5 Best Practices for Implementing AI in Agile Organizations

Agile organizations understand the importance of implementing AI in their teams to boost productivity and improve their bottom line–leaving many leaders with a loaded, time-sensitive question: where do we start?

The recent and exponentially growing popularity of generative AI and its limitless applications brings with it excitement, fear, confusion, and a race to optimization.

But don’t worry—we’ve consulted the experts, spent countless hours researching, and even designed a foundational AI course, so ICAgile has your back in the AI department.

We sat down with our CEO and Director of Learning to explore some of the foundational practices agile organizations can utilize to develop an effective AI strategy that is tailored, adaptable, and meets the mark.

Why AI & Agile?

Artificial intelligence isn’t going anywhere. On an individual and organizational level, growth and success will hinge on who can effectively approach and manage AI strategies. “It’s not that AI is coming to take anybody’s job, but the workers of the future who know how to leverage machines to accelerate tasks will be,” says Christina Hartikainen, Director of Learning at ICAgile.

Businesses stuck in traditional, non-adaptive ways of working are falling behind. In the modern market, artificial intelligence is not an option but a necessity to keep up with competitors and accelerate value delivery.

Organizations that practice an agile mindset are in an advantageous position for AI adoption. From working iteratively for continuous improvement and collaborating in cross-functional teams to promoting a culture of learning and leadership–agile teams are seasoned in approaching new challenges with curiosity and reason.

5 Best Practices for AI Implementation in Agile Organizations

Embrace AI Strategically, not Reactively

Shannon Ewan, CEO of ICAgile, shares the importance of adopting an agile culture as the first step in building an artificial intelligence strategy. “Leaders that have invested in the organizational, structural, and cultural changes necessary for agility will be far better-positioned to embrace AI strategically, as opposed to reactively.”

Ewan emphasizes the critical nature of decentralized decision-making, noting that autonomous teams are empowered to solve the problems closest to their area of expertise. In contrast, managers in an autocratic workplace may never collect insight or employee feedback regarding tools and processes, leading to an untailored AI strategy that doesn’t fit a team’s unique functioning.

When developing a plan to implement AI within your team, remember the goal is to drive positive outcomes for all stakeholders and optimize organizational workflow, allowing your team to focus on the tasks that make the most impact.

There is no collection of tools that drives the most success because each business has exceptionally different needs. Hartikainen adds, “If you do AI for AI’s sake, you could get caught up in a constant cycle of chasing the next shiny thing without actually making any impact or progress on what you’re trying to achieve.”

Continuously Adapt Your AI Approach

Continuous improvement is one of the hallmark practices of an agile mindset, and when it comes to tech tools, there are endless opportunities to learn and adapt. The speed at which AI is changing is unprecedented, and in deciding to embrace AI technologies within your business, the journey has only just begun.

New AI tools are hitting the market every day, and new regulations around the technology are on the horizon. Teams should avoid getting too attached to particular tools and be open to changes and experimenting. Hartikainen notes how many AI tools and their features are “here today and gone tomorrow,” with a rate of change at an all-time high. Agile teams must be ready to inspect, adapt, and update their approach to AI often.

Welcome Machines as Part of Your Team

Artificial intelligence provides far more capability than to be considered a set of tools–agile organizations should welcome machines as a valued part of their team. AI has drastically increased human potential to innovate in every market. “Machines are producing outcomes that are actively influencing human and collective intelligence, so it’s thinking about how we can use AI to bring out the best in everyone,” describes Ewan.

The symbiotic relationship allows AI and its users to continue learning from one another; however, humans should still evaluate AI results critically–the same way we conduct peer reviews of each other's work. After all, artificial intelligence has limitations. “Understanding how these systems are trained and how they learn bias” is critical to making sense of AI-generated data, explains Hartikainen.

Consider Ethics

Ethics is a big topic of discussion around artificial intelligence, and rightfully so. Agile organizations should exercise caution when making decisions related to AI that may impact areas such as bias, job loss, and privacy & compliance.

Ewan highlighted the prevalent concerns around job loss, explaining that each international market is structured differently, and we need to consider how AI affects the global workforce. “We have a responsibility to think about these impacts and norms that we’re setting to discover how humans and machines can work together to do things that neither could do alone.” She suggests that agile organizations should incorporate AI to amplify their teams' existing talents rather than looking to replace human workers.

Other ethical factors, such as privacy, compliance, and intellectual property usage also influence AI-related organizational decisions. For example, Hartikainen points out that some AI-powered models have been trained using copyrighted material, contributing to a prominent problem lawmakers are working to solve.

Understanding the ethical nuances of AI tools is vital, leading some teams to invest significant energy into the effort, such as appointing AI ethics officers or leading AI ethics initiatives dedicated to the cause.

Establish a Shared Language Around AI

By establishing a shared language around AI, teams have the preliminary knowledge and context required for strategic planning, adaptation, and productive ongoing conversation.

“Education and learning are absolutely critical,” says Ewan. She describes how agile organizations should embrace a learning culture to reduce the fear surrounding AI in the business world. Teams can research, read, take a course, develop policies and procedures, and practice transparency around AI knowledge and implementations in the workplace.

ICAgile recently developed the first-ever foundations-level AI course for agile organizations. Rather than recommend a specific set of tools that will be here today and gone tomorrow, thought leaders have put together a comprehensive course on how global organizations can build a tailored AI strategy that considers historical knowledge, compliance, and continuous adaptability in an ever-changing industry.

With an incredible amount of information available about AI, we recommend starting with the basics. By creating a strong foundation to build on, teams will be in the proper position for new technologies and tools to be released.

Conclusion

By following these five best practices, your team will be ready to turn the unknown into understanding and implement AI with a blueprint in place.

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5 Best Practices for Implementing AI in Agile Organizations

Why AI & Agile?