How do you bring people together to do better work when everything around them feels increasingly complex, distributed, and uncertain?

In today's episode, I sat down with Jessica Guistolise from Lucid Software, and what struck me straight away was her belief that work has always been a group project, even if many organizations still behave as though it is not.

Jessica shared how much of the friction we experience at work comes from misalignment, unclear expectations, and a lack of shared understanding. When teams are spread across time zones, systems, and now AI-powered workflows, those gaps only widen. Her perspective is simple but powerful. When people can actually see the work, rather than interpret it through documents, meetings, or assumptions, something shifts. Conversations become clearer, decisions become faster, and collaboration starts to feel human again.

We also explored how visual collaboration platforms like those from Lucid Software are helping teams move away from scattered tools and disconnected workflows toward a more unified way of working. Jessica described it as having everything on one workbench, where teams can brainstorm, plan, and execute without constantly switching context.

What really stayed with me was her focus on inclusivity in collaboration. Not everyone contributes in the same way, and visual environments can create space for different thinking styles, whether someone is outspoken, reflective, or somewhere in between. That idea of creating a shared language across teams, roles, and even personalities feels increasingly relevant in a world where communication often breaks down.

Of course, no conversation right now would be complete without talking about AI. Jessica offered a refreshingly honest view. There is uncertainty, and there should be. But rather than avoiding it, she believes leaders need to make AI visible, map how it is used, define where human judgment matters, and encourage teams to experiment openly.

One of the most interesting ideas she shared was reframing mistakes as early learnings. When teams feel safe to test, fail, and share what they discover, progress accelerates. When fear or blame enters the picture, everything slows down.

We also touched on AI literacy and what it really means in practice. For Jessica, it comes down to clarity. Clear workflows, clear guardrails, and clear expectations about accountability. AI might assist, but humans remain responsible for outcomes. That mindset, combined with leadership that actively participates in experimentation, creates an environment where people feel confident stepping forward rather than holding back.

This conversation left me thinking about how many organizations are still trying to layer AI onto unclear processes and expecting better results. Jessica's message is that clarity comes first, then technology can amplify it.

So if work really is a group project, are we giving our teams the visibility and confidence they need to succeed, or are we still asking them to figure it out in the dark?

What happens when the very pricing model meant to speed up AI adoption ends up slowing it down?

In this episode of Tech Talks Daily, I sit down with Sameet Gupte, CEO and co-founder of EvoluteIQ, to discuss a part of the enterprise AI story that still doesn't get enough attention.

While so much of the conversation around AI focuses on models, copilots, and the latest agentic promises, Sameet brings the discussion back to a business reality that every enterprise leader understands. If the economics do not work, adoption stalls. And if success in a pilot makes the final rollout even more expensive, something has gone wrong long before the board signs off on scale.

Sameet argues that many organizations are still trapped by legacy pricing structures built for an earlier generation of automation. Per-user and per-bot pricing may look manageable at the pilot stage.

Once a company tries to expand automation across departments, processes, and geographies, the numbers can quickly stop making sense. That creates what many now call pilot purgatory, where a company proves something can work, but cannot justify taking it any further. It is a problem rooted in incentives, procurement, and fragmented technology stacks, and it is one that CFOs are watching very closely.

What I found especially interesting in this conversation is how Sameet frames the issue. He believes most enterprises do not actually have an automation problem. They have an orchestration problem.

In other words, the challenge is rarely a lack of tools. It is getting all the systems, workflows, approvals, data flows, and legacy infrastructure to work together to produce a clean business outcome. That idea changes the conversation from buying isolated features to rethinking the process as a whole.

We also discuss why outcomes-based pricing is increasingly resonating with enterprise buyers. Sameet explains why predictable costs, transparent commercial models, and shared accountability are helping move automation conversations out of innovation teams and into the CFO's office.

For public companies and large global enterprises, that matters. Leaders want fewer surprises, fewer overlapping vendors, and a much clearer line between spend and return.

There is also a broader theme running through this episode about where the market is heading next. Sameet sees real urgency around vendor consolidation, enterprise simplification, and the need to rethink how AI is introduced into the business. His view is that companies need to pause, define what they actually want AI to do, and then choose tools that fit the business, rather than reshaping the business around the latest platform pitch.

If you are trying to make sense of AI adoption beyond the hype, this conversation offers a practical and timely perspective on pricing, scale, and what real transformation could look like inside the enterprise.

After listening, do you think the future of enterprise AI will be shaped as much by commercial models as by the technology itself, and what are you seeing in your own organization?

Useful Links

• Connect with Sameet Gupte, CEO and co-founder of EvoluteIQ
• Learn More About EvoluteIQ

How many bad customer experiences does it take before someone walks away for good? In my conversation with Amitha Pulijala, we explore why the answer might be fewer than most businesses are prepared for, and what that means for anyone investing in AI-powered customer experience.

New research from Cyara reveals a stark reality. Twenty-eight percent of consumers will abandon a brand after just one poor interaction, and nearly half will do the same after only two or three. That leaves very little room for error at a time when more organizations are introducing AI into customer journeys, often at speed and at scale.

Amitha, who leads product strategy in the AI and CX space, brings a grounded perspective shaped by years of working with large enterprises and complex contact center environments. What stood out in our discussion is how the real challenge is no longer about whether AI can handle customer interactions. In many cases, it already can. The issue is whether customers trust it enough to let it try.

We unpack the growing perception gap: 73 percent of consumers still believe human agents resolve issues faster, even though AI systems can deliver near-instant responses. That disconnect often comes down to past experiences, from bots that fail to understand context to systems that trap users in frustrating loops with no clear way out.
There is also a clear line that customers draw around where AI belongs.

Routine, high-volume tasks such as password resets or appointment confirmations are widely accepted. But when conversations shift toward financial security, healthcare, or legal advice, expectations change. People want human judgment involved and reassurance that the outcome is reliable.

What makes this conversation particularly relevant is the generational divide shaping expectations. Younger users are far more open to AI-led interactions, provided they work seamlessly. Older generations remain more cautious, often preferring the certainty of speaking with a human. That creates a design challenge for businesses trying to serve everyone without alienating anyone.

Throughout the episode, Amitha emphasizes that trust is built through experience, not intention. That means testing AI systems in real-world conditions, monitoring how they perform over time, and ensuring that when things do go wrong, the transition to a human feels smooth and informed rather than abrupt and frustrating.

This is not a conversation about replacing humans with machines. It is about understanding where AI can add speed and efficiency, where it should support human agents, and where it should step back entirely. The organizations getting this balance right are not the ones deploying AI the fastest, but the ones validating it most carefully before customers ever see it.

As businesses race to embed AI at every touchpoint, a bigger question emerges. Are we building systems that customers actually trust, or are we creating new points of friction that push them away?

Useful Links

• Connect with Amitha on LinkedIn
• Survey Data
• Cyara Website
• Follow Cyara on LinkedIn