The ability to harness the potential of AI initiatives starts with a robust data infrastructure and a cohesive analytics strategy that tailors AI technologies to your unique data and business contexts.
The hype around AI is leveling out. Expectations are aligning more closely with reality, and the pressure is on for AI initiatives to deliver real results.
Although 82% of companies have invested in AI, half are unclear about its business impact or unsure how to best implement the technology. Companies want to get more out of their AI initiatives, but they just don’t know how to get there, and many are feeling the pressure to turn things around quickly.
The problem is that in rushing to integrate and scale AI tools, companies overlook the foundation required for effective implementation. Ironically, the pressure to adopt AI tools as quickly as possible is actually slowing companies down from achieving results.
How AI differentiation makes all the difference
What sets successful AI investments apart from those that don’t quite hit the mark? Often, it comes down to differentiation.
While generative AI and large language models (LLMs) offer vast knowledge and easy integration, they don’t always grasp the unique needs and context of a specific business. It’s difficult for a company to stand out from the competition when they are relying on the same information and insights.
Take an online retailer, for example. By using AI to predict customer behaviors across different regions, they can better segment their audiences, fine-tune marketing efforts, and improve product recommendations. However, generic LLMs may not have the specificity to understand local shopping trends or cultural preferences.
Without specific, contextualized data, AI tools tend to offer only broad, generalized insights and limited value. Instead, companies can hone AI tools that are more accurate, have tailored insights, and applications that are finely tuned to meet a company’s precise goals. How? By integrating unique company data and training specialized models deeply versed in specific fields.
My own company is currently navigating this shift. We had the technical infrastructure and expertise to help our customers integrate AI tools that are specifically designed to fit their own business requirements. But we quickly recognized the need for more specialized and contextualized data to fuel marketing analytics and customer insights, leading to our recent acquisition of the digital analytics and data consulting firm Softcrylic.
We’re far from alone in this endeavor. Across the market, we’re seeing companies shift from generic, large-scale models to more specialized, medium-sized language models (MLMs) or smaller language models (SLMs) that are tailored for specialized purposes and powered by industry-specific data.
As companies continue to integrate AI technologies, they’ll need to identify similar ways to differentiate models and tailor tools to meet their own business needs and challenges.
See also: Vibing on AI Governance
5 essential elements for your AI models
How can you differentiate your AI models from the competition? Rather than relying on readily available AI models that prioritize immediate use cases and quick outcomes, focus on foundational elements of AI, such as data quality, trust, and AI literacy.
By building a stronger foundation for data applications, you can develop unique and effective AI models that will set your company apart from competitors. In particular, make sure you support and strengthen the following five areas.
1) Data quality and control
Data is the foundation of every AI model. Without high-quality data, you are building on a faulty foundation that’s sure to crumble as your applications grow more complex and mission-critical. No matter where you are in your AI journey, you need to continuously monitor the integrity of the data feeding your AI models.
It may be time to reevaluate your data governance and control measures to ensure that data is accurate, reliable, and contextualized — and that AI outcomes line up with your data inputs. Investing the necessary time, resources, and expertise in data quality helps ensure AI outcomes are trustworthy and mitigates potential bias.
2) Data and AI literacy
It’s not just technical experts who need to know the ins and outs of AI technologies. Everyone in your organization needs to have a baseline understanding of how AI models and data analytics function, but especially leaders and decision-makers.
A thorough understanding of AI mechanics enables your business leaders to make informed decisions and align AI initiatives with your business objectives. This knowledge ensures your leaders grasp how models generate their recommendations and the data driving these insights. To bolster data literacy among leaders, implement regular training sessions and workshops that demystify AI technologies and showcase real-world applications and outcomes.
3) Agility in Data Engineering
As you look to differentiate models with unique datasets, leverage commercially purchased or partnered data sources to enhance the richness and diversity of insights. To do this effectively, it’s crucial that you have systems in place that ensure data is quickly incorporated into your AI systems.
Data engineering gives you the ability to quickly ingest new data sources, structured and unstructured, both internal and external, for data exploration with AI. Doing so ensures your AI systems can keep up with changing market demands and customer needs, enhancing the effectiveness and responsiveness of your AI initiatives.
4) Production readiness
When developing any AI application, it’s important to start with production readiness in mind and choose use cases that integrate into existing workflows rather than expecting users to learn an entirely new process for AI. This approach ensures that the solutions you build are scalable and can perform under real-world pressures, reducing the gap between prototype performance and actual operational efficiency.
Early integration of monitoring and measurement tools — often referred to as AI governance or MLOps — allows you to continuously assess and improve the performance of AI applications, ensuring you can scale success over time.
5) Data privacy and security
Securing your AI systems requires a robust framework to ensure both data privacy and the integrity of AI models to prevent unauthorized access and potential breaches. Strict access controls define who can see and manipulate data, especially after it has been transformed by AI processes, which often obscures the data’s original form.
Regular audits and compliance checks should be enforced to ensure that data handling adheres to regional and industry-specific regulations. Additionally, encrypting data both in transit and at rest can protect against external threats, making it more difficult for bad actors to exploit. By embedding these security practices into your AI strategy, you can safeguard your systems against vulnerabilities and ensure your AI systems function as intended.
Taking the time to get your AI projects right
Your ability to harness the potential of AI initiatives starts with a robust data infrastructure and a cohesive analytics strategy that tailors AI technologies to your unique data and business contexts.
By focusing on these foundational details, you can differentiate your AI models to produce specific outcomes and more value for your entire organization. It can take a little more time, but it’s worth it in the long run.
The race to AI success isn’t about who starts the fastest; it’s about who can finish with the best results for their business. What steps are you taking to get there?
