Coherent Solutions, with decades of expertise in digital product engineering, brings a strategic perspective on applying AI to catalyze business growth, both from the standpoint of applying AI to your business directly or building AI-powered capabilities into your digital product or service offering to enable your customers to take advantage of it.
Regardless of whether your organization directly uses AI technologies or creates AI-powered solutions for third parties (like customers or partners), all the considerations detailed here are equally relevant and important.
Identifying Business Applications for AI
The first step in harnessing AI is identifying precise business use cases. This requires a keen understanding of the questions that AI can answer for your business. Here are several areas where AI can significantly impact:
Improving Customer Service and Customer Experience:
- Chatbots for 24/7 customer support.
- Personalized product recommendations on e-commerce sites.
Optimizing Existing Processes:
- Automating routine paperwork tasks.
- Streamlining supply chain management through predictive analytics.
Increasing Employee Productivity:
- Tools for managing and prioritizing emails and tasks.
- Enabling call center agents access to up-to-date information and provide quicker responses to customer inquiries.
- AI-driven analytics to optimize team performance.
Adding New Services or Products:
- AI-generated content for digital platforms.
- New data-driven products based on customer behavior analysis.
Content Production:
- Automated news and report generation.
- Creation of personalized marketing content.
Increasing Quality of Products and Services:
- Quality control systems using image recognition.
- Predictive maintenance for manufacturing equipment.
The Critical Role of Data in Business AI
The effectiveness of AI business applications depends on the quality of data. It’s not just the quantity of data that matters but its relevance, accuracy, and security. It’s crucial to understand that more data isn’t always better. Data relevance plays a vital role, as using outdated information can result in flawed decision-making.
Organizations should also be cautious with historical data, as the correct solution from the past might not be the best fit for the present. For example, an AI-powered demand forecasting model developed a few years ago may have been highly accurate at the time, but if it’s now being fed sales data from a period of significant market changes, the model’s predictions could be completely off-base. Similarly, an AI system applied for credit risk assessment might make poor decisions if it’s relying on credit histories from before the COVID-19 pandemic, which significantly impacted consumer spending and debt patterns.
Data enrichment is another important aspect of a complete data strategy. Considering internally and externally available information that complements organization data ensures comprehensive business insights that account for the evolving market landscape.
Choosing the Right AI Applications for Your Business
Selecting the appropriate AI tools and technologies is critical. Beyond the hype of generative AI and large language models (LLMs), classical machine learning methods continue to be effective for many business needs and offer robust optimization, prediction, and pattern recognition solutions. While recent breakthroughs in AI applications for business, such as the remarkable capabilities of LLMs, have captured public’s attention, traditional machine learning techniques remain highly valuable in many business applications. Algorithms like logistic regression, decision trees, and support vector machines excel at a number of tasks, such as fraud detection, predictive maintenance, and customer churn modeling. These classical methods often require less data and computational resources than the latest deep learning models, making them more practical and cost-effective for certain business use cases.
The decision between using static or dynamic AI models and the extent of human involvement in the AI loop can also significantly impact the outcome of AI initiatives. Organizations should carefully evaluate the tradeoffs between the flexibility and adaptability of dynamic models versus the stability and explainability of static models, balancing their specific business needs and the level of human oversight required.
By understanding the strengths and limitations of both classical and cutting-edge AI approaches, businesses can build a robust, complementary set of tools to tackle their most pressing challenges.
AI Business Integration: Organizational Change
For AI to be truly transformative, it must be integrated into both technological processes and organizational culture. To achieve the most significant impact, AI should not only automate tasks but also drive business actions and decisions. This requires a culture that embraces innovation, processes that are refined to leverage AI, and a workforce trained to utilize these new tools effectively. Addressing AI bias, ensuring the ethical use of AI, and promoting responsible AI practices are essential. This includes a focus on eliminating biases and hallucinations in AI models and fostering an environment that prioritizes ethical and responsible AI development.
Conclusion
At Coherent Solutions, we understand that the journey to effective AI integration is complex. It demands not only cutting-edge technology and data science expertise but also a deep commitment to being true partners with our clients. By focusing on the right AI applications for your business, prioritizing data integrity, selecting the appropriate AI models, and fostering an organizational culture attuned to innovation, businesses can unlock the full potential of AI to solve their most pressing challenges.