AI is transforming the way people do business. With AI’s increased affordability and accessibility, the rise of AI makes it easier to start or run a business like never before.
For many businesses, a widespread use of AI is to streamline business processes and automate repetitive tasks to improve efficiency, and this use has only grown across enterprises. According to McKinsey’s 2025 state of AI survey, 78% of respondents said their organizations use AI in at least one business function, up from 72% in early 2024 and 55% a year earlier. Additionally, IT, marketing and sales functions were the business units using AI the most.
But just implementing AI is not enough.
Businesses need to focus on safe, secure and trustworthy uses of AI. In the future, this will be characterized by three main capacities:
Grounding: AI must understand the concepts it reasons over and operates with.
Grounding is crucial for improving the accuracy and reliability of AI outputs. Without it, AI models are prone to “hallucinations,” where they generate information that sounds plausible but is factually incorrect. To ensure this does not occur, advanced testing and evaluation need to be implemented along with verification and validation techniques to ensure that the AI they implement is not only “doing the thing right” but also “doing the right thing.”
Instructible: AI should not only learn from data but also adjust its behavior based on direct feedback.
This means that even non-experts should be able to tell the AI what to do or correct it, and the AI should respond by improving its performance over time. To ensure AI’s inscrutability, further research and development are needed to enhance AI’s reasoning ability.
Alignment: AI use must be judged by how well it aligns with business expectations and objectives.
Assessing alignment includes considerations like fairness, privacy, transparency and accountability. Business owners can do this by clearly telling their AI systems their business objectives. Then, businesses need to continuously evaluate AI systems in diverse scenarios to identify and mitigate potential risks and biases that are not in line with the business objectives.
Neuro-symbolic AI offers another way to implement safe and secure AI. By combining the pattern recognition strengths of neural networks with the logic and reasoning capabilities of symbolic AI, neuro-symbolic AI has the potential to make more transparent, explainable decisions — an integral step in improving humans’ trust in AI. For small businesses, this could mean automating the review of partnership contracts for compliance and clarity or leveraging real-time data analysis to optimize manufacturing workflows and reduce inefficiencies. For larger enterprises, neuro-symbolic AI can speed the transition from creation to reality for new products and minimize explicit and implicit biases in AI systems.
To truly unlock AI’s potential, businesses must prioritize trust, transparency and alignment with long-term goals. This means adopting AI tools that are explainable, responsive to feedback and capable of evolving alongside the organization. Businesses should feel empowered to incorporate AI into various processes across the organization, including those that go beyond the common ways of integrating AI. With thoughtful implementation, primarily through innovations like neuro-symbolic AI, businesses of all sizes can build more innovative, safer and more agile systems that support sustainable success.
Opinions expressed by SmartBrief contributors are their own.
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