Global
Artificial Intelligence (AI) is no longer a hype or science fiction, it is a reality organizations are waking up to. For businesses, AI holds immense potential to transform human productivity, enhance revenue and accelerate innovation. Yet an important question facing many organizations is – how to gear up for AI adoption.
AI, in simple terms, refers to a scenario in which computer systems are programmed to mimic human intelligence to make decisions and perform various tasks, otherwise performed by humans.
Technology advancements in the form of Machine Learning (ML) technologies, predictive analytics, virtual assistants, fraud detection sensors, and driverless cars are all pointing towards the revolutionary change that AI is bringing across businesses and industries.
In the digital economy where organizations are facing cut-throat competition and the threat of major industry disruption at lightning speed, AI’s growing potential for breakthrough innovations in sectors like banking, retail, healthcare, and even more traditional ones like manufacturing, is highly recognized. IDC’s recent findings predict that worldwide revenues for AI and cognitive systems will reach $12.5B in 2017, which is an increase of about 60% over the previous year. This is expected to go up to $46B by 2020.
Consequently, the case for AI is strong as it reduces the effort required for time-consuming and repetitive tasks. But real implementation of Artificial Intelligence technologies could be complicated, and it may not be an easy path for organizations to tread unless they carefully plan their way forward.
For those looking at making the first moves, here are some useful steps on how to integrate AI into your business models and strategic plans.
Understand What’s in It For You
Experts suggest that getting familiar with what AI can do for your business will be a good starting point for adoption. There is enough evidence on how different industries are using AI to drive breakthrough transformations. From diagnosing sophisticated illnesses in the healthcare space to enhancing customer service in retail to fraud detection in banking, there are numerous ways in which AI applications can manifest for businesses. You can identify possible application areas based on your specific business needs, and the insights and data that your business generates.
Before an organization decides to dip its toes into AI, it is important to understand the implications of adopting the technology on its processes, people and technologies, and align its AI plans to the overall business strategy. Find out which areas will have the most potential impact with AI adoption – E.g. is it customer service or business processes or sales and marketing? Howe can the company add AI capabilities to enhance its existing products and services? What are the business problems that we can solve using AI?
Get Your Data Sorted
AI systems draw their potential from the wide universe of data served to them– business information, social media, and information generated from across various devices. Computer systems are programmed to derive intelligent insights and take actions based on data. So, for AI to work effectively, enterprises must have well-structured data that can support AI applications. Again, ask questions like – Do we have necessary data to launch AI applications? How are we currently collecting and analyzing data? Will AI really add value in offering insights? Reliable, fast and accurate data is a prerequisite to achieving success with AI.
Build Your AI Team
Make a realistic assessment of whether your organization has the talent and skills to implement AI. Executing Artificial Intelligence projects requires specialized skill sets that are often hard to find. Organizations can build ambitious AI plans but that may not be implemented unless they have the right resources. Data scientists, and people with analytical skills and advanced programming skills like Python, C/C++, Java will be highly essential for AI projects. IDC predicts the need for over 180,000 people with deep analytical skills in the US by 2018, and the requirement for people with data management and interpretation skills to be even higher.
Companies should consider these constraints and find ways to support their talent needs before launching into new AI projects.
There are resources like Udacity courses and Stanford University’s online courses on AI that could be useful in training new teams on the foundations of AI.
Start Small and Get Expert Advice When Needed
Identify what you need to achieve with AI and the potential of your organization to do that. If you find a considerable gap, it makes sense to partner with AI experts who can bring in the expertize and ideas to support a smooth transition. No matter how lucrative it might seem to plan multiple entry points into AI, the key is to start small with a pilot project, identify and state clear goals for the teams and assess performance on a regular basis. After the pilot phase is over, you will have useful insights into how best to progress towards a full-blown AI implementation.
