Map your company’s AI adoption progress and next steps using our framework
This article was adapted from a piece written by Volaris Group‘s VP of Integration and Strategy, Jeff Chow.
With developments in AI evolving rapidly in recent years, it’s not surprising that businesses, their leaders, and teams can find themselves stuck too long in one stage of the AI adoption journey, or with team members not on the same page.
By applying a framework to your company’s AI maturity journey, you can more accurately pinpoint your team’s progress on AI adoption, identify the challenges your team might face getting on the same page with your company’s AI strategy, and map your company’s next steps.
Assessing your organization’s AI maturity
As AI continues to evolve, the Volaris gold standard is for all our company leaders to be on the leading edge. This goal requires moving our businesses toward the advanced stages of AI adoption.
We see AI adoption within a company as happening over four distinct stages. From least to most mature, these stages are:
- Skeptic: Company leaders are talking about AI, but not in a strategic or actionable way. ROI on AI tools is not yet understood, so there is no tracking of ROI. Employees may believe that AI is a fad.
- Incrementalist: Company leaders are talking about how AI can make incremental changes, and a few teams have deployed a couple of their favorite off-the-shelf AI tools within the company. Some experimentation is taking place among employees, who believe that AI has the potential to be valuable within the business. Understanding and tracking of ROI is still limited.
- Adopter: Company leaders are reporting regularly on their AI progress. All employees are finding ways to leverage AI capabilities. The company has adopted advanced AI tools across all teams and embedded AI into its business processes and product roadmap. ROI is clearly tracked.
- Evangelist: Company leaders proactively invest in AI, seeing it as a competitive advantage. The company has adopted an AI-first approach to business processes and product development. Employees have developed a T-shaped skillset and a growth mindset. As the company moves forward, teams are pushing the boundaries of what is possible with available AI tools. Companies have a clear understanding of ROI and are tracking it.
Companies that have successfully reached the more mature phases of AI adoption can tap into the most transformative mindset for their business while maximizing value.
How to get to the next level of AI maturity
Advancing along the AI learning journey doesn’t always happen in a straight line, as our Volaris strategy and integration teams have seen while they are helping our businesses navigate their post-acquisition experience. Instead, it’s often a complex journey for leaders and their teams to develop informed confidence and conviction in their decision-making.
Common pitfalls in the earlier stages of the AI maturity journey include:
- Unrealistic optimism about the capabilities of AI tools can be fueled by hype, but without a true understanding of the AI landscape can result in broad and uninformed mandates
- After being confronted with the realities and unknowns related to AI implementation, such as talent shortages, data challenges, and ethical risks, conviction can plummet—trapping leaders in inaction with doubt and insecurity
To get to the more mature stages of AI adoption, a business needs its leaders and employees to embrace AI, and all staff need to gain practical, grounded knowledge. They can do this by:
- Transitioning their focus from “knowing it all” to knowing what’s needed—a shift that requires asking better questions, building a strategic team, and focusing on solving specific business problems rather than simply chasing every new AI tool
- Engaging in formal learning through courses and training programs that introduce AI fundamentals
- Participating in social learning with peers to build momentum, get inspired by real-world examples, and learn from the successes and failures of others
- Moving from theory to application through experiential learning
How your business can overcome key risks and challenges
Volaris is helping our businesses overcome key challenges and risks with the support, guidance, and resources within our network. These include:
- Regulatory and compliance risks: Regulatory constraints can pose a significant challenge in industries such as healthcare and banking, where data is sensitive and acceptance of AI in customer-facing applications is critical. Volaris has created templates to help our businesses assess and manage risk, providing a starting point for safely adopting AI technologies.
- Data security and confidentiality: Cybersecurity threats and protecting customer data are two significant concerns. Misinformation and inaccuracy of AI-generated content can also pose harm to a company’s reputation or operations. For businesses in the Volaris network, we have developed a set of AI governance resources and recommendations that assess the risks of current AI tools with frequently updated security reports.
- Organizational readiness and change resistance: Cultural resistance and fear can prevent timely AI adoption, particularly in legacy organizations. Learning about AI and conducting small experiments can be the best way to break the resistance barrier. Leaders can also dispel fears of job loss by reminding teams that AI is meant to help with painful, repetitive tasks, not to replace people.
- Competitive pressure and innovation urgency: Many businesses believe they must act quickly to avoid falling behind their competitors. However, the sense of urgency needs to be accompanied by a competitive analysis to ensure customers are on board with new AI product elements.
- Talent and capability gaps: Hiring and retaining talent with the suitable mindset and skills to leverage AI becomes a focus for many leaders. Ensuring employees are upskilled can help businesses get ahead of this challenge.
- Market disruption and product cannibalization: Leaders may fear that AI’s ability to replicate less complex products could lead to product attrition. Meanwhile, customers may be tempted to use AI to reduce their reliance on vendors. Having open conversations with customers can help them understand what AI can help them bring in-house versus what value they can get from partnering with vendors.
- Structural inertia: A risk-averse culture can lead to inaction. Often, management incentives can create opposition to an initial investment in AI. Inertia can be overcome with fast and inexpensive experiments that show where AI can make a difference—or not.
- Underestimation: Some leaders expect that it is easy to unlock AI potential and start seeing benefits immediately. However, until teams start to experiment with AI, they can’t truly understand the impact or effort involved.
From our experience, the businesses that address these key challenges and risks on their way to AI maturity are most capable of empowering their teams to move forward with confidence, support an AI-first transition, and champion the integration of AI into the business.
