Arjun Pillai, Cofounder & CEO, Docket.
Although AI adoption is clearly growing, scaling it across an entire enterprise comes with unique challenges. For example, while 92% of companies plan to invest in AI, only 1% have reached maturity, showing the extreme disparity between the desire to maximize the technology and the ability to truly do so.
The same report shows that half of business leaders believe AI development and release are too slow in their organizations, while employees long for more support and training on GenAI than what they’re currently getting. Companies must overcome these hurdles to scale AI across the enterprise and consistently deliver value. Here are four steps to help you get there.
1. Develop a strong strategy backing GenAI adoption.
Many leaders purchase a solution because a competitor or peer recently implemented it, and it seems promising for their own business. Or, the solution has become so widely popular that the leader feels their company will be left behind without it.
These are not the best reasons to make a purchase decision. You should implement technology at your company to solve your team’s unique problems. Additionally, it should have the potential to yield an outsized impact on the outcomes most important to you. Start from the problem and work toward the technology that solves it. By defining the exact problem you want to solve with GenAI products, you’ll help your entire team buy into the implementation’s purpose.
It’s important to resist the temptation to adopt multiple fragmented products. Instead, invest in a product that will solve a critical enterprise problem. Focusing on a single, cohesive solution can drive more meaningful results and ensure better integration across your organization’s operations.
Key Takeaway:
To build a strong strategy, start with your company’s acute problems and only consider solutions that directly solve them.
2. Integrate data for seamless AI deployment.
Once you’ve identified an AI solution that promises to solve a pressing business problem, make a plan for deployment. Start by creating systems that enable secure data integration across critical business functions. This allows you to have strong use cases across the organization, which in turn ensures the acceleration of AI adoption to propel business growth.
The infrastructure you design should support real-time data streaming and integration in order to adequately support your technology’s responsiveness and decision making. The goal is to integrate data from all your various sources (e.g., your CRM, MAP, ERP, etc.) into a single, unified layer. This way, your AI models have a single, holistic view of your data.
Once this is done, create a feedback loop so your team can monitor and refine the system on an ongoing basis. This is essential for gaining long-term value from the solution.
Key Takeaway:
Integration between all of your data sources is critical for effective AI adoption and results.
3. Focus on your training data and pilot period.
Pay close attention to the datasets used to train your AI to minimize bias and improve model performance. Your models should source their data from reliable datasets, which provide diverse representations of the information so the results you get are unbiased and trustworthy.
You can also configure the AI to leverage its innate ability to learn continuously and self-correct over time for authority and dependability, ensuring that the system adapts to new trends, data and changing business needs.
As you set up your technology, aim for transparency across the board. Users should have clear visibility into confidence scores, citations and references, with minimal opportunity for hallucination. In addition, having a pilot period for the technology is a valuable way to introduce it to your team and pave the way for smoother adoption. During this time, create processes for ongoing data collection and model updates and train the team to get the most from the solution.
Key Takeaway:
Start with quality data to ensure optimal AI training, and consider launching a pilot period for new tech to gain your team’s support and increase their comfort.
4. Select data infrastructure that supports AI systems.
If you begin with the ultimate goal of scaling in mind, you’ll start with the right infrastructure to get you there. This guarantees your systems can scale efficiently without compromising performance as your business grows and data volume increases.
Put automated data pipelines in place to streamline the data flow from collection to processing and model training. Going this route allows you to create more efficient data processes, which leads to faster iterations and updates.
You can catch issues early by proactively monitoring and analyzing model performance in real time, giving yourself time to make necessary adjustments and ensure the AI continues to meet your business’ needs as it scales.
Key Takeaway:
Choose scalable, high-performance infrastructure that can handle large datasets and heavy AI workloads.
In Summary
There are a variety of unique challenges leaders face when scaling AI across an enterprise organization. But in order to be one of the few companies that not only implement the technology but also reach maturity, you must be strategic about how you select, integrate, train and support your AI. Success will come from thoughtfully navigating these challenges, following these four steps and committing to long-term, intentional execution.
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