AI Readiness: 8 Critical Steps for Preparing a Successful Implementation

By Arun Kumar, Global Lead, Data, Analytics + AI at Material

Organizations of all shapes and sizes are racing to incorporate AI into their workflows and operational processes. This sense of urgency may be why a recent study from BCG indicates that 74% of companies using the technology have yet to generate real value from it.

Among the many common roadblocks to measurable AI business impact, a lack of bedrock organizational preparedness is one of the most critical to understand and address. Just as laying a solid building foundation is essential to supporting a house, establishing a multidimensional organizational foundation is crucial to successfully implementing and scaling AI.

Organizations should consider eight key upfront steps to ensure their foundation for AI success and impact is solid.

1. Map current AI usage and aspirations.

Certain teams in your organization may already be using AI tools, either company-sponsored technologies or free offerings such as ChatGPT and MS Copilot. Identify the tools employees are using, their purposes and their perceived effectiveness. At the same time, determine the business objectives you want AI to help you achieve. Not only should these goals and objectives align with overall business strategy, but they should also be specific and well defined. Just about every organization wants AI to help it boost revenue and reduce cost. Success lies in establishing how AI will empower that goal: By improving customer segmentation? Surfacing new insights from historical data troves? Enabling dynamic pricing?

2. Identify frictions, pain points and gaps in existing processes.

As you did when auditing AI usage, ask employees for their input. Don’t rely solely on managers; get feedback from those in the proverbial trenches regarding operational bottlenecks, inefficiencies and other issues. In addition, track metrics such as time spent on routine tasks, customer satisfaction scores and error rates. Visually mapping processes from beginning to end can further clarify areas ripe for AI-powered improvement and optimization.

3. Pinpoint specific opportunities where AI can add business value.

AI isn’t a panacea to all business ills, and one AI solution won’t resolve issues in every department and process. An agentic customer service tool that relieves call center employees from answering fundamental queries, for example, is unlikely to also detect anomalies that indicate potential security breaches. You need to determine which areas are most in need of improvement, which initiatives will have the greatest impact and which projects can deliver early wins. Then it’s a matter of uncovering which AI applications can best assist in achieving those objectives.

4. Understand the organization’s cultural readiness for AI adoption.

Many organizations are resistant to change – especially if certain behaviors or processes led to success in the past. And many employees are especially resistant to AI, fearing it will take their jobs or they won’t be able to learn to use it effectively. Surveying staff at all levels of the organization should reveal just how open or averse to AI adoption a company is. It might also expose discrepancies between management and lower-level employees or among various departments regarding AI readiness.

Employee buy-in is a major element of a successful AI implementation, and leadership buy-in is critical to employee buy-in. Management needs to assure employees that AI is meant to assist, rather than replace, them. Leaders must encourage experimentation and emphasize the importance of continual learning and improvement by making trainings and other educational resources available.

5. Set governance and ethical policies for AI use.

A recent survey from law practice Littler found that only 44% of organizations had policies concerning the use of AI. And Gartner expects that by 2027, even as companies become more adept at applying AI, inadequate ethical governance frameworks will lead to 60% of them failing to achieve the anticipated value from their efforts. Organizations must put data security, privacy and ethics policies into place before putting AI technology to work. These policies should include periodic audits of algorithms and outputs to avoid bias. Regular reviews of local, federal and international regulations should also be codified, to avoid falling afoul of updates to GDPR, CCP and other laws that could result in reputational damage or heavy fines.

6. Assess technical infrastructure, systems, martech stacks and data stores.

Is your existing technical infrastructure scalable enough to accommodate AI’s additional requirements? Does the architecture require a major overhaul to ensure a seamless integration of new AI tools? Do you have adequate network bandwidth for real-time processing? Is the AI technology on your shortlist compatible with your current martech stack?  And since AI is nothing without data, do you have acceptable quantities of readily accessible, clean, high-quality data? AI has not made the old “garbage in, garbage out” adage obsolete.

7. Clarify staffing needs and necessary skillsets to implement AI strategy.

Software engineers and developers will be critical to integrating AI tools into your current infrastructure. A large organization might need an AI architect as well. Data scientists are key to developing and honing models and algorithms; domain experts and business analysts are just as crucial in ensuring the AI solutions remain aligned with business objectives and the outcomes are actionable. And of course, employees in the departments that will be adopting AI need training, whether they’re marketers who will be modeling and segmenting programmatic ad audiences or HR professionals who will be using AI to help screen resumes and handle administrative tasks.

8. Embrace a crawl, walk, run approach to AI implementation.

Prioritize your AI efforts with the help of a proven strategic partner. A phased “crawl, walk, run” approach can ensure a solid foundation for long-term impact. Start with the quick wins – simple use-cases that can prove the practical value and relevance of AI solutions. Socialize the initial successes to build broad organizational support and enthusiasm for the work, and buy-in for future initiatives.

How Material Can Help

Unlocking value from AI demands more than a one-size-fits-all (or a one-tool-fits-all) approach. Auditing, assessing, prioritizing and planning must precede the actual AI implementation. Heaping these tasks on top of existing workflows can seem daunting; in fact, many might consider these tasks daunting in and of themselves. Material partners with organizations to assess their AI readiness and develop actionable strategic roadmaps for implementations that optimize workflows and yield meaningful ROI.

Our AI Accelerator, Material’s practical AI training and strategy program, can help your team or organization gain confidence and fluency in the technology, uncover meaningful use cases, set realistic expectations and develop effective roadmaps for productive, scalable AI solutions. Reach out to our AI experts today to learn more.