Ethical AI for enterprise businesses
With the rapid improvements in artificial intelligence technologies, many organisations feel under pressure to innovate.
However, how do you adopt ethical AI for enterprise business? It needs investigation, research, strategy and planning to uncover where AI might not be used ethically and then ‘closing the loops’ to ensure it is used wisely.
We’ll take you through our top tips here.
Opportunities for AI in enterprise business
In the contemporary digital landscape, here at Stimulus, we see more and more enterprise businesses seeking to investigate AI solutions and integrate AI technologies into their existing software stack. It’s true that AI can enhance operational efficiency, reduce labour requirements and help you gather and use data to strengthen your activities. But there are also some serious ethical and legislative considerations before you get started.
You might be under the impression that you don’t need or aren’t even using AI technologies at work. And yet, many contemporary software solutions are already incorporating AI components within them.
AI in Business Intelligence and workflow management
New AI technologies are used in Business Intelligence (BI) tools like Microsoft Power BI, to perform data visualisation. Interactive dashboards are created to provide an interactive way to look at complex data sets and the impacts of various changes on possible outcomes. The data is used to assist planning and forecasting certain information that can be used to benefit every area of the businesses. AI can also be used to automate tasks that might be repetitive or prone to human error.
Automation Anywhere is an example of a tool that facilitates the automation of business processes across a variety of functions. AI elements can also be accessed in supply chain management tools and Human Resource Management tools
AI in Natural Language Processing (NLP)
Examples of NLP tools include IBM Watson, a cognitive computing platform that employs NLP to analyse textual data, extracting valuable insights that can inform strategic decisions. - Google Cloud Natural Language API is another tool that offers comprehensive NLP functionalities, including sentiment analysis and entity recognition, enabling you to derive meaning from what might previously have been considered unstructured data.
AI in Customer Relationship Management (CRM)
A CRM tool provides enterprise businesses with a sophisticated way to manage all the data related to customers. Salesforce Einstein is an additional feature of this popular CRM that provides predictive analytics and personalised customer insights, thereby optimising customer engagement strategies. Meanwhile, HubSpot uses AI-driven analytics for marketing automation, allowing enterprises to refine their customer interaction processes. Automated chat bots uses on websites also incorporate AI. Pimcore is an effective CRM that is also making innovations in the use of AI across product information and metadata management.
If you currently use AI in one of these applications, or you plan to in the future, it’s important to adopt a strategy for ensuring the ethical appropriate use of the tools. This can involve considering ethical risks and developing strategies to mitigate them. A solid practice or policy foundation helps you ensure suitable practices for the future.
How to use AI in an ethical and effective way
While there seem to be clear benefits and improvements enabled by AI, there are also ethical and practical factors to consider before you launch headfirst into these new technologies. It’s important to take the time to consider when and how you will use AI, how you will manage and information generated by AI, how you will let your customers know you are using these technologies. You should also have guidelines and protocol in place should something backfire.
Considerations, guidelines and policies for AI in your business
Some important considerations to be addressed in your practices, procedures and policies include:
Bias and fairness: AI systems can unintentionally perpetuate and even exacerbate existing biases present in historical datasets. This can lead to unfair treatment of individuals based on attributes such as race, gender, age or socioeconomic status. To mitigate these risks, consider conducting audits of training datasets to identify and address biases. You can also implement fairness-aware algorithms that actively reduce bias in decision-making. Involving diverse teams in the development process can ensure you give leverage to a variety of perspectives and experiences.
Transparency and explainability: Often regarded as “black boxes,” many AI models lack transparency in how decisions are made. This poses challenges, especially in sectors where accountability is critical, such as finance and healthcare. To mitigate these risks, use explainable AI (XAI) techniques that make the workings of AI models more interpretable to stakeholders. In evidence of this activity, you can also provide clear documentation and training for users regarding how AI-driven decisions are made and the factors that influence them.
