Artificial Intelligence (AI) has successfully moved from conceptual initiatives in the boardroom to implementations in actual customer-facing and employee-performance solutions. Today, services ranging from eCommerce to movie streaming are undergoing a transformative change in their customer experience landscape using AI. Worker productivity and employee experience are also being augmented by leveraging AI. Gartner estimates that worldwide spending on AI will surpass USD 297.9 billion by 2027.
As more digital initiatives by businesses are becoming AI-enabled, decision-makers need to be aware of the essential technologies and strategies that will ensure success when executing AI initiatives. Experts have often called out the high failure rates that have touched as high as 80% in AI projects across industries globally. No business will want such disruptions or failures in their core operations or customer-facing digital experiences.
To ensure the successful adoption and growth of AI models, enterprises must begin their work way ahead of the implementation stage. They need to plow the field well before sowing the seeds. AI success involves the use of technology, tools, and processes that will ensure that their digital ecosystem is equipped to handle the power that will be unleashed by artificial intelligence. That’s why, leaders must be aware of the major areas in their ecosystem where they need some essential elements to guide the rightful implementation and growth of AI models.
The key ingredients to power AI success
Let us have a closer look at some of the major elements that could decide the success of AI initiatives in businesses today:
Access to good-quality data
In the world of AI, your intelligence model is only as good as the data you use to train it. This is a fundamental principle that applies to any AI project. AI systems are built or modeled based on behaviors exhibited by automated and other processes in response to a wide range of data. It improves accuracy, speed, and performance by learning through as much accurate training data as possible. What enterprises must ensure in this scenario is the availability of not just truckloads of training data but also that this data is of the highest quality.
Take the case of generative AI. It is estimated that by 2025, nearly 30% of all GenAi projects globally will be abandoned because of poor data quality. Organizations must bring in more relevance to the data they use to train AI models. To achieve this, they might need to widen the scope of data collection for AI training. The inferences that AI models arrive will be more accurate and beneficial when enterprises can accommodate data from not just their internal channels. They must collate data from internal channels, 3rd party sources, customers, competitors, market sentiments, and much more. This will ensure that the resulting AI model will have more contexts to match the outcomes derived from a scenario. Ultimately this will provide more reliable insights and tighter actions.
Reliable governance model
As AI penetration increases, consumers and government bodies often express concerns regarding the use of personal data, the way AI models interpret outcomes from training data, and how businesses use AI to control user engagements. To prevent any misuse and maintain strict compliance with privacy and ethical considerations, enterprises must work on an AI governance model. This model will set the standards, roadmaps, and guidelines for the use of training data, the creation of new models, and policies for human-machine interactions.
Building the right governance model involves accommodating globally recognized security protocols, risk management strategies, and strict adherence to responsible training frameworks for AI models. For example, an AI model that aids in populating news feeds for users cannot be trained with data that creates a bias toward a particular political, religious, or demographic ideology.
With apt governance, AI software development gets an ethical oversight that ensures no harm is done to any stakeholders as the system evolves.
Scalability
The digital infrastructure on top of which AI initiatives are launched must be able to fully support the rapid dynamics of resource allocation. From storage to computing power to data management, AI projects will always require truckloads of resources.
Traditional servers, application architectures, databases, etc. may not be capable enough to facilitate the supercharged transactional nature of AI tech. What enterprises need is a digital infrastructure built with easy-to-scale and accommodative environments to support all possible innovations. The cloud is a great option, but enterprises need to have a clear understanding of how to select the right cloud options as well as build cloud-native digital ecosystems to support scalable AI initiatives.
The right workforce
Be it skills and expertise or culture, your staff plays a vital role in the success of AI initiatives. Firstly, they must be fully on board with the idea of using AI to run the business better. Fears of job insecurity must be eliminated, and employees must be given awareness of why AI is a good companion and enabler of productivity in their work.
They must be guided with frameworks and policies on how to effectively use AI systems, prepare training data, and handle AI application development without risks. By involving all business users who may also be non-technical staff, the AI growth journey will be more fruitful and accommodative of the organizational culture.
The bottom line
AI will be a key driver of success in every business’s digital journey for the coming decades. However, taking your steps into the world of AI requires quite a lot of homework and field setting as explained above. In this context, businesses need an experienced technology partner like Recode to ensure that AI is implemented in their digital assets with the most sustainable and beneficial approach. Get in touch with us to know more.
