Four Core Considerations to Harness the Power of AI Within Enterprises
According to the IBM Global AI Adoption Index 2022, the global AI adoption rate is 35%—a four-point increase from the previous year. Furthermore, 44% of organizations are actively working to embed AI into their current applications and processes.
These figures illustrate that businesses are not merely interested in AI as a novel concept; they are committed to harnessing its capabilities to stay competitive and relevant in an increasingly data-driven world.
However, Can Artificial General Intelligence (AGI) Systems truly meet businesses' complex needs?
The short answer to the question is NO.
Before deeming an AI model enterprise-ready, addressing challenges like bias, data privacy, and regulatory compliance is vital. This piece highlights key considerations, empowering businesses to leverage AI for digital success.
First, let's differentiate between Artificial General Intelligence (AGI) and Enterprise General Intelligence (EGI) to grasp why AGI isn't fit for enterprises.
Artificial General Intelligence vs. Enterprise General Intelligence
AGI emulates broad human intelligence. But when tailored for business, it becomes Enterprise General Intelligence (EGI), boasting domain-specific expertise for specialized organizational needs.
Think of EGI as a chef trained solely for a specific restaurant, mastering dishes for its unique clientele. Put this chef in a different cuisine, and he may falter.
Within its domain, an EGI might possess advantageous biases that align closely with enterprise goals. These biases act as informed heuristics based on historical data and can help enhance efficiency. For instance, if the EGI is built for a luxury car brand, it might prioritize customers looking for premium features over cost-effectiveness.
In contrast, generic AI systems like ChatGPT are like Swiss Army knives—versatile, ready for various tasks but not specialized in any one area.
Another key distinction between the two is output evaluation. AGI lacks a clear reward approximation due to its broad range of tasks, making it challenging to quantify its success uniformly.
In contrast, EGI has defined metrics for success, allowing for precise output assessment and subsequent improvements.
4 Key Considerations for Enterprise-Ready AI
Here's the thing about generic AI - while it might suffice for a general user, when it comes to enterprises laser-focused on delivering value, even tiny errors can lead to significant fallout.
Especially in sectors like finance or healthcare, where stakes are sky-high, a misstep in AI-driven processes can have dire consequences.
These concerns are amplified by data privacy and security risks, as unauthorized handling of sensitive data can open the door to a world of security and privacy troubles.
To combat these challenges and ensure AI readiness for enterprises, it is essential to consider four key factors: explainability, auditability, controllability, and reliability.
A recent study, by the researchers at the University of Southern California, has revealed that more than 38.6% of the facts used by AI are biased, underscoring the need for Explainability in AI models.
Understanding how AI algorithms reach specific outcomes is essential to ensure the model's trustworthiness. This process of understanding, retracing, and comprehending how an AI-enabled system makes decisions is called explainability.
Enterprises failing to deliver accuracy and accountability can trigger intense public, media, and regulatory scrutiny. This could mean significant financial hits and reputational damage to the company if the model is found to be flawed, biased, or non-compliant with regulations.
Explainability fosters trust among users, ensures AI complies with laws and ethics and allows businesses to audit and correct system biases or errors.
Auditability involves thoroughly assessing an AI system to scrutinize the model's inner workings and decision-making processes.
An AI audit maps out every facet of the AI system's operation, highlighting potential pitfalls and improvement areas and rectifying those errors.
The ultimate goal of the practice is to ensure the reliability and accuracy of AI systems. It helps maintain the AI's performance and ensures that it operates in line with legal and ethical standards, noncompliance with which could lead to severe penalties.
Consider the audit of Facebook's ad systems by USC researchers, which found algorithms limiting job ads to women, violating US employment laws. Auditable AI models can prevent such biases and save enterprises from bearing losses later.
Controllability in AI refers to the ability to guide, adjust, or influence its actions, ensuring it consistently behaves as intended. In the worst-case scenario, it should relinquish complete control to humans.
The AI should also have mechanisms to learn from feedback, refining its actions over time. Users must be able to modify its functions, whether by tweaking parameters or adding training data.
Understanding the AI's decision-making process helps in controlling its actions. There should be mechanisms to intervene or halt AI actions if they go off track, akin to an emergency stop.
Reliability in AI refers to the ability of AI systems to produce accurate and trustworthy results over time without significant degradation in performance, ensuring resistance to noise, outliers, and adversarial inputs.
Consider a self-driving car's AI. It must navigate routes consistently, whether sunny or raining, and even when faced with unexpected obstacles or potential cyber threats. If it misinterprets traffic signals in a storm or is fooled by fake road signs, its reliability is at stake. A dependable AI performs well, no matter the conditions.
To bolster reliable AI, emphasis is increasing on data quality, transparency, oversight, and accountability. A significant move is the European Artificial Intelligence Act, which sets standards for AI to be robust, explainable, and fair.
Hold the Temptation
Tempted by the success of ChatGPT? The numbers tell a compelling story - 1.43 billion site visits just in August 2023. While the allure of AI is clear, businesses should look beyond just adapting generic AI.
Instead, they ought to explore the specific benefits of Enterprise General Intelligence (EGI) for their operations. Here are a few pointers for successful EGI adoption:
- Define precise business problems.
- Ensure clean, comprehensive data.
- Collaborate with AI specialists.
- Test in controlled settings.
- Maintain ethical decisions.
- Regularly update with fresh data.
- Encourage user feedback.
- Ensure scalability.
- Stay updated with AI advancements.
- Safeguard data at all stages.