We have been witnessing technological disruption almost every decade, be it the Internet burst, Cloud computing or the recent rise of AI. With each wave, the debate around benefits and harms continues, eventually resulting in widespread adoption. Consider internet and cloud technologies today that have penetrated our daily lives, and this will replicate with AI for sure.
Digital transformation leverages technologies to enhance experience for customers, employees, and other stakeholders, enabling better products and services while continually adapting to rapidly changing needs and expectations. Over the past 3-4 years, digital technology adoption has surged, fuelled by Covid-19. Advances in cloud computing, analytics, process automation, AR/VR, AI/ML, Chat GPT and computer vision have spurred organizations to rapidly adopt these technologies to enhance customer experiences. The use of AI in the supply chain eco system will increase our cyber exposure making t crucial to remain vigilant.
As AI becomes more deeply embedded in the supply chain ecosystem, cyber exposure will increase making it crucial for us to remain vigilant.
AI Use Cases
Most manufacturing companies are on the Digital transformation journey, exploring AI and ML to tackle their operational issues. Some of the use cases that come to mind are – –
- Optimal inventory managementÂ
- Demand forecasting
- Probability of winning / losing a bid and many more
- Predictive maintenance
- Employee productivity enhancement
- Customer behaviour analytics, and many more….
Challenges
Every organization may be at a different stage on the technology adoption and maturity curve, but most have a defined roadmap to follow. Certain challenges need to be considered –Â
- Data accuracy, that affects outputÂ
- Difficulty in detecting AI Model Manipulation with attackers injecting bias into AI training data, leading to cascading disruptions –Â
- Inherent AI Issues – bias, hallucinations are some of the known risks. To manage this, we need to understand the model and data on which AI has been trained.
- Cyber Risks -– A poorly trained or compromised AI model can wreak havoc which can cause major disruptions in manufacturing.Â
- Lack of Awareness – many users start with AI tools not being mindful of the potential risk.
- Privacy and IP issues – sensitive data may be exposed or misused without proper safeguards
Way forward
With AI and ML gaining popularity, their real-world applications are now gaining traction. AI as a very powerful tool can not only help businesses and cybersecurity teams, but also empower government agencies, doctors, sportsmen, students among others. To adopt AI responsibly for business, we need to ensure the following –
- Assess the AI tool, the underlying model, how it works and the data it has been trained on.
- Ensure data security and privacy
- Drive awareness of AI tools and responsible usage
- Ensure complete visibility across organization, monitoring all AI tool usage
- Include AI linked scenarios in the incident response plans and practice it during the cyber drill. Being a new field, initially we will have a lot of learning.
AI is here to stay, we must use this technology to our advantage to enhance productivity, augment forecasting, lower the cost of inventory, enhance uptime, improve customer experience, build cyber resilience (AI powered technologies) and benefit from informed decision making. We need to arm ourselves to fight AI with AI.