Over the course of the last few years, the use of AI has shifted from a novelty to a necessity. AI promises to be a powerful tool for businesses to improve operations and efficiency; however, with the sudden arrival of AI in the business world, too little consideration was given to the ethical and responsible implementation of this technology.
Just as we have seen AI’s capabilities to solve real-world business problems skyrocket, so have reports of “hallucinations”, biases, and other sometimes-hidden issues. Only now are industry leaders starting to acknowledge the need for more guardrails. This will ensure that AI is used responsibly and ethically by users and customers.
What Is Responsible AI?
Responsible AI is the result of creating and deploying AI in a way that allows people and organizations to control the safety and output of the technology, making it trustworthy and reliable. The three main pillars of responsible AI are data governance, cost sustainability, and trustworthiness.
What responsible AI means may differ slightly from organization to organization, but there are several key tenets to the concept. Microsoft notes that fairness and inclusiveness, reliability and safety, privacy and security, and transparency are of utmost importance. These concepts not only prioritize the protection of the organization using the AI model, but also the people who may be affected by its output, such as vendors, customers, and more.
While AI providers are actively addressing issues such as hallucinations and biases, it is up to the users of the technology to ensure that the AI is used in a way that is safe, reliable, and trustworthy. This is more than just selecting the right AI model; it is about the entire lifecycle of the AI solution. This includes the careful curation of data sent to the AI model, the tools made available to the models, and the rigorous and repeatable testing to validate outcomes.
Why Does Responsible AI Matter?
As we know, AI can be a powerful tool for success, but without reliability, companies may experience more harm than good in the long run. Protecting your data and creating cost-effective, sustainable AI-powered solutions is far more important than the fastest or cheapest implementation possible.
Implementing responsible AI may seem like a burden; however, it is well worth the extra work. Some of the benefits include increased privacy and security for both you and your vendors, building trust with stakeholders, and achieving regulatory compliance. Possible negative impacts include leaked data, hallucinations, or untrustworthy outcomes.
What Is the Importance of Data?
Because AI was built using data, it is inherently flawed. Based on the data fed to the engine, AI can be biased or even entirely inaccurate. According to researchers at MIT, flaws in AI are a result of “the nature of their training data” as well as other “inherent limitations.” When organizations fail to understand these limitations and create a robust guardrail and data validation system, hallucinations or other biases, even if relatively rare, can have a significant negative impact on the very business problem that AI is intended to solve.
Establishing a limit-first approach is key when allowing your AI solution to access your data. Giving AI access to your database is the quickest and easiest way to lose data security, increasing the risk of leaked data.
At ICG, we believe that data security is of utmost importance. Improperly curated prompts and context management can expose unnecessary data to the AI model, which may not only be a data security risk but can also lead to inaccurate or even harmful outcomes. In addition, inappropriately scoped tools can lead to delivering unnecessary, potentially sensitive data to the AI model. Once again, not only does this represent a data security risk, but it can also lead to inaccurate results.
What Does Responsible AI Look Like in Action?
Guardrails are key for AI-powered solutions because they enable data governance. Data governance prevents the AI from doing and saying things it shouldn’t, such as divulging information to the wrong people. Guardrails protect against bad requests that come in and prevent bad information from going out.
At ICG, we use customizable, rule-based guardrails to ensure that your AI-powered solutions put data accuracy and safety first, creating a strong foundation to build upon. This decreases the risk of inherent problems within the core of your technology, which could necessitate a complete overhaul of the solution in the future.
Some examples of guardrails are: hallucination detectors, a valid URL checker, an address validator, and more. As time goes on and technology continues to improve, guardrails should change to reflect this.
How to Promote Trustworthy AI Solutions
It is common for people to think that AI solutions deploy extremely quickly. Although the time to a minimum quality proof of concept may be fast, the time to a reliable and trustworthy solution takes significantly longer. There are many reasons for this, but A/B testing plays a significant role.
A/B testing is crucial when making changes to prompts as it allows us to determine whether changes will improve the output and continue to produce trustworthy results. Making data-driven decisions when changing prompts can save your organization money and help you understand the impact of the changes made.
Creating Solutions That Last
It can be easy to see an issue and “throw” AI at it without considering the costs that come alongside it. If an organization uses AI for everything, it is extremely difficult to manage costs. Creating a sustainable solution means using AI as a self-learning system, not a solution to every little problem.
At ICG, we create sustainable solutions through treating AI as a tool, not the solution itself. This practice uses AI in the most efficient and cost-effective way, making solutions more sustainable for organizations over time.
Adding AI to Your Back Office
ICG is always working towards new ways to implement AI within our solutions. Some ways that your organization can add AI to your back office are:
- Disputes/Deductions Processing: Use AI to identify disputes/deductions trends and automatically resolve or direct the issue to the correct authority. Where possible, automate the resolution if the item is within tolerance and frequency. AI can also suggest how to prevent similar disputes in the future.
- Vendor Onboarding: Automatic identification of vendor risks/red flags, as well as assessing initial and ongoing vendor health. Additionally, automate bank account, COI, and diversity validations.
- Anomaly identification: Automatically determine changes in vendor payments, approvers, invoicing trends, 3-way match discrepancies, GL allocation trends, etc.
- In-Situ Chat: An AI chatbot readily available to your vendors or internal team to ask questions about trends, invoices, payments, discrepancies, disputes, etc.
- Invoice Approvals: Automatically apply GL coding to NonPO invoices and route to the appropriate approver.
- Duplicate Checks: Using AI, items identified as potential duplicates can be compared to previous items to verify status. After this, AI can make recommendations, mark it as a duplicate, or automatically process the invoice.
- Audit: Use AI to confirm transactions by comparing backup to invoices, disputes, PCard/Expense Reports, new vendor setup packages, etc.
Learn More
Responsible AI-powered solutions are the best way to have a safe, reliable AI technology experience in your organization’s back office. ICG consistently promotes data-driven decision-making, as well as a continuous improvement mindset. We will always strive to create solutions that focus on impactful, long-term results for your organization, helping to achieve stronger vendor relationships and ROI-boosting outcomes, and more. As AI’s capabilities grow, so will ICG’s responsible, AI-forward technology. To learn more about ICG’s AI-powered solutions, contact us for a free demo.