encarar essa transformação sem perder o ritmo?

🇪🇸 Resumen en Español

El auge de las empresas ágiles en la carrera por la inteligencia artificial se consolida tras un caso emblemático: el equipo de TI de Seagate logró reinventar su plataforma de servicios en solo tres meses para impulsar su estrategia de IA, un movimiento que refleja cómo la adaptabilidad se ha convertido en el factor diferencial para triunfar en la era tecnológica. Mientras muchas compañías se pierden en bucles de burocracia o proyectos interminables, este hito subraya por qué las organizaciones que apuestan por la agilidad no solo avanzan más rápido, sino que también integran soluciones innovadoras con mayor eficacia.

Según el CEO de Freshworks, la clave no radica en la tecnología en sí, sino en cambiar la mentalidad empresarial: eliminar silos departamentales, empoderar equipos multidisciplinares y priorizar ciclos de desarrollo cortos que permitan iterar en tiempo real. Para los lectores hispanohablantes, este enfoque tiene implicaciones profundas, especialmente en un contexto donde las pymes y grandes corporaciones de Latinoamérica y España aún luchan por modernizar sus infraestructuras digitales. La lección es clara: quienes adopten esta filosofía de “menos burocracia, más acción” no solo ganarán en competitividad, sino que podrán cerrar la brecha tecnológica que amenaza con dejar atrás a los rezagados.