Signals of the future

Learnings from the frontier of human-centered AI adoption

SYPartners is a transformation consultancy. For over 30 years, we've helped the world's most iconic organizations lead into the unknown.

In the past two years, we have been exploring GenAI adoption and new ways of working in an agentic era.

Through in-depth interviews, surveys, multiple client engagements, and workshops held at Columbia University and Stanford d.school, we have learned alongside leaders in startups, enterprises, and academia.

Here are our findings to date.

As part of our research, we surveyed 100+ senior leaders about their organization's ability to make the most of AI agents.

Participants rated their levels of concern and readiness across different dimensions of implementing AI agents.

For example, leaders felt least concerned and most ready about deciding which tasks will be automated by AI agents. However, they felt most concerned and least ready about defining accountability for decisions made by AI agents.

A clear pattern emerged: Dimensions traditionally owned by tech teams ranked higher in readiness and lower in concern.

This makes sense.

Tech teams have been at the forefront of bringing AI into organizations. They have had a head start on vendor and tool selection, workflow redesign, and early automation.

Conversely, leaders felt most concerned about skills, trust, accountability, and culture. And they didn't feel adequately prepared to support their teams on these dimensions.

This is our key insight: Leaders are most worried about—and least ready for—the human and cultural dimensions of implementing AI agents.

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Going deeper into our research, five signals are emerging.

They highlight five opportunities for forward-thinking leaders to shape the next wave of AI transformation—one centered on innovation, people, and new ways of working.

Signal 1

Most AI efforts prioritize productivity. Applying AI to drive growth is the next white space.

Signal 2

Human implications of AI adoption are overlooked in favor of old playbooks.

Signal 3

People leaders are underrepresented in AI conversations.

Signal 4

Nimbleness is becoming a business-critical skill for large enterprises.

Signal 5

The next AI accelerator is strong CEO ownership.

Signal 1

Most AI efforts prioritize productivity. Applying AI to drive growth is the next white space.

Signal 1

Most AI efforts prioritize productivity. Applying AI to drive growth is the next white space.

Across industries, leaders are directing AI budgets toward efficiency and productivity wins—goals that revolve around cutting costs, speeding workflows, and increasing productivity.

And that's sensible. In the early stages of AI adoption, efficiency is the safe and provable bet—it's where ROI is easiest to measure, governance is easiest to maintain, and risk is lowest. But as organizations progress from pilots to scaled implementations, growth-oriented use cases start to emerge. As one example of this shift, our data shows that the focus on workforce augmentation nearly doubles once organizations move into the stages of implementing and scaling AI agents.

Efficiency is the entry ticket—an investment in plumbing, controls, and workforce readiness. For many organizations, it will be their first brush with AI adoption.

But efficiency becomes less of a differentiator as adoption maturity grows. The real upside will be felt by organizations and leaders who make the most out of this foundation — using it to unlock customer-facing innovation, revenue capture, and market expansion.

Most AI efforts prioritize productivity. Applying AI to drive growth is the next white space.

Top 3 reasons for AI adoption by stage - showing improving efficiency, reducing costs, and workforce augmentation across different implementation stages

Examples from the frontier

"Dom" is an AI-powered voice assistant that allows customers to order via voice. It created a whole new, low-friction sales channel—boosting order volume and reinforcing Domino's tech-forward brand credibly.

The Chinese technology conglomerate harnessed AI to sharpen ad targeting and accelerate gaming content development, fueling a 13% year-over-year revenue increase and a 20% uplift in marketing services revenue in Q1 2025.

What story of AI-driven growth would we be proud to tell in 5 years?

Signal 2

Human implications of AI adoption are overlooked in favor of old playbooks.

Signal 2

Human implications of AI adoption are overlooked in favor of old playbooks.

Leaders know the tech-and-process playbook to implement AI: pick vendors, redesign workflows, automate work. That's familiar from past digital eras. What's different now—especially with GenAI and emerging agents—is the importance of the human system affected by the change. Mindsets, behaviors, skills, and ways of working determine whether AI tools are used and create value.

As teams move from AI pilots to scaled applications, the residual problems become less about model choice or tech integration, and more about who owns what, how to ready managers, how to reskill teams, and how to build durable trust in AI-mediated decisions. This is why many "successful" pilots underperform at scale: The process is engineered, but the new ways of working are underbuilt.

Leaders are waking up to the realization that human enablement is the key to realizing the full value of AI. As this happens, the focus is shifting from what we build to how our people work with what we've built.

Human implications of AI adoption are overlooked in favor of old playbooks.

60%

of leaders rolling out AI agents are more concerned about People & Culture than Tech & Process

SYP Future Of Work Study

Examples from the frontier

The global consumer goods company created an AI ethics committee embedded into product teams, ensuring new AI use cases are vetted through a human-impact lens (bias, trust, inclusion). This gave employees clarity and safety, which increased willingness to experiment with AI in marketing and operations.

The biopharmaceutical company created an internal AI science fair where scientists and business staff demo prototype tools—ranging from molecule modeling assistants to trial-data summarizers. By making AI tangible and peer-driven, Pfizer accelerated adoption and improved trust, while surfacing dozens of grassroots use cases that moved into formal pipelines.

How should we ready our teams to harness AI with confidence?

Signal 3

People leaders are underrepresented in AI conversations.

Signal 3

People leaders are underrepresented in AI conversations.

GenAI and AI Agent explorations typically begin in technology and engineering teams—a natural starting point. Yet, organizations are quickly finding that the impacts of GenAI extend far beyond these teams' remits. GenAI adoption shifts roles, sparks new ways of working, and challenges skills that employees have honed over their careers. So the question becomes: What is the role of HR teams to shape the transition?

