How companies can break barriers to entry by integrating AI into their operations
Many companies are now aware that integrating AI and generative AI into their work processes can streamline business operations, improve efficiency and save time and money. Some are on their way to meeting this reality; According to Deloitte’s recent Generative AI report, 18-36% of organizations say they are already achieving the benefits they expect from using Generative AI to a “large” or “very large” degree, depending on the type of benefit being achieved. pursued.
However, despite the clear benefits of leveraging this breakthrough technology, integrating rapidly evolving AI into business operations also comes with several challenges. As AI continues to evolve, the obstacles business leaders face in adopting these technologies also continue to grow. There are therefore challenges that must be overcome before organizations can fully realize the benefits of AI and generative AI – but business leaders can rest assured that there are key strategies they can implement that will play a crucial role in enabling successful integration.
Global Business Leader for Hitachi Vantara.
Don’t do AI for AI’s sake
A common pitfall for many AI projects is the lack of a coherent strategy and defined objectives. An AI project, even with heavy investment, will not deliver results if companies do not take the time to align it with their business objectives and define exactly how it will add value – and how much value – relative to the costs of implementation. After all, there’s no point in rolling out a project with the aim of adding £5m to your turnover if it’s going to cost you £10m to get there.
Whether you want to deploy in your own data center or in the cloud, AI projects can be expensive, especially when you consider both the infrastructure needed for deployment and the services needed to make it all work. Organizations therefore need to be very clear about why they are doing what they are doing and what the return on investment will be. Business leaders should resist the urge to jump on the AI bandwagon and instead pursue thoughtful projects that align with their organization’s overarching goals.
Part of this means being careful about “AI washing” – the over-promotion of overhyped AI solutions – and focusing on pragmatic applications that deliver real value. These may be smaller, more niche use cases, as opposed to large-scale process overhauls. For example, a construction company that considers health and safety one of its top business priorities can install AI-enabled cameras on site that can monitor workers’ workwear throughout the day. If someone is not wearing the correct protective equipment, the AI will report this to a supervisor who can intervene to ensure this is ensured, ensuring optimal levels of health and safety at all times. In this way, the company uses AI in a way that gives them tangible, measurable business results that suit them.
To help them find these targeted use cases, organizations should look for partners who can help them analyze their business from the outside in and identify the areas where AI can really make a difference for them.
Well-organized data is the backbone of successful AI
After defining their AI strategy, organizations need to think about how to implement it successfully. AI is extremely data intensive, especially when it comes to some of the latest generative AI use cases being explored. Companies therefore need to be able to locate all their data assets, consolidate and clean their data, and streamline their repositories to make them suitable for AI applications. This requires a comprehensive understanding of both their data sources and storage services.
To create an AI chatbot, for example, a company must train it on a plethora of disparate data sources, from user manuals to past customer calls. Only then can it be programmed, using that existing data, to accurately respond to frequently asked questions.
Make sure you have the right skills and pay attention to regulations
To successfully implement AI projects, companies must find or recruit the necessary skills for their business. As AI expertise is currently in extremely high demand, people with the relevant skills can be both difficult to find and expensive, so time must be taken into account.
They must also stay abreast of evolving AI regulations to ensure compliance. For example, the European Union’s landmark AI law recently came into effect, regulating the development, use and application of AI for both developers and operators. This important step highlights the importance placed on the safe and ethical development of AI technologies within Europe – a sentiment we also see taking hold around the world.
There are numerous elements that must be taken into account with these regulations. For example, when entering data into AI models, organizations should ensure that they have received appropriate permission to use it, and that it is anonymized where necessary. There are also restrictions on data leaving the location of the person who collected it, for example moving it to the cloud.
Back to basics
Without the right strategy, skills, and data sources in place, AI projects cannot be successful. However, somewhat reassuringly, this is not a new challenge; these are all obstacles that may have contributed to the failure of IT projects since the inception of such projects. Yes, the capabilities that AI offers are different, but the fundamental elements that companies need to think about when implementing these projects remain the same. It’s a fact that many business and IT leaders should take comfort in as they embark on their AI journey.
If companies can do their homework – with support from the right partners – to ensure that the AI initiatives they want to integrate will add tangible value to their business, and then take the necessary steps for a rewarding implementation, they will be successful books. . Successful AI integration is here for the taking – organizations just need to take the time to really get it right.
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