The single biggest shift in AI since January: AI has moved from experimentation to operationalization. The question is no longer whether firms should use AI, but how quickly they can embed it into design, planning, project delivery, and decision-making. Competitive advantage is increasingly determined by execution, not adoption.

What’s moved the challenge/opportunity needle most: The opportunity has shifted from productivity gains to capability expansion. We’re focusing less on automating tasks and more on augmenting expertise—helping teams analyze more options, evaluate risk faster, and make better decisions with greater confidence.

One Q1 assumption about AI that no longer holds true: We expected AI adoption to remain constrained by user skepticism. Instead, demand from clients and project teams accelerated faster than anticipated. The challenge is no longer convincing people to use AI—it’s governing its use responsibly and strategically.

The biggest change in AI use: Execution. The technology is improving rapidly, but we are discovering that process redesign, change management, and data readiness determine success. We’re investing as much in operating models and governance as we are in technology.

Where to lean in harder and where to pull back: We’re leaning harder into enterprise AI, data strategy, and workflow integration. We’re pulling back from standalone tools that create novelty without measurable business outcomes. Value creation is becoming the only metric that matters.

Why the outlook is more optimistic than in January: Despite economic uncertainty, innovation continues to accelerate. We embrace experimentation while maintaining discipline that is creating meaningful advantages. The gap between leaders and laggards is widening faster than expected.

One concrete adjustment to Q4 strategy: We shifted from broad AI + Data enablement to targeted business use cases with measurable outcomes. Every initiative now starts with a business problem/opportunity, success metric, and adoption plan—not a technology discussion.

What  “playing offense” looks like in today’s market: Investing before certainty arrives. We are building AI capabilities, data foundations, and workforce skills now because waiting for perfect clarity is increasingly a competitive disadvantage.

The biggest constraint right now and the best workaround: Organizational capacity. Technology is moving faster than most teams can absorb. We’re addressing this through focused pilots, stronger governance, and practical education tied directly to business outcomes.

Why macroeconomics are more of a concern than in January: More of a concern, but less of a differentiator. Every organization faces the same external pressures. The greater determinant of performance is how quickly firms can adapt, prioritize, and make informed decisions despite uncertainty.

Top priorities for AI before 2027: Turning AI + Data from isolated successes into scalable enterprise capability. Sustainable advantage comes from institutionalizing new ways of working, not from individual experiments.

One signal that was missed in January: The speed at which clients would begin asking not just about AI adoption, but about AI governance, security, and accountability. Trust has become as important as capability.

One early signal the industry needed to move on sooner: We recognized early that AI would be most valuable when embedded into existing workflows rather than deployed as separate destinations. Adoption follows the work, not the technology.

Biggest surprise in how the market actually behaved: The market became simultaneously more cautious and more aggressive. Organizations are scrutinizing spending more closely while accelerating investment in technologies that clearly improve productivity and decision quality.

One overlooked risk the industry should be paying more attention to: Data quality. Many organizations are focused on AI tools while overlooking the data foundations required to scale them. Poor data will become the biggest constraint on future value creation.

The under-the-radar opportunity to watch in Q4: AI + Data powered decision support. The biggest gains are increasingly coming from helping experts make better decisions faster—not from replacing work, but from enhancing judgment.

One quiet shift in guest behavior, design, or capital that could have outsized impact: Guests increasingly expect personalization without friction. The organizations that connect data, design, and operations to deliver seamless experiences will create disproportionate value.

One best practice for monetizing opportunities in the second half: Focus on outcomes, not innovation theater. We are identifying a handful of high-value use cases and executing them exceptionally well.

Where teams are most vulnerable right now: Teams are vulnerable to initiative overload. The pace of change is creating fragmentation. Strong prioritization, governance, and clear ownership are becoming critical leadership capabilities.

Thr smartest way to protect margin without sacrificing long-term value: Invest in productivity while preserving expertise. The goal should be eliminating low-value effort so talented people can spend more time on creativity, strategy, and client impact.

The most thought-provoking insight from ALIS DESIGN+ 2026: The idea that resilience is now a competitive strategy, not a defensive one. We’ve applied that thinking by building adaptable systems, processes, and teams designed to perform under uncertainty.

One “must” now that wouldn’t have been considered a year ago: Using AI as a strategic advisor alongside a productivity tool. It’s increasingly helping shape decisions, scenarios, and planning—not just generating content.

One contrarian AI bet right now: The biggest AI winners won’t necessarily be the companies with the most advanced technology. They’ll be the organizations that redesign how work gets done and redesign their business model along with it.

 What’s getting overhyped vs what’s being underestimated: Overhyped: fully autonomous AI. Underestimated: the value of human-AI collaboration. Most near-term gains will come from augmentation, not replacement.

Who will be the big winners and losers of 2026/27 and why: Winners will be organizations that combine strong data, adaptable talent, and disciplined execution. Losers will be those treating AI as a technology project rather than a business model transformation.