The applications of artificial intelligence in businesses and other organizations have brought forth a new concept and practice called artificial intelligence strategy or AI strategy.
An AI strategy embodies a precise blueprint outlining how an organization can harness the power of artificial intelligence to complement or improve further its overall organizational strategy and meet its organizational goals and objectives.
However, despite its seeming advantage and specific beneficial applications, developing and implementing an AI strategy can still have disadvantages.
The Pros and Cons of Artificial Intelligence Strategy: Pointers and Implications for Organizations
Advantages of an AI Strategy: Applications and Specific Benefits
The main advantage of an AI strategy rests on the fact that it enables an organization to leverage artificial intelligence to advance further its organizational goals and objectives and become a source of competitive advantage. A specific AI system can help solve pressing problems affecting the operations of an organization or become a tool for driving growth.
Below are the applications and specific benefits:
1. Adaptable and Scalable
An AI strategy can be developed and implemented in suitable organizations regardless of their size due to the availability of various AI tools, products, and solutions. An organization can choose a single focus area and expand to other areas when needed. The strategy can be scaled up or scaled down to adapt to changing business needs. Accenture announced its AI strategy in 2017 and this same strategy has scaled up to take advantage of newer technologies and trends.
2. Builds New Capabilities
It can also be used to create new products, expand existing ones, or accelerate product development. Specific AI applications can be used to make an organization more innovative and differentiated. It can also be used to promote efficiencies across the entire operation or in specific processes. Nvidia has developed foundation AI models as part of its platform strategy and to launch its enterprise-level generative artificial intelligence services.
3. Increases Productivity
The capabilities of AI to automate tasks and make predictions from data can help increase productivity in an organization. Automation lessens the workload of humans while AI-powered data analytics lessen the time needed for data collection and interpretation. Citibank uses machine learning to provide personalized customer experiences and Bank of America uses an intelligent agent to detect fraudulent transactions and prevent billions of dollars in losses.
4. Cost Reduction Solution
Another advantage of an AI strategy is that it can serve as a framework for reducing costs. AI can optimize or automate operations and specific processes. Companies like Google and Amazon use machine learning to make their data centers more energy efficient and reduce costs from energy consumption. Wells Fargo uses chatbots to automate customer service tasks and reduce the number of human customer service representatives by 20 percent.
5. Supports Decision-Making
The strategy can also be built around the knowledge requirement of an organization. Advances in natural language processing and large language models have brought forth various generative AI applications that can interact with humans using natural languages while deep learning has improved the predictive capabilities of a particular AI model. The AI-powered inventory management system of Walmart helps in the re-ordering of products and logistics.
Disadvantages of an AI Strategy: Issues and Notable Limitations
There are some challenges in developing and implementing an AI strategy. These include the resource requirements and exposure to potential problems or rebounds that come from misusing and abusing technology. It is also important to underscore the fact that using artificial intelligence does not guarantee organizational success.
Below are the issues and notable limitations:
1. Specific Cost Requirements
A notable disadvantage of an AI strategy centers on the needed investment for its implementation. The relevant costs include the required hardware and software for building an AI infrastructure and the training and expertise needed to run and maintain this same infrastructure. A small organization might not be able to afford more expensive AI solutions like advanced automation, big data analytics, and generative artificial intelligence services
2. Implementation Complexity
Remember that implementing the strategy requires specific organizational capabilities that can come from hiring or contracting individuals or a firm with expertise in deploying AI solutions. The strategy needs to be developed in a manner that it aligns with the overall organizational strategy and its goals and objectives. Its value-producing benefits should outweigh its cost. Determining this requires consistent monitoring and regular evaluation.
3. Technological Dependence
Overdependence on artificial intelligence exposes an organization to specific risks that can arise when a particular AI system fails or one component of its AI infrastructure malfunctions. This can disrupt operations and result in opportunity costs. It is important for an AI strategy to determine the scope and limitations of artificial intelligence applications while also providing specific guidelines for dealing with technical glitches and system failures.
4. Workforce Displacement
Implementing an AI strategy has the potential to replace certain occupations or specific roles and tasks. This would not sit well with affected employees and might also bring forth additional costs for the organization due to severance requirements. An organization needs to thoroughly examine and consider the impact of its AI strategy on its workforce and ensure that its implementation would remain fair and reasonable to involved stakeholders.
5. Notable Shortcoming
An organization should not depend on its artificial intelligence capabilities alone. An AI system can suffer from bias depending on how its algorithm or model was developed and trained. A biased system produces inaccurate results. The absence of human touch in certain automated processes or analytical functions can produce erroneous conclusions or ambiguities due to limited contextual understanding. Using AI also raises data privacy and security concerns.
