Solwey

09/15/2026

PwC's 2026 AI Business Predictions reinforce a trend we're seeing across every industry:

The companies creating the most value with AI aren't adopting more tools. They're changing how their business operates.

One prediction stood out in particular: the AI leaders of 2026 won't spread investments across dozens of disconnected use cases. Instead, they'll take an enterprise-wide approach, focusing on the workflows that create the greatest business impact and building AI into the way work gets done.

We couldn't agree more.

Too many organizations are still measuring AI success by the number of pilots they've launched or the number of employees using AI. Those are adoption metrics, not business metrics.

At Solwey, we believe AI should be measured by outcomes:

• How much manual work was eliminated?
• How much faster do teams execute?
• How many operational bottlenecks disappeared?
• How much more efficiently does the business run?

The competitive advantage isn't having access to the latest model. It's redesigning operations so AI can orchestrate workflows, connect systems, and execute work across the business.

Great report: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html

09/10/2026

AI can generate code in seconds. No-code platforms can launch applications in days. So why are companies still investing in custom software?

Because building software faster and building the right software are two very different things.

Off-the-shelf platforms are great at solving common problems. But the processes that give a business its competitive advantage are rarely generic. They live in the way teams operate, how information moves, how customers are served, and how decisions get made.

When standard software can’t support those processes, businesses often end up adapting themselves to the technology. Teams create workarounds, move data between systems, maintain massive spreadsheets, and add more integrations just to keep everything connected.

Custom software reverses that relationship. Instead of changing the business to fit the tool, the technology is designed around how the business actually needs to work.
AI is making custom development faster too, but speed doesn’t replace the thinking that happens before development begins. Someone still needs to understand the business problem, challenge assumptions, design the architecture, and decide what should actually be built.

That’s where custom software still has a very clear role.

Use existing tools when the problem is standard. Build custom when the way you solve the problem is part of what makes your business different.

More in our blog: https://www.solwey.com/blog/why-companies-still-invest-in-custom-software

09/03/2026

For the past two decades, the modern data stack has been built around one assumption: the end goal is a dashboard.

Data is collected, transformed, analyzed, and visualized so people can decide what to do next. The insight is generated, but the action still depends on someone opening another application, updating a record, sending an email, or launching a workflow.

That's becoming the biggest bottleneck.

AI agents are changing the role of enterprise data. Instead of simply identifying a problem, they can interpret the signal, verify what's happening in operational systems, determine the appropriate response, and execute it. The data warehouse becomes more than a reporting layer - it becomes the starting point for intelligent action.

Think about a customer showing signs of churn. Traditionally, an analyst or customer success manager reviews a dashboard, investigates the account, decides on a retention strategy, updates the CRM, and reaches out. Every step introduces delay.

An AI agent can perform much of that work automatically. It can validate that the account is still active, check for recent support activity, select the right retention workflow, create follow-up tasks, and keep humans involved only when approvals or strategic decisions are required.

This is a fundamental shift in how organizations operate. The value of enterprise data is no longer measured by how quickly it produces insights, but by how quickly those insights turn into meaningful business outcomes.

More in our blog: https://www.solwey.com/blog/beyond-dashboards-why-ai-agents-are-the-next-logical-step-in-the-data-stack

09/01/2026

Deloitte's latest State of AI in the Enterprise 2026 report confirms something we've been seeing across the market.

Enterprise AI is no longer a question of if. It's a question of how.

Worker access to AI has increased by 50%, companies expect to double the number of AI projects in production over the next six months, and agentic AI adoption is accelerating rapidly.
But one statistic stood out to us more than any other:

Only 34% of organizations are truly redesigning their business around AI.

That's the gap.

Many companies are adding AI to existing processes instead of rethinking how work gets done. They're deploying copilots, experimenting with agents, and giving employees access to powerful models, yet the underlying workflows remain the same.

