Martin Owen is CEO of Quark Software and is an expert in data and content management technologies for digital transformation.
Artificial intelligence (AI) has morphed from a futuristic technology to a here-and-now business opportunity. It offers tremendous opportunities for organizations willing to invest, such as boosting productivity, reducing operational costs and gaining critical insights to deliver better customer service and drive better business outcomes.
There’s still plenty of fear, uncertainty and doubt, though, particularly as organizations struggle to identify how their people and operations infrastructure can best use and benefit from the emerging technology while mitigating against poor implementation, security risks or unethical use.
Many organizations are on different paths in their AI journey and determining how to best adopt and benefit from the technology. According to IBM’s Global AI Adoption Index 2023, 42% of IT professionals at large organizations report they have actively deployed AI, and an additional 40% are actively exploring using the technology. The same report reveals organizations are identifying specific use cases in which AI can be beneficial.
So, where should an enterprise organization begin its AI adoption journey? Let’s look at three ways organizations can get started on a “bring your own AI” (BYOAI) approach and how it gets you on the right path to making AI deployment a controllable reality and an incremental opportunity for your business.
Use your trusted internal data, not untrusted external data.
When building and training your AI models, it’s safer to use trusted internal data that is owned and managed by the organization itself. The data is more consistent, accurate and secure and best aligns with your organizational needs and objectives.
This can help your organization avoid the potential risk that comes with training an AI model that includes data managed by a third-party resource or the wider internet. Importantly, you can control how best to manage, protect and analyze the data, ensuring accuracy while giving your business an advantage in the market.
Consider this: For enterprises operating in life sciences and financial services, content regulation and compliance are critical. Determining whether to connect to your own AI service and large language models (LLMs) rather than to an external service is a crucial decision. You need to ensure your carefully crafted ontology and universal taxonomy within your content ecosystem isn’t compromised with metadata and speech that risks non-conformance of regulated content.
Just one mistake in the formatting or terminology can mean pharma labels or investor reports are rejected for publishing. This impacts your time to market and very well leads to a missed business opportunity.
Train on your own LLMs.
Training AI on private data models gives your business control in how you leverage AI, ensuring minimal people and operations impact and optimal innovation potential. Here are a couple of other key benefits:
• It allows your business to tailor AI to meet the specific unique needs of your organization and protect some of the salient IP and confidential back-office information in the process.
• It also improves your security and data-privacy posture because you are using your private data models and not relying on a third-party source that could be susceptible to risk.
When it comes to getting started with building and training LLMs, enterprise organizations often begin by defining clear objectives for AI use. When analyzing your content strategy, for example, it’s particularly important to do this as it ensures your business understands that AI is acting as an enabler to achieving measurable goals rather than being viewed simply as an emerging technology that must be used because the business has invested in it.
Once your business has conquered this, you can be successful at solving core but oftentimes complex content issues such as translation, collaborative authoring, summarization, publishing timelines and intelligent content reuse. Remember, with no clear context of the goals of AI to support targeted business use cases, your organization will lack the full benefits of it.
Generate results internally.
Generating AI results internally can lead to better quality and more valuable data, as the organization is using its own curated intelligence to train the AI models.
It can also offer flexibility as the models can adapt to support changing business or operational goals. Finally, internally generated results can also deliver more comprehensive and personalized insights into customer behavior patterns, compared to external data offerings which may generalize away from unique selling points and customized workflows.
Conclusion
Many large organizations have already started to invest in AI heavily. We’ll see a great multitude of organizations get on the fast track in exploring the potential of AI and see that the true value of AI lies in supporting specific use cases.
Only after identifying and prioritizing areas where AI can have the most significant impact—such as improving operations processes, supporting stronger team collaboration, optimizing processes or improving the customer experience—can organizations begin to see tangible outcomes and accurately measure AI ROI.
Before you can leverage AI effectively, identify your AI team, solidify an AI adoption strategy across the enterprise and consider a BYOAI approach, as it can provide a new level of customization and innovation that will fuel your competitive advantage.
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