Artificial Intelligence

The state of artificial intelligence in 2024

8th December 2023
Paige West
2

This year, we saw innovation teams experimenting with a variety of automation tools powered by artificial intelligence (AI).

As enterprises navigate the potential for business value through large language models (LLMs) like generative AI, adoption of AI continues to grow increasingly widespread. According to recent research, the large majority (89%) of IT executives say that they have AI strategies in place, with 37% having a roadmap spanning three to five years. 

Maxime Vermeir, Senior Director of AI Strategy, ABBYY further explores.

Organisations were surrounded with AI hype in 2023 but have since had time to cut through the noise and determine the best business use cases for using it in their operations. This resulted in a realisation that despite their profound potential to generate value, the most powerful general-purpose AI tools can be unscalable, costly, and resource-consumptive, rendering them unsuitable for many enterprise automation goals. However, enterprises that don’t find a way to apply specialised AI solutions to business goals will find themselves falling behind their competitors.

In 2024, there is a need for purpose-built AI that will solve specific pain points effectively, efficiently, and in a scalable and resource-conscious way.

Key challenges and focuses for businesses in 2024 will be strategically integrating AI into organisations, measuring the success of AI implementation, and managing the ethical and legal risks of AI while staying ahead of the innovation curve.

AI strategies

In order to harness the power of AI, businesses need to anchor their AI strategies around clear, purpose-driven goals that align with business outcomes. These are three steps businesses should follow to establish effective AI strategies:

  1. Identify clear objectives:
    • What business objectives do you want to achieve with AI? Whether it's improving operational efficiency, enhancing customer experience, or driving innovation, it is crucial to clearly define your goals and the metrics by which you’ll measure success
  2. Choose specialised AI solutions:
    • The versatility of generalised AI can seem appealing, but opting for specialised, contextual AI solutions tailored to specific business challenges are more likely to deliver accurate and actionable insights with less cost and risk
  3. Invest in quality data:
    • Relevant, high-quality data is necessary for successful AI implementations. Ensure your data is clean, organised, and accurate to real-world scenarios your AI solutions will encounter

Measuring success of AI projects

From ABBYY’s perspective, the crux of measuring success of AI initiatives lies in the tangible impact they have on business processes, rather than just the technical metrics. Metrics like F-scores can provide useful insights into the performance of AI models, but they don’t necessarily translate to how effective they are in the real-world. Success metrics should always go back to how AI can enhance business operations.

The three main metrics we prioritise are those that reflect direct business value. These include:

  • Straight-Through Processing Rate (STPR): an increase in STPR means that more transactions or processes are being completed without manual intervention thanks to AI
  • Time saved: efficiency gains can be estimated by measuring the time saved by implementing AI solutions
  • Return on Investment (ROI): this captures the financial value from AI initiatives and demonstrates the cost-effectiveness and value add to the business. In 2023, an average of 57% respondents anticipated seeing at least twice the cost of investment ROI, while only 43% delivered this increase.

By focusing on these metrics, businesses can ensure their AI initiatives are delivering real value, driving process efficiency, and contributing to the bottom line. This approach can help businesses achieve meaningful enhancements in how they operate and deliver value.

Addressing the environmental impact of AI

Businesses will continue to grapple with the trade-off between generative AI capabilities and their ecological impact, such as immersive search capabilities that consume large amounts of energy. Using generative AI today to search and summarise data consumes 10 times the energy of a normal search, which is unsustainable in the global effort to reach an average planetary temperature of 1.5 degrees by 2025. There are alternative AI models that use robust machine learning and natural language processing with business rules for highly specified purposes; for example, in transportation and logistics, extracting data from the 44m bills of lading issued every year and processed by at least nine stakeholders at 12 touchpoints with a highly accurate AI-model, trained on thousands of bills of lading.

The growing influence of regulation

As AI technologies continue to permeate various sectors, regulatory bodies will likely ramp up scrutiny to ensure ethical use and data privacy. This will also include measures to ensure that claims made by AI vendors are accurate and verifiable. These frameworks and regulations will sensitise users to the potential risks that shadow the possibilities and will bring business users back to the reality of integration challenges.

With more demand for transparency among businesses and regulators in AI decision-making, advancements in Explainable AI (XAI) will gain momentum, as it helps to demystify complex AI models and foster trust among users and stakeholders.

Embracing a human approach to AI

C-suite leaders have already begun to discover the hidden costs and ecological impact of generative AI, lifting the veil of hype to reveal practical challenges of integrating AI applications into their organisation’s infrastructure. Still, artificial intelligence has proven itself as a transformative tool that will be instrumental in modernising businesses and driving operational excellence.

In order to overcome these challenges, business leaders need to embrace a more human understanding of their data and processes. This involves bridging the gaps in understanding between AI teams and the business side of the organisations they serve. By fostering collaboration between AI specialists and professionals with actionable, hands-on business knowledge, enterprises can ensure that AI is driving operational excellence in the right areas and yielding truly actionable insight. Businesses need to carry this approach through impact assessments, strategising, implementation, and measuring success.

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