AI Check-In:
Three Things to Know About Data-Driven Strategies

Artificial intelligence (AI) has been developing at breakneck speeds for years, yielding no shortage of headlines around new advancements, use cases, regulations, and other variables to keep innovation leaders on their toes.

The AI buzz has historically taken many different forms, from fear of a HAL 9000-esque sentience or Skynet-scale robot takeover to enterprise hype cycles around robotic process automation (RPA) and the more contemporary generative AI.

As the dust slowly settles and enterprises continue inching towards AI maturity, business leaders are now incumbent to evaluate the recent trends and developments that will determine where and how AI can fit into their organization. While recent research suggests that many businesses are investing to avoid falling behind the competition, they’re increasingly placing trust in purpose-built AI like intelligent automation and small language models (SLMs).

The most recent headlines have lauded specialized AI data preparation systems like retrieval augmented generation (RAG) along with SLMs as the key to ensuring long-term value of AI investments – but what’s driving this momentum towards specialization? Are these applications of AI really intended to benefit businesses, or will they just squeeze more profit from the AI-hype cash cow?

Through a retrospective lens, we can determine where this AI momentum is coming from, where it’s headed, and what business leaders should do about it.

Generative AI is valuable, but good data is priceless

Generative AI has been adopted in some capacity in nearly every sector. While its versatility has made it an excellent launching point for enhancing processes in a wide variety of use cases, the shortcomings of generative AI and LLMs in the areas of accuracy and cost-effectiveness have drawn almost enough attention as the benefits.

Recently, businesses have achieved greater success by using high-quality business data to tame unwieldy LLMs and generative AI tools. Narrowing the scope of these tools’ outputs to a more specific context or knowledgebase drastically reduces the likelihood of hallucination, thus wasting less resources on inaccurate outputs and instilling a higher degree of trust and autonomy into AI initiatives.

Ergo, the exigence for data-driven approaches like Agentic RAG and SLMs.

The recent wild west of AI experimentation eventually led businesses to the realization that their data is far more valuable than lofty and arbitrary promises of AI integration, thus ushering in a gold rush towards actionable data-driven insights to guide AI investment.

 

 

Trending toward a data-driven future

If you’ve interacted with generative AI tools like ChatGPT, you’ve likely seen for yourself its amusing ability to pull misinformation seemingly out of thin air and posit it as fact. While it’s entertaining to see AI-generated internet search results so confidently recommend super glue as a pizza topping, the CFO that approved the purchase order for your new AI assistant probably isn’t laughing.

RAG addresses this concern by prescribing AI platforms a predetermined set of data to retrieve its answers from, akin to a word bank in a word search or an answer sheet to an exam. This enables the best of both worlds, combining the accuracy of retrieval-based methods and the versatility and user-friendliness of generation.

While this data-driven approach marks a major improvement on its own, RAG has already progressed beyond relying solely on a single set of data, giving way to a more flexible system called agentic RAG.

Agentic RAG utilizes intelligent agents that cross-reference multiple sources and engage in multi-step reasoning to verify precise and relevant outputs. This enables better handling of more advanced queries with an even higher degree of reliability.

The rapid development and prioritization of this strategy reflects the overall trend of enterprise AI use towards being increasingly data-based.

 

Keeping AI simple and built for purpose

Accuracy and cost-effectiveness aren’t the sole reasons for businesses to scale down their AI tools – they might not have a choice if they want to maintain compliance.

Global AI regulation continues to develop, casting a looming shadow over businesses that lack transparency in their AI strategy. As a result, momentum is increasing for SLMs and other purpose-built tools that are more explainable and conducive to compliance.

Process intelligence enables greater awareness of both how enterprise data is being used, what processes could benefit from AI-powered automation, and compliance in ever-evolving mandates and regulations. Particularly as more organizations are relying on consulting services and autonomous auditing procedures to gauge ethical use of AI, insights through process intelligence will be instrumental in helping businesses keep pace with the curve of innovation without sacrificing compliance or responsibility.

 

 

The pursuit of a more optimal customer experience has been cited by global business leaders as a primary driver of AI adoption. As customer expectations continue to emphasize simplicity, speed and accuracy, technologies like intelligent document processing (IDP) become more instrumental in helping organizations meet this standard.

A poster child of the purpose-built approach to AI, pretrained AI skills autonomously extract, process, understand and classify key data in specific documents to prevent excessive storage of consumer data, limit the potential for harmful inaccuracies, and ensure that enterprise data is being used to its full potential.

 

How ABBYY steers AI advancement

The forces driving AI are rooted in the pursuit of long-term sustainable operation, encompassing cost, compliance, and effectiveness. As inflated promises of AI integration in enterprise tech continue to muddy the water of AI’s role in business, it’s important for innovation leaders to reflect on the lessons they’ve learned and the needs of their organization going forward when deciding how to leverage the technology.

For the sake of AI’s continued use in business and overall acceptance into society, leaders should continue to pave the data-driven path towards transparency and responsibility.

ABBYY strives to contribute to this future of AI through initiatives like Intelligent Automation Month, which educates business leaders on the benefits of AI-powered automation in a series of webinars every September. September 2024 is the second annual Intelligent Automation Month, featuring expert-led sessions exploring timely topics like responsibly navigating AI regulation, guiding automation initiatives with data-driven insights, and embracing the incoming wave of e-invoicing.

You can register and find more information for Intelligent Automation Month sessions here: https://www.abbyy.com/intelligent-automation-month/