No matter your company’s size or industry, these tried and tested ideas can help you avoid the most common pitfalls during AI implementation.
Many organizations are in the process of implementing AI or generative AI (GenAI) solutions, and many others are still trying to decide where to even start. No matter where your organization is in the AI adoption process, our AI transformation team has ideas that can help you avoid the most common AI implementation mishaps and set a path for a successful AI transformation.
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1. Don’t start with too much, too fast – and rightsize your AI projects
The biggest mistake we see companies making – by far – is starting their AI journeys with overly ambitious projects. Often prompted by a top-down mandate from the board or chief executive officer to implement AI, companies develop massive projects that can take six months to a year (or more) to complete. In the long run-up to deployment, the people in charge of these implementations can often lose sight of why the project began in the first place. As teams get bogged down in the complexities, that loss of momentum can lead to a growing sense of skepticism regarding whether the implementation can deliver the promised return on investment and might derail the whole project.
To avoid implementation fatigue, we recommend starting small with practical, manageable use cases. Companies can use AI on a small scale in a number of ways and start realizing value in just two to four weeks. Identifying use cases and achieving quick wins first provides a foundation of success to build on for future, larger projects. By starting with something achievable instead of the biggest possible projects, companies can use quick wins to help develop a long-term AI vision.
2. Take a top-down as well as a bottom-up approach to target AI implementation at every level
For AI initiatives to take hold within an organization, a dual approach of top-down leadership and bottom-up employee engagement is critical. Without top-down executive buy-in, AI initiatives lack the strategic vision and resource commitment needed for success. And without bottom-up involvement from frontline employees, initiatives can fall behind on adoption. A top-down and bottom-up approach provides a strong support system coupled with enthusiastic adoption, which can drive innovative use cases grounded in the day-to-day needs of the company.
How are we doing this at Crowe? We’ve seen great success with this approach, as it’s the one we use at Crowe. We’ve made AI a top priority at the executive level, with the board and leadership championing the transformative potential of this technology. And we complement our executive sponsorship with comprehensive efforts to educate and enable our people through educational sessions, working groups, and specialized “guilds” focused on AI. In this way, we empower our people to understand how AI can enhance their roles and contribute ideas for practical applications. By giving our people a voice and avenue to explore AI’s possibilities, we’ve seen an outpouring of creative use cases emerge from those closest to the work, which we can then bring to the executive team as a win and a case for additional support.
3. Give tangible examples of AI adoption with trainings and demos
People who are unfamiliar with the latest AI capabilities can sometimes exhibit skepticism or resistance. But the companies that illustrate AI’s practical benefits through tailored demos and proofs of concept find they’re often able to convert skeptics into enthusiastic advocates. Providing tangible demonstrations of how AI can streamline daily tasks and boost productivity is another great way to get people experimenting with AI in a low-stakes, high-reward environment. These trainings and demonstrations should cover a variety of AI-related topics, be given on a regular cadence, and be open to all employees so everyone can participate.
4. Pick a platform that already offers AI capabilities and maximize its offerings
The AI startup landscape is booming, with hundreds of new companies emerging each month promising revolutionary AI solutions. This crowded market can quickly become confusing and overwhelming for businesses that want to incorporate AI into their operations. Rather than getting distracted by the latest shiny AI tool or chasing every new startup, companies should consider using mature, scalable platforms they already rely on and trust.
Major technology players have been investing heavily in AI capabilities for their platforms, and many have recently rolled out AI-related enhancements and products. By choosing an AI-enabled platform that is already integrated into an existing tech stack, companies can avoid the headaches of disjointed app sprawl as well as some of the security risks that come with implementing a patchwork of disparate AI tools from various vendors.
The key to success is thoroughly exploring and exhausting the AI capabilities of a chosen platform before considering additional third-party tools or apps. This approach allows for a streamlined integration, with all AI components talking to each other within established systems. It also allows companies to build AI competency on a solid foundation and makes it easier to scale and expand AI efforts over time.
The AI landscape will only continue to get bigger, more complex, and more confusing. While it’s vital to get your company started, it’s also important to work with a team that understands the ins and outs of the technology, the options for implementation, and the possibility for transformation.
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