DevReady PodcastHow Businesses Are Really Using AI in 2026: A Practical Guide to Scaling AI for Growth

Introduction

Artificial intelligence is no longer a future concept. In 2026, it is actively shaping how businesses operate, make decisions, and scale. In this episode of the DevReady Podcast, Anthony Sapountzis, CTO and Co-Founder of Aerion Technologies and DevReady.ai, is joined by Gareth Rydon, Co-Founder of Friyay.ai, to explore how companies are moving beyond experimentation and into structured AI adoption. This conversation breaks down real-world use cases, practical workflows, and the mindset required to unlock meaningful value from AI.

For business leaders, founders, and teams navigating the fast-paced AI landscape, this episode offers a grounded perspective on what actually works and how to apply it.

The Shift from AI Experimentation to Business Strategy

Many businesses have already started experimenting with AI tools across departments. Teams are using AI for coding, design, marketing, and internal communication. These early wins are valuable, but they often remain isolated within individual roles.

The real challenge now is scaling those successes across the organisation. Businesses are asking how to take individual productivity gains and turn them into repeatable systems. This requires a shift in thinking from ad hoc usage to structured implementation.

A successful AI strategy focuses on alignment. It connects tools, processes, and outcomes so that AI becomes part of the business infrastructure rather than a collection of experiments. Companies that make this shift are seeing stronger efficiency gains and clearer returns on investment.

How AI Is Being Used in Everyday Life and Work

AI is no longer limited to technical teams or specialists. It is becoming part of everyday life, both inside and outside the workplace. From answering questions through voice assistants to generating creative assets, AI is increasingly accessible to everyone.

Even younger users are interacting with AI tools for learning and exploration. This signals a broader shift in how people engage with technology. AI is becoming a natural interface for solving problems and finding information.

At the same time, safety and guardrails remain important. Features such as restrictions on image generation involving children highlight the balance between innovation and responsible use. These safeguards play a critical role in building trust and ensuring AI is used appropriately.

The Rise of All-in-One AI Platforms

The AI landscape is evolving rapidly, with new tools and features being released constantly. Platforms are expanding their capabilities, moving towards all-in-one solutions that combine multiple functions.

Tools that once focused on a single use case are now offering broader functionality. For example, coding tools are incorporating design capabilities, while general AI platforms are adding development features. This convergence is reshaping the competitive landscape.

As a result, the differences between tools are becoming less pronounced. Most leading platforms now offer similar core capabilities. This raises important questions about differentiation and long-term value.

For businesses, the key is not choosing the “perfect” tool but understanding how to use the tools effectively within their workflow.

Managing AI Overwhelm and Staying Focused

One of the biggest challenges in the AI space is the constant influx of new tools and updates. This creates a sense of urgency and pressure to keep up, which can quickly lead to overwhelm.

A more effective approach is to focus on mastering a small number of tools. By going deeper rather than broader, users can build confidence and achieve better results. This also reduces the cognitive load associated with switching between multiple platforms.

Filtering information is equally important. Many sources simply repeat announcements or benchmark data without offering practical insights. Following a small number of trusted voices who provide real-world examples can make a significant difference.

Progress in AI comes from consistent practice and application. It is not about knowing every tool but about using a few tools exceptionally well.


AI Coding Tools and Practical Workflows

AI-powered coding tools such as Codex, Claude Code, and Cursor are transforming how developers and non-developers approach software creation. While each tool has its own interface and nuances, their capabilities are increasingly similar.

The difference lies in how they are used. A structured, step-by-step workflow is essential for achieving reliable results. Breaking projects into smaller components allows AI to generate more accurate outputs and reduces the risk of errors.

For example, building a user interface can start with creating individual components, followed by assembling them into complete screens. This iterative approach ensures clarity and control throughout the process.

Rather than relying on AI to handle everything at once, successful users guide the process and refine outputs along the way. This leads to more consistent and scalable results.


The Importance of Clear Communication with AI

The most valuable skill in working with AI is not technical knowledge. It is the ability to communicate clearly and effectively. This includes defining goals, providing context, and reviewing outputs.

AI tools respond best to well-structured input. Describing the desired outcome in detail helps the system generate more relevant responses. Asking AI to clarify or expand on ideas can further improve results.

Another effective technique is to let the AI ask questions. By prompting the tool to “interview” the user, it can gather the information needed to build a more accurate plan. This approach is particularly useful when starting new projects or exploring ideas.

Reviewing outputs is equally important. AI-generated content should always be assessed and refined to ensure it aligns with the intended objective.


Planning, Focus, and Smarter AI Workflows

While AI enables faster execution, it also increases the importance of planning. Rushing into tasks without a clear plan can lead to confusion and inefficiency.

Taking time to think through ideas before using AI leads to better outcomes. This includes outlining goals, identifying key steps, and considering potential challenges. Tools such as whiteboards or notebooks can help organise thoughts before execution.

Managing context is another critical factor. Techniques such as branching conversations and clearing sessions help maintain focus and prevent information overload. These practices improve both efficiency and clarity.

Ultimately, AI works best as a collaborative tool. It enhances human thinking rather than replacing it. Businesses that embrace this approach are better positioned to achieve sustainable results.

Key Takeaways

  • AI adoption is shifting from individual experimentation to organisation-wide strategy
  • Most AI tools offer similar capabilities, so focus on mastering a few
  • Clear communication and structured prompting are essential for success
  • Planning and workflow design have a greater impact than execution speed
  • Managing context and reducing overwhelm leads to better productivity
  • AI is most valuable when used to enhance thinking and decision-making

Useful Links

Gareth Rydon | LinkedIn

Friyay.ai | LinkedIn

Friyay.ai | Website

FAQs

How are businesses using AI in 2026?

Businesses are using AI to improve productivity, automate workflows, support decision-making, and enhance customer experiences across multiple departments.

What is the best way to start using AI in business?

Start by identifying one or two use cases and focus on mastering the tools that support them. Build structured workflows before scaling further.

Are all AI tools the same now?

Most leading AI tools offer similar core capabilities, but the experience and workflow integration can vary. Effectiveness depends on how the tools are used.

How can businesses avoid AI overwhelm?

Limit the number of tools you use, follow trusted sources for learning, and focus on practical application rather than constant updates.

What skills are important for working with AI?

Clear communication, critical thinking, planning, and the ability to review and refine outputs are key skills for effective AI use.

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