AI becomes another tool instead of a new operating model. That means connecting systems instead of creating new silos. It means orchestrating workflows instead of generating reports. It means building governance, security, and ex*****on into AI from day one, not after deployment.

The companies creating the most value will be the ones willing to rethink how their business operates. That's where AI stops being an experiment and starts becoming a competitive advantage.

Great report: https://www.deloitte.com/us/en/what-we-do/capabilities/applied-artificial-intelligence/content/state-of-ai-in-the-enterprise.html

08/27/2026

One of the biggest misconceptions about AI in product development is that it helps teams build faster.

It does. But that's not where the biggest value comes from.

The real advantage is that AI helps teams learn faster.

Too many organizations have optimized product delivery while neglecting product discovery. They ship features efficiently, yet many of those features never deliver meaningful customer value. The problem isn't ex*****on. It's building the wrong thing.

AI can dramatically accelerate customer research, analyze thousands of support tickets and user conversations, identify recurring pain points, generate prototypes in hours, and help teams validate ideas before engineering resources are committed.

But AI doesn't replace product judgment.

It amplifies whatever discovery process already exists. If teams are asking the wrong questions, AI helps them move in the wrong direction faster. If they're grounded in customer needs and disciplined experimentation, AI compresses the time between insight and validation.

More in our blog: https://solwey.com/blog/what-is-ai-powered-product-discovery-and-how-can-it-reduce-the-risk-of-building-the-wrong-features

08/25/2026

Most AI projects never make it past the pilot phase. Yours shouldn't.

08/20/2026

When people talk about AI in manufacturing, the conversation usually starts with automation.

It should start with quality.

One of the most expensive problems manufacturers face isn't finding defects - it's how long it takes to understand them. By the time teams connect warranty claims, service notes, production records, supplier data, and sensor logs, months may have passed. Meanwhile, the same issue continues to make its way into the field, driving up warranty costs and eroding customer trust.

This is where AI creates real business value.

Not by replacing quality engineers, but by accelerating the work that slows them down. AI can detect early warning signals, connect data across disconnected systems, identify likely root causes, and surface the issues that matter before they become costly.

That changes the role of quality teams. Instead of spending weeks collecting evidence, they can spend their time making decisions, implementing corrective actions, and improving products.

And in today's market, the speed at which you respond to customer issues may become one of your strongest competitive advantages.

More in our blog: https://solwey.com/blog/how-is-ai-changing-manufacturing-quality-management

08/18/2026

For the past few years, the AI conversation has been dominated by chatbots, copilots, and productivity tools.

According to a new report from Goldman Sachs, shared exclusively with Axios, the next AI boom won't happen on our screens. It will happen in the physical economy - factories, utilities, mines, warehouses, and industrial operations.

We couldn't agree more.

The biggest opportunity for AI isn't helping employees write faster emails or summarize documents. It's helping businesses run better.

Imagine AI that identifies production bottlenecks before they impact output, optimizes scheduling based on real-time constraints, coordinates maintenance across facilities, or orchestrates work across ERP, MES, CRM, and operational systems without manual intervention.

That's where the next wave of productivity gains will come from. The future of enterprise AI isn't just digital. It's operational.

Great article:

AI has made technology companies increasingly dependent on non-tech firms.

08/13/2026

One of the biggest mistakes B2B startups make is leading with their product.

Customers don't buy technology because it's impressive. They buy because it solves a problem they care about, reduces risk, and delivers a measurable outcome.

Founders spend months perfecting features, while buyers are asking completely different questions:

-Will this fit into the way we work?
-Can we trust this company?
- Is the ROI worth the investment?
-Will this make my job easier - or more complicated?

A great product is essential. A great go-to-market strategy is what turns it into a business.

More in our blog: https://solwey.com/blog/a-practical-guide-to-avoid-go-to-market-failure-from-first-principles-to-ex*****on

08/11/2026

AI success isn't just about building models. It's about building the right process.