Why CEOs Should Embrace Purpose-Built AI

Generative artificial intelligence (gen AI) continues to be one of the most pressing technologies for consideration by executives. As much as 85% of companies say they will be investing in it within the next two years, according to ISG research, marking it a top agenda item for CEOs across the globe.

Despite the high interest, there can be many barriers to AI adoption, from high costs to security concerns and innovation-averse organizational culture. As a result, ISG further reported that leaders are asking providers to explore possible use cases and strategies within existing services to generate real business value.

Focusing the use of AI to augment results of existing use cases while also facilitating new, more complex use cases, both in a purposeful way, can encourage buy-in and ensure that AI will work towards solving real-world business challenges, while also mitigating concerns of cost efficiency, accuracy, and ethics.

CEOs should consider adopting purpose-built AI to drive value for their employees and customers by increasing the value of their business data and the transparency of how they use it.

Increase transparency 

Pressures from regulatory bodies and general skepticism towards biased outcomes similar to the Google Gemini controversy will raise expectations for transparent and explainable AI use in business. If your organization is one of the many that have woven or plan to weave AI prominently into its core messaging, do not expect this to be taken lightly by customers. They will have questions about how their data will be used and how AI is generating value for them, and reasonably so.

By using specialized AI models in contexts that leverage your organization’s data, business leaders can more effectively and accurately describe how clients’ data is put to work to ultimately return greater value.

Additionally, legislation in the EU is also expected to have sweeping impact on European companies throughout the next year. Innovation leaders should take proactive measures to stay compliant in anticipation of further global regulation mirroring that of the EU.

For example, the White House AI Bill of Rights in the United States describes guardrails for large-scale AI use, such as having explanations for the use of consumer data and preventing excessive storage of data without a clear purpose.

Using small language models, a component of purpose-built AI, reduces the risk of noncompliance with such regulation, as organizations are less likely to use more consumer data than necessary.

Get more value from enterprise data

For many companies, valuable data and actionable insights can be lost in the high volume of digital and paper documents crucial to core business processes. In fact, research from ABBYY revealed that 6 in 10 professionals lack easy access to document information ancillary to their responsibilities, causing delays in completing customer transactions.

Tasking a human employee to sift through terabytes of documents for information is a misallocation of resources that will adversely impact productivity, efficiency, and experience. However, using a large-scale generative AI model for this purpose is analogous to using a hydraulic press to drive a nail into a plank – wasteful and likely to yield limited success, as LLMs are vulnerable to inefficiencies and hallucinations from the massive stores of data they navigate.

Using small language models instead allows organizations to unlock and utilize valuable data with a high degree of accuracy and efficiency. Specialized AI tools, such as intelligent document processing (IDP), can be trained over time to process a specific type of document, even in cases of varying format, layout, size, or language.

This is especially valuable in document-heavy contexts like accounts payable and transportation and logistics. When trained, purpose-built document AI can offer high straight-through processing, quickly and accurately uncovering the most crucial insights from documents with minimal need for human oversight, thus streamlining processes and accelerating revenue cycles.

Use AI with a purpose

Failure to effectively implement artificial intelligence for intelligent automation is typically resultant of an unclear purpose or innovation strategy. Leaders cannot afford to incur technical debt like many did during 2020-2021 when robotic process automation was the newest tool on the market. It’s not enough for the tools themselves to specialize in certain areas – as a leader, you must have a deep understanding of the challenges that exist within your organization’s processes and workflows and where AI can be used to identify automation opportunities and augment employees to empower them to do their work better and provide customers greater experiences.

Once you’ve determined if, where, and how AI will generate value, look for experienced vendors offering specialized solutions to begin your digital transformation journey where it’ll count.

Written by Ulf Persson.
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