Maintaining Purpose in the Age of AI

How to implement AI in ways that strengthen meaning at work—and the communities that work creates.

Fire exists to generate heat. Left unmanaged, it can flare unpredictably—useful in moments, destructive in others. A well-built hearth gives that heat direction: it contains the flame, makes it efficient, and warms everyone in the room safely.

Purpose works the same way. Individual drive creates energy, but it’s the “hearth” of a supportive community—clear norms, shared goals, and psychological safety—that turns that energy into sustained value and shared well-being.

As we enter 2026, it’s hard to ignore the momentum. AI capabilities are advancing quickly, and organizations across industries are moving from experimentation to real deployment—often faster than their governance, change management, and cultural norms can adapt.

I ended 2025 in a series of meetings and conferences where the conversation frequently turned to “what’s coming next” over the next 3, 5, and 10 years. Most people agree AI will transform how work gets done. What stood out to me, though, was the confidence many smart, thoughtful leaders had that the transformation won’t be limited to business processes—it will reshape social patterns: how teams relate, how careers develop, how communities cohere, and how people derive meaning from their daily lives.

That realization has been sitting with me. And it has pulled two themes to the surface—two human needs that are not optional, regardless of how powerful our technology becomes:

Purpose (a sense that what I do matters, and that I’m growing)

Connection (a sense that I belong to a real community, and that I’m seen by other people)

These aren’t “soft” topics. They’re operational issues. When purpose and connection weaken, engagement drops, trust erodes, retention suffers, and organizational performance becomes fragile. When they strengthen, people learn faster, collaborate better, and build resilience—especially during periods of change.

Why purpose belongs at the center of AI strategy

Purpose is a core driver of motivation. It’s the difference between “I’m completing tasks” and “I’m contributing to something that matters.” In healthy environments, purpose comes from a combination of:

• Autonomy: I have agency over how I do my work

• Mastery: I’m developing skills and competence

• Impact: My work creates visible value for customers, patients, colleagues, or the mission

• Recognition: Someone notices and appreciates the contribution

AI introduces a dual possibility.

On one path, AI becomes primarily a margin-improvement lever: automate tasks, reduce headcount, and squeeze more output from fewer people. That can produce short-term financial benefits, but it often comes with hidden costs: loss of institutional knowledge, diminished morale, and a workforce that feels like the point of work is simply to be replaced.

On the other path, AI becomes a capability amplifier: it removes friction, reduces mundane cognitive load, and gives skilled professionals more time to focus on judgment, creativity, relationship-building, and strategic thinking. This is the path where both performance and meaning can increase at the same time.

The key distinction is not whether AI is used—it’s how leadership defines success.

If success is measured only in reduced labor costs, the system will be designed to minimize human involvement. If success is measured in improved outcomes and improved human effectiveness, the system will be designed to elevate people.

A practical framing I use with clients is simple:

Don’t ask: “Where can we replace people?”

Ask: “How can we amplify the value generated by our team?”

In many organizations, the answer is not a lack of intelligence or commitment. It’s overload: tedious documentation, manual reconciliation, repetitive reporting, and preventable context switching. That is exactly where AI can help—without stripping work of its meaning.

Connection is not a “nice-to-have”—it is a risk surface

Humans are built for community. Connection is how we learn, how we calibrate trust, how we resolve conflict, and how we sustain motivation through difficult work.

But AI is changing the texture of connection in at least three ways:

1) Simulated interaction is getting convincing.

As AI becomes better at imitating human conversation, it will be tempting to substitute real relationships with scalable, low-friction alternatives—especially in customer support, HR, coaching, and even management.

2) Work becomes more mediated.

Teams that once collaborated directly may increasingly collaborate through tools—summaries, agents, auto-generated updates—reducing the number of real moments where shared understanding is built.

3) Isolation can increase quietly.

If AI tools make individual contributors “more self-sufficient,” it can unintentionally reduce the frequency of peer-to-peer help, informal mentoring, and collaborative problem solving—the very interactions that create community.