Communication is key when it comes to transparency; fostering open dialogue about AI capabilities and limitations, will ensure both internal and external stakeholders, as well as your customers, understand the potential impacts.
Privacy and data protection: The acquisition and use of vast amounts of personal data heightens privacy concerns. Enterprises must navigate the complexities of data protection laws while leveraging data effectively. To mitigate these risks, implement data minimisation practices, collecting only the data necessary for specific purposes. Data anonymisation techniques will protect individual privacy while still deriving meaningful insights from data.
Accountability and responsibility: Developing a strategy, position statement or approach for situations where there are problems or errors resulting in the use of AI can put you in good stead should there ever been a tricky situation related to your use of AI. To be prepared for worst case scenarios, establish clear lines of accountability within the organisation and define roles for oversight of AI applications. Develop comprehensive policies that outline operational guidelines and ethical mandates for AI usage and ensure that there is an established process for addressing incidents involving AI decision-making. These might include models for handling complaints and appeals.
Job displacement and economic impact: AI-driven automation can result in job loss and fear of job security among employees, leading to broader economic implications. Adopting AI can cause stress or cultural problems around your organisation. One study has found that employee’s willingness to adopt AI has a direct impact on how successful the technologies will become.
To ensure you have a person-centric approach to AI, commit to workforce development initiatives that focus on reskilling and upskilling employees to adapt to new job demands created by AI. Engaging employees in conversations about the role of AI within the organisation can also foster a culture of innovation and collaboration
Cyber-security, attacks and breaches: AI systems can be vulnerable to manipulative tactics, such as adversarial attacks that seek to deceive the system into making incorrect predictions. Mitigating cyber-security risks can involve significant investment in technological measures to protect AI systems against potential threats and attacks. Regular and ongoing security assessment will help you identify and address vulnerabilities within AI infrastructures.
User consent and autonomy: Ensuring that users have control over their personal data and are informed about its use is a fundamental ethical concern. To respond effectively to these concerns, we recommend establishing a transparent data governance framework that outlines how user data is collected, stored, and processed. There are many ways you can use your websites and online platforms to ensure you are operating effectively in this space.
Many ecommence platforms help you to seek explicit informed consent from users before data collection, offering clear opt-in and opt-out options. Email subscriber tools have inbuilt permission and consent processes, and showcase your responsibility to provide users with easy-to-understand information regarding their rights and choices concerning data usage.
Regulatory compliance: Another thing about AI operations is that the rules, guidelines and recommendations are always changing. Staying on to top and understanding evolving AI regulations is an important activity that can help you mitigate risks associated with legal repercussions. To ensure you are operating within regulatory guidelines, consider appointing dedicated compliance officers or teams who can help you stay informed. Depending on your industry, you may also be able to connect with specialised regulatory bodies or industry associations to stay informed about best practices and compliance standards.
By working with your staff to be aware of and address these ethical considerations, enterprises can promote a culture of responsibility and accountability in AI deployment. Establishing robust frameworks for ethical AI usage will not only mitigate risks but also foster trust among stakeholders and enhance the overall societal value of AI technologies. Engaging in continuous dialogue and collaboration with diverse stakeholders is essential for navigating the evolving landscape of AI ethics within enterprises.
At Stimulus, we understand the pressures of changing technologies and the challenge or trying to stay on top of your data handling and storage requirements. As Pimcore Silver Partners, we can help you deploy a tool that will enable innovate and grow win the face of these changes.
Related questions
Does Pimcore use AI?
Pimcore is a comprehensive software tool that is used by thousands of businesses involved in online trading and ecommerce. Pimcore has components that can manage your Product Information Management (PIM), Customer Relationship Management (CRM) and Digital Asset Management (DAM) among other capabilities. Pimcore does harness AI, through an extension called Pimcore Copilot. Pimcore Copilot acts as an advanced digital assistant, delivering seamless task automation and AI driven workflow enhances. Copilot can be used to create text for your product listings and catalogues. It can be used to create updates and variants of images, or classifying data records within you databases.