In the modern world, data is often referred to as the “new oil.” This analogy is apt because, like oil, data is a valuable resource that can be refined and used to power various processes and technologies. One of the most promising technologies that rely on data is artificial intelligence (AI). However, for AI to be successful, it needs to access high-quality, relevant data. Knowing this, it is important for business leaders to explore why business data is critical for AI success.

Analysts in the technology sector are optimistic about the potential of generative AI (GenAI) and its impact on the future of businesses. According to some studies, global spending on AI increased by 26.9% in 2023, and Salesforce reported that AI adoption among customer service professionals had risen by 88% between 2020 and 2022.

Gartner predicts that in 2024, 40% of enterprise applications will incorporate conversational AI — a significant increase from less than 5% in 2020. Additionally, by 2025, 30% of large companies are expected to have implemented an AI-backed development and testing plan, increasing from 5% in 2021. Ignoring AI’s potential could leave businesses in the dark as the transformative effects of generative AI are already becoming apparent in various industries.

The rapid adoption of AI in business is largely driven by the promise of increased productivity. For instance, in marketing, generative AI is expected to yield significant productivity gains. Marketers estimate that generative AI can save them the equivalent of more than a month per year, allowing for more meaningful work. Forrester predicts that AI will usher in an era of creativity, with enterprise AI initiatives projected to boost productivity and creative problem-solving activities by 50%. Already, current AI projects have reported productivity improvements of up to 40% in software development tasks.

How to make AI work for businesses

Successfully getting AI up and running can be a challenge. It requires strategic planning, ample resources, and a steadfast dedication to innovation. Keeping this in mind, the following are a few points to consider when embarking on your artificial intelligence journey.

Training AI models

One of the primary uses of AI is to develop models that can perform tasks without explicit programming. These models are trained using vast amounts of data, which allows them to learn patterns and make predictions or decisions. The quality and quantity of data used to train these models directly impacts their performance.

For example, when developing an AI model to predict customer preferences, it needs access to historical customer data. The more comprehensive and accurate this data is, the better the model will perform. Conversely, if the data is incomplete or inaccurate, your AI model may produce unreliable results.

Improving AI performance for business

Even after an AI model is trained, it can continue to learn and improve its performance over time. This process, known as “reinforcement learning,” relies on real-time data to refine the model’s predictions or decisions.

For instance, when using AI to optimize the supply chain, the model can analyze incoming data, such as weather forecasts or customer demand, to make more accurate predictions about inventory levels or shipping schedules. As your model receives more data, it can continually update its understanding of the environment and make better decisions.

Adding personalization and customization

AI is increasingly being used to provide personalized experiences to customers. For example, AI-powered recommendation systems can analyze a user’s past behavior and preferences to suggest products or content they might be interested in. To do this effectively, the AI model needs access to individual user data.

Similarly, businesses can use AI to customize their products or services based on customer preferences. For instance, an e-commerce platform might use AI to tailor product recommendations or pricing based on a customer’s browsing history or purchase behavior.

Identifying opportunities and risks associated with AI for business

Data is also crucial for AI to identify potential opportunities or risks for a business. For instance, AI can analyze market trends, customer feedback, or competitor activities to identify emerging trends or threats. This information can help businesses make informed decisions about product development, marketing strategies, or risk management.

Challenges businesses may face with AI

Despite companies recognizing the significance and potential impact of AI, they frequently encounter difficulties transitioning from pilot projects to full-fledged production. Some of the primary challenges organizations must confront to scale their AI initiatives include cost constraints, a shortage of skilled personnel, insufficient tools and technologies for machine learning operations, inadequate volume and quality of data, and concerns regarding trust and governance.

AI technology has brought a host of ethical and regulatory concerns to the forefront, particularly surrounding data privacy, bias, and transparency. Failure to comply with regulations can result in legal repercussions and reputational harm. Moreover, ethical considerations may erode customer trust.

Steps to avoid issues that arise with the use of AI

Compliance and regulation: Stay abreast of evolving regulations and ensure strict compliance. Regularly review and update policies and practices to align with changing legal requirements.

1. Data privacy: Implement stringent data protection protocols. This includes encryption, access controls, and regular audits to ensure compliance with privacy regulations like the General Data Protection Regulation (GDPR) or California Consumer Privacy Act (CCPA).
2. Bias mitigation: Regularly audit algorithms and AI systems for biases. Implement measures to mitigate biases, such as diverse training datasets and algorithmic fairness techniques.
3. Transparency: Be transparent about the use of AI and data. Provide clear explanations of how AI systems work and the data they use. This fosters trust and helps users understand the technology.
4. Ethical guidelines: Establish clear ethical guidelines for AI development and deployment. This includes principles for fairness, accountability, transparency, and human oversight.
5. Customer trust: Communicate openly with customers about data usage and privacy measures. Provide options for users to control their data and opt-out of certain uses.
6. Training and awareness: Provide ongoing training and awareness programs for employees to understand the ethical implications of AI. This includes educating staff on the responsible use of data and AI technologies.
7. Continuous improvement: Regularly review and update AI systems and policies to ensure they remain aligned with ethical and regulatory standards.

By proactively addressing these issues, businesses can minimize risks and build trust with customers, regulators, and other stakeholders.

AI support for businesses provided by Citrin Cooperman

Citrin Cooperman’s Digital Services Practice is well-versed in AI and can help implement the technology to increase success. To learn more about how we can help automate some of your business’s mundane functions and enable your employees to focus on more meaningful work, reach out to your Citrin Cooperman advisor or Derek Nachimow at sales@citrincooperman.com.