What does it take to accelerate value from AI? Enterprises have a window to build the foundations that make it real, from strong data and redesigned processes to a modern digital core, operating model and talent strategy. Accenture’s Chief Strategy and Services Officer, Manish Sharma, shares what organizations need to prioritize to realize AI's value. Read the full report: https://accntu.re/4vwhSW2 [Video description: Video shows Manish Sharma, Accenture Chief Strategy and Services Officer, speaking to camera. He shares that organizations have the next two to three quarters to prepare for AI at scale. He outlines four focus areas: strong data foundations, streamlined processes, a modern digital core, and an operating model with reskilled talent. He notes that talent readiness will be the biggest challenge and urges leaders to move quickly].
Gustavo O.
2w
Strong emphasis on the structural enablers of AI value creation—data foundations, operating model, processes, and talent readiness. The key challenge I see at enterprise scale is the sequencing problem: balancing foundational investment with the pressure to demonstrate near-term, measurable business impact in fast-evolving AI use cases. In practice, this often creates a tension between building a scalable AI backbone and capturing incremental ROI early enough to sustain executive and board-level commitment. How is this being addressed in large-scale transformations?
A very relevant perspective, especially where data foundations and the digital core are concerned. In many enterprise environments, the real bottleneck is ensuring consistent and reliable data flows across systems. Without strong integration and well-structured data pipelines, scaling AI initiatives beyond pilot stages becomes a challenge. Bridging that gap is often where the real transformation happens.
Ruan Lotter
2w
Hilarious how Accenture is still pushing the SCAM while other companies are starting to realise what's happening... How is Accenture this disconnected from reality? Remember how they hyped up the Metaverse?https://youtu.be/OFmxKgaLN80
The operating model piece is critical! Our 2026 data shows teams with test-and-learn infrastructures are 3x more likely to fully integrate AI, up from 2x in 2025. The gap is widening fast. That structure is what actually enables rapid iteration and learning at scale.
The real bottleneck isn’t the tech, it’s aligning data, processes, and especially talent at the same pace.
Codynex
2w
Talent readiness is the only bottleneck that can't be solved with more compute. You can buy the models and fix the data, but if your operating model doesn't empower a reskilled workforce to act on AI outputs, you’ve just automated your existing inefficiencies.
Two to three quarters to prepare for AI at scale, with talent readiness as the biggest challenge. Those two points sit awkwardly together. Reskilling an enterprise workforce on AI through hiring and internal training alone takes longer than 6 to 9 months for most organisations. The timeline works only if the talent plan looks beyond the internal workforce from day one.
Am I mistaken or do these 4 points apply to ANY technology adoption strategy 🤔
Praxis Latam
2w
At Praxis LATAM, in our daily work on the consulting frontlines, we see that the 'talent readiness' he mentions isn't just about training people to use a tool, it's about a fundamental mindset shift.
The biggest bottleneck to AI scale isn't the technology—it’s the human element. Manish hits the nail on the head regarding the two-to-three-quarter window. Many leaders are still treating AI as a plug-and-play software installation rather than a fundamental shift in how the business operates. If your data is fragmented and your core processes are still built for the pre-AI era, you’re just applying automation to inefficiency. I’ve found that the most successful organizations are prioritizing the "reskilling" component long before the tools are even fully deployed. The companies that win will be the ones that view their workforce as a partner in the AI transition rather than an afterthought. Speed is critical, but structural alignment is what actually sustains the value. #ArtificialIntelligence #DigitalTransformation #BusinessStrategy #FutureOfWork #EnterpriseTech