Four Steps to Go from Experimentation to Embedding AI Across the Enterprise

AI is everywhere. In just a couple of years, this technology has evolved significantly and is transforming the way most of us do business. And yet, many organizations continue to grapple with how they can really integrate AI into their daily operations. It’s critical that this changes soon.

To thrive in the age of AI, companies must do more than simply adopt AI. They must embrace an iterative approach, continuously learning and adapting as the technology evolves. In this article, I’ll share four commitments that companies should make to transition to full AI adopters.

Understand Your Business Challenges

AI for the sake of AI only adds more tools to your tech stack. Before you can talk about how your organization is going to use AI, it’s critical to first understand the problems your business is facing.

Is there a bottleneck in your operations? Are you struggling to make sense of overwhelming amounts of data? Do you need more personalized customer engagement strategies? Or are there bigger questions, like how to differentiate yourself in your industry?

Understanding these challenges will help you determine where AI can have the greatest impact and ensure that its integration delivers real business value.

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Study How AI can Help Solve Business Challenges

Once you’ve identified your business challenges, it’s time to think about how AI can help address them. AI can contribute to solving challenges at different stages of its adoption. To fully realize AI’s value, organizations must understand the three phases of AI adoption.

Phase 1: Operational efficiency (AI as an assistant)

In this initial phase, AI is used primarily to improve efficiencies by assisting employees with tasks like content creation, data analysis and summarization, and thought partnership.

AI acts as a tireless assistant, boosting individual productivity — from marketers using ChatGPT to generate initial drafts of content to finance analysts using AI to compile reports, identify trends, and flag potential risks.

Phase 2: Workflow automation (AI as an optimizer)

As businesses gain more experience with AI, they move into optimizing processes. In this phase, AI is integrated into workflows to automate broader business processes, improving cross-departmental collaboration and overall efficiency.

AI now starts to impact teams, not just individuals. For example, product teams use AI to synthesize customer feedback in real-time and then use AI to convert that unstructured data into a structured product brief in a matter of minutes, not days.

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Phase 3: Agentic AI (AI as a performer)

When people talk about AI today, they talk about it through the lens of either phase one or two. But, the next phase is already here: AI operating autonomously. Examples include AI-powered customer service agents, AI-led marketing campaigns, and even AI tools that manage entire business functions. In this phase, AI takes over tasks that previously required human intervention, allowing employees to focus on more strategic initiatives.

Whatever phase your organization falls in, it’s important to not silo your AI tools. They must be inter-connected across your different platforms to have widespread adoption and impact.

Address Barriers to AI Adoption

As with any new technology, there will be factors that can get in the way of adoption. Consider the people, processes, and/or tool challenges that can slow innovation and growth. Whatever those problems are, they may also prevent an organization from embedding AI across the enterprise.

Some common barriers are:

Functional silos and fragmented processes: To break down this barrier, organizations must champion cross-departmental collaboration, standardize workflows, and create a culture of transparency. Aligning goals and using inter-connected tools enhances efficiency and ensures smoother, more integrated operations across the board. The good news is that enterprise leaders seem excited and optimistic about AI’s potential impact on collaboration, with one in three saying that they would like to use AI to help teams work better together — and, in turn, innovate faster — in a recent Miro survey.

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Education: Microsoft found that 78% of AI users bring their own AI tools to work, but its impact is limited when these efforts are isolated among individuals and their teams. According to their survey, leaders recognize the value of AI, but “the pressure to show immediate ROI is making [them] move slowly.” To embed AI across an organization, it’s crucial to provide everyone with access to AI tools and ensure that they understand when and how to use them.
Culture: Organizations must cultivate a culture where employees feel safe to make mistakes as they learn to use AI. And yet, Miro found that more than one in four leaders say that their organizations lack a culture of experimentation, which gets in the way of innovation. Encouraging experimentation and fostering psychological safety around AI adoption will help employees embrace the technology and push its boundaries. On the individual level, using AI should feel exciting and as if there’s value derived from using it.

Focus on Privacy and Security Concerns

Last, but certainly not least, think about the privacy and security concerns that come with AI. As organizations integrate AI, CISOs and generals counsels alike cite security as a major — perhaps, the greatest — concern when it comes to deploying this technology. They’re right. Despite all its benefits, AI does come with potential risks, including potential data manipulation, privacy breaches, and model vulnerabilities.

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To mitigate these risks, organizations should develop strong AI governance policies, conduct regular audits, and stay informed about evolving threats. Transparent communication and ongoing education, combined with frequent reviews of security practices, ensures that AI can be deployed confidently while upholding the highest security and privacy standards.

While it’s crucial to be vigilant, AI also should be seen as an asset to enhance security. AI can significantly improve enterprise security through tasks like identifying and classifying sensitive information, detecting anomalies, and providing advanced threat intelligence.

AI-powered systems can help automate repetitive security tasks, creating more space for driving strategic work. By integrating these capabilities into your cybersecurity framework, AI not only strengthens your defenses but also helps maintain compliance with evolving regulations.

Evolve Together

By following these four steps — understanding your business challenges, identifying AI solutions to those challenges, addressing the barriers to adopting AI, and mitigating privacy and security risks — organizations can move from just tinkering with AI to making it central and integral to an organization’s operations. Each step is essential to unlocking AI’s full potential and ensuring it benefits all teams.

Embedding AI throughout your organization removes constraints and inefficiencies, allowing teams to innovate quickly and freeing people to be more creative. But know that AI is not a silver bullet for all of a business’s problems. We still need human interactions to gauge and respond to the challenges organizations face. AI simply plays a key role in turning those problems into opportunities for innovation and growth.

About the author: Jeff Chow is the Chief Product & Technology Officer at Miro. He has over 25 years of experience building high growth organizations focused on delivering customer-centric digital products. He is passionate about building a team culture where collaboration and quick problem solving contribute to transforming a good business to a great one. Prior to Miro, Jeff was the Chief Executive Officer and Chief Product Officer at InVision, and held leadership roles in Product and Product Design teams at Google and TripAdvisor. Jeff has founded, run, and exited several startups in mobile, consumer, and marketing industries. Jeff received his BS in Mechanical Engineering at MIT.