Successful AI adoption goes beyond determining the platform you’ll use. The true key is readiness, a term that encompasses your organization’s AI adoption strategy, roadmap, and change management initiatives.
Most businesses today are either exploring AI opportunities or using the technology in a limited form. Luckily, the early stage of AI adoption is a great place to be. You have the benefit of learning from the mistakes of others without making them yourself and starting your AI adoption journey with best practices in mind.
The costs associated with AI are simply too high to ‘dive in’ without a thorough, professionally developed strategy. With that in mind, here are a few fundamental principles to apply toward your AI adoption strategy:
Without a practical approach, AI adoption frequently fails
Virtually all organizations using AI achieve productivity benefits, but that doesn’t mean they’ll meet their targets. Especially if those targets aren’t realistic in the first place. On a macro level, trillions of dollars are going into AI and that’s leading expectations towards the unrealistic. The most important investment you can make in AI is a carefully considered approach that centers change management. Think of it like this: you wouldn’t put a person with a learner’s permit in the driver’s seat of a Ferrari. Your team can progress to the point they are comfortable with the AI equivalent of a supercar, but they’ll need plenty of time to get comfortable with the technology first.
To ground your AI approach in what is practical, ensure the following:
- AI efforts are aligned with your business strategy;
- You are not rushing to apply AI towards large scale enterprise-wide projects;
- Staff are engaged in the shift toward AI and receive training;
- Business leaders are aligned with your AI efforts; and
- KPIs and impacts are measured and communicated.
The pillars of AI readiness support successful implementation
The typical AI journey consists of the following phases:
Experimentation: In which users employ trial-and-error to find where AI suits their workflows.
Scale: In which use-cases are identified.
Impact optimization: In which new, enhanced workflows are fully integrated and optimized.
Strategy will inform your practical AI adoption roadmap
AI Strategy and Change: Moving from AI basics, like generating imagery or text, to more impactful work requires a vision that’s compatible with your organization. Link your organizational and AI KPIs to avoid getting lost on your AI adoption journey.
AI Risk & Governance: Know the risks associated with using AI. Ask yourself, where is the information processed by AI going? What might the cost of an AI hallucination be? It’s particularly important to understand how confidential information entered into AI could be converted into training data and potentially retrieved by another user.
AI Data Optimization: The age old adage, “garbage in, garbage out” applies in the context of AI. AI is a probability tool that relies on the data flowing into it. If the AI is using data that is inaccurate, erroneous, or outdated, the AI can not generate a reliable output.
AI Platforms and Partners: Find the platform that’s right for your sector and business needs. Already have a robust ecosystem with a leading technology company? The AI offering likely carries compatibility benefits. CBIZ has developed a platform, Vertical Vector AI, that emphasizes security because we think that the single most important feature of an AI platform is its ability to protect confidential information.
Identifying and validating tangible value and feasible use cases
Start using AI in a focused, structured manner. Employ a 30, 60, 90-day project cycle so you can track, measure, and complete AI implementation efforts without overextending. A lot of AI adoption over the past year has been driven by FOMO – fear of missing out. As cautionary tales become more common, we’re likely to see more concern over FOMU: fear of messing up.
Organizational alignment and ownership is a powerful driver of adoption
Identify those responsible for AI and adoption. One big risk is that AI can get siloed and neglected without leaders pushing for the workforce to take advantage of its potential.
Ground your timing and expectations with change management
Don’t suffer from analysis paralysis. Start quickly to get comfortable with the AI integration process, but do so under tightly controlled conditions. Don’t bite off more than you can chew. Starting small but moving fast enables you to get a handle on AI tools and scale up implementation as your organization’s confidence grows. The 30-to-90 day window of change mentioned above can help you track your efforts from thesis through implementation. It’ll establish a fast but not unreasonable pace for projects of a manageable size. Finally, recognize that you have to build trust and confidence internally by demonstrating the reliability of your AI and make that a priority.
Conclusion
AI readiness is a top-down effort to apply AI towards the right use-cases, with sufficient controls in place, and a change management strategy that encourages rapid adoption. Unfortunately, it’s one aspect of AI that’s essential to success but doesn’t come a subscription to your platform of choice.
Contact a CBIZ professional to ensure AI readiness.
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