The result can look productive on paper while weakening the social fabric that makes an organization durable.

This is why I believe “connection” should be treated as a first-class design constraint in AI programs, not an afterthought.

What responsible AI implementation looks like when purpose and community are explicit goals

In practice, “responsible AI” often gets reduced to compliance checklists: privacy, security, bias, transparency. Those are critical, but insufficient.

A more complete approach includes human outcomes: how the system changes roles, relationships, incentives, and identity at work.

Here are several implementation principles that keep purpose and community in view.

1) Design AI to augment judgment, not replace accountability

The most sustainable AI systems clarify who is responsible for decisions and keep humans meaningfully engaged in high-stakes judgment.

• Use AI for drafting, summarizing, triaging, and pattern detection

• Keep humans responsible for final decisions, especially where tradeoffs and values matter

• Make escalation paths explicit

This protects quality and builds trust—internally and externally.

2) Measure what matters to humans, not just what matters to dashboards

If you only track cycle time and throughput, you’ll optimize for speed. If you also track human outcomes, you’ll optimize for sustainability.

Consider adding metrics such as:

• Time recovered for strategic work (not just time saved)

• Employee experience: clarity, autonomy, perceived value of work

• Collaboration signals: cross-team cycles, peer review frequency, mentoring time

• Customer trust indicators: complaint rates, rework, escalation frequency

What you measure becomes what you build.

3) Use AI to create more “high-value human time”

A strong AI program doesn’t just remove tasks—it creates space for the work that builds mastery and meaning:

• Deeper client conversations

• Better training and onboarding

• Higher-quality analysis and review

• Creative problem solving and innovation

• Stronger internal communication

If AI “saves time,” but the organization reinvests that time only into more volume and urgency, people will feel less purposeful, not more. The reinvestment strategy matters.

4) Build community into the workflow, not beside it

If you want connection, you need interaction patterns that make it inevitable:

• Human-in-the-loop review that is collaborative, not punitive

• Shared learning sessions (“here’s what the AI got wrong and what we learned”)

• Peer calibration meetings for high-stakes decisions

• Clear handoffs that include reasoning, not just outputs

Community is built through repeated, meaningful interactions—not announcements about culture.

5) Be transparent about role impact and skill pathways

Uncertainty kills trust. Responsible implementation includes clear communication about:

• What will change and what won’t

• Which tasks are being reduced

• Which skills become more valuable

• How the organization will invest in upskilling

• What “good performance” looks like in the new environment

The goal is to move from fear-based ambiguity to a shared plan.

The CommonWealth Data Solutions stance for 2026

At CommonWealth Data Solutions, our aim is to help organizations implement AI in ways that are both technically sound and socially responsible. In 2026, I’m making purpose and community explicit design goals in our work.

That means we will emphasize approaches such as:

• Workflow-first delivery: start from the human process and pain points, then choose tools

• Augmentation as a default: optimize for better decisions and better work, not just fewer workers

• Governance that includes human impact: role changes, accountability, and trust boundaries

• Adoption support: training, change management, and feedback loops as part of the build—not a separate effort

• Community-aware design: preserve and strengthen the collaboration patterns that make organizations thrive

If your AI initiative improves efficiency but leaves people feeling less capable, less connected, or less purposeful, the long-term cost will eventually outweigh the short-term gain. If, instead, your AI initiative makes people better at what they do—and strengthens the relationships that make teams work—you’ll get compounding benefits: better outcomes, higher retention, faster learning, and a healthier culture.

A call for 2026

I hope more leaders adopt this stance.

2026 will be pivotal not just for businesses, but for our communities and social fabric more broadly. Our pace of technological change is accelerating, and it demands thoughtful stewardship. I’m committed to being a voice—and a practitioner—who helps guide this evolution in ways that increase individual purpose and strengthen communities at the same time.

If you’re planning an AI initiative this year and want to pressure-test it through the lens of purpose and connection, I’m happy to compare notes.