Organizations that bring the Technology and People sides of the business together to co-lead GenAI tend to advance quicker. Without that partnership, deployments are slowed with friction mounting from fear, anxiety and uncertainty. Bringing the People/HR voice in as a co-lead from the start is crucial, as introducing GenAI isn't just a technology shift—it's a human and work transformation: expanding roles, unleashing skills, and raising questions about contributions that employees may have honed over their careers.

People leaders are underrepresented in AI conversations.

70%

of HR leaders are not actively involved in AI projects and don't contribute to decision-making processes.

SHRM research

Examples from the frontier

The biotech company created a hybrid Chief People and Digital Technology Officer role, uniting HR and IT to accelerate AI adoption. They deployed 3,000+ custom generative AI tools across workflows, improving efficiency, aligning workforce readiness, and scaling innovation.

The investment company institutionalized collaboration between its CHRO and CTO to co-design AI initiatives, leading to smoother adoption, empowered employees, and AI strategies aligned with business goals and workforce needs.

How could our People team best support AI adoption?

Signal 4

Nimbleness is becoming a business-critical skill for large enterprises.

Signal 4

Nimbleness is becoming a business-critical skill for large enterprises.

Organizations are overestimating their own nimbleness in adapting to the pace of GenAI change, often because they're relying on frameworks like Agile that were built for a different era of transformation. Agile was designed to help teams move faster within known systems and predictable constraints. But AI is not just another software rollout—it's a fundamentally different kind of change. It's fast-evolving and deeply ambiguous. What once gave organizations confidence and control can now create false security and slow their ability to truly adapt.

Our data suggests that large enterprises—with their deep infrastructure, mature technology teams, and vast budgets—are able to launch into AI exploration quickly by piloting and exploring a wide range of possibilities early.

But size soon becomes drag. Bureaucracy, complexity, and legacy processes slow the movement from prototype to scaled adoption. Meanwhile, mid-size and smaller organizations—unencumbered and more nimble—are often moving faster into implementation, scaling, and optimization.

Our learning: However adept organizations are at launching pilots, those that cannot evolve their methods to scale quickly risk being outpaced by those that can.

Nimbleness is becoming a business-critical skill for large enterprises.

32%

list change management among their greatest AI/agent challenges

SYP Future Of Work Study

Examples from the frontier

The multinational banking and financial services corporation created cross-functional AI pods that run in two-week sprints with end-users embedded in the process. Instead of measuring delivery speed, the pods test AI behaviors, track trust and adoption, and pair tech leads with HR to surface fears and adapt training.

The social media company runs an annual "Makeathon," where employees across functions pitch and prototype AI tools. The event has driven adoption beyond engineers, producing live tools such as an internal chatbot that now handles 4,000 queries per month—with 96% of core users and 78% of engineers reporting time saved.

What obstacles prevent us from being more nimble and how could we overcome them?

Signal 5

The next AI accelerator is strong CEO ownership.

Signal 5

The next AI accelerator is strong CEO ownership.

The bolder bets on GenAI and agentic workflows are unleashed by the support of the CEO.

In the most successful examples, the CEO not only facilitates innovation and maintains a tight connection to business strategy, but also creates the conditions that enable other leaders—including CTOs and CHROs—to take part in the work.

Organizations with AI centers of excellence that bring together the voices of technology, people, culture, and business strategy are moving more quickly into scaling agentic workflows. They're also showing how new ways of working—imagined holistically for their business—can unlock growth.

The next AI accelerator is strong CEO ownership.

28%

of companies reported that their CEO is responsible for AI governance

McKinsey Global AI Survey

Examples from the frontier

"We took AI and data out of technology. It's too important... But we put AI at that management table."

Jamie Dimon moved AI leadership out of the technology organization to report directly to the CEO. Dr. Manuela Veloso, Head of AI Research, sits at every management meeting. With $2 billion in AI spending, JPMorgan Chase's approach reflects Dimon's belief that AI transformation cannot be delegated—it requires direct C-suite ownership across every business function.

Benioff has taken a CEO-as-chief-AI-officer stance in recent years, tying Salesforce's GenAI bets to its core values of trust, equality, and customer success.

He has built formal conditions for scaling—funding the Einstein Trust Layer, making governance structures transparent, and ensuring CHRO/people leaders are part of the exploration. The center of excellence they built (spanning product, ethics, culture) speeds pilots and ensures they're not just technical demos, but values-driven rollouts.

How could our CEO further deepen and accelerate our AI transformation?

SYPartners is a transformation consultancy that designs for both humanity and performance.

We help organizations make the most of AI by…

Building a shared AI vision

Create the conditions for leaders to envision how AI can drive new growth and impact.

Defining guiding principles for human + AI collaboration

Develop principles and guardrails that inspire responsible, values-driven AI usage—anchored in your organization's purpose, goals, and culture.

Designing new ways of working and equipping teams to thrive

Reimagine mindsets, behaviors, and workflows to help employees use AI confidently and creatively—supported by tools, rituals, and learning experiences that make adoption real.

Envisioning new AI-driven customer experiences

Design experiences, connections, and interactions that express your brand's humanity in the Human + AI era—building trust and lasting relationships with customers.

SYPartners is a transformation consultancy. For over 30 years, we've helped the world's most iconic organizations lead into the unknown.

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Signal 4

Nimbleness is becoming a business-critical skill for large enterprises.

Signal 4

Nimbleness is becoming a business-critical skill for large enterprises.

Our learning: However adept organizations are at launching pilots, those that cannot evolve their methods to scale quickly risk being outpaced by those that can.

32%

list change management among their greatest AI/agent challenges

SYP Future Of Work Study