There are a number of things that keep business leaders up at night. Whether it’s the unstable political and economic climate which only looks to further deteriorate, or the unrelenting pressure imposed on businesses by consumers to meet their ever-evolving demands – pick your poison. However, we’re forgetting one thing. Technology. Perhaps most pertinent of all, the rapid advances in technology which are constantly disrupting the way in which we do business are enough to give you insomnia by themselves. There’s no denying that if businesses fail to keep up the pace, they will inevitably fall behind the competition. It’s a case of evolve or die.
And one of the major players in this space is artificial intelligence (AI). As one of the latest buzzwords to enter our media landscape, the current perception remains that many businesses are still looking to adopt AI in order to reap the endless business benefits; more efficient operations and the delivery of new products and services to customers. However, are they taking the most optimal approach required to meet these goals?
It’s easy for businesses yet to achieve success with AI – or even to get started on their journey – to get despondent about the lead they perceive their competitors to have. They often see AI in absolutist terms: you either have cross-organisational, fully automated systems, or none at all.
But AI isn’t a binary – it’s a spectrum: one where successful applications are built on a platform of smaller, successful projects, which themselves were the result of trial and error. Rather than betting the farm on rolling out AI across the enterprise as quickly as possible, it's much more effective to experiment with initiatives that deliver real benefits on a smaller scale.
That doesn’t change the fact that there are several obstacles standing in the way of successful AI projects. None of these is insurmountable; nevertheless, organisations must understand what difficulties they need to overcome in order to develop and deliver projects that solve real business challenges.
AI: The key challenges
Earlier this year O’Reilly asked over 3,000 business respondents about their preparedness for AI and deep learning, including their adoption of the necessary tools, techniques and skills.
Of particular note is an AI skills gap revealed in the survey. A paucity of talent is seen as by far the biggest bottleneck for successful AI projects, identified by a fifth of respondents. This is an especially big issue in AI projects, since building such applications from scratch relies on end-to-end data pipelines (comprising data ingestion, preparation, exploratory analysis, and model building, deployment, and analysis).
It’s not just technical talent that enterprises need, though. They also require people with the business acumen to make strategic decisions based on the data and insights that AI provides.
Deep learning remains a relatively new technique, one that hasn’t been part of the typical suite of algorithms employed by industrial data scientists. Who will do this work? AI talent is scarce, and the increase in AI projects means the talent pool will likely get smaller in the near future. Businesses need to address the skills gap urgently if they are serious about developing successful AI initiatives. This will likely involve a mixture of employing outside consultants and developing the necessary skills in-house – for example, by using online learning platforms.
To be fair, most businesses in our survey (75 per cent) said that their company is using some form of in-house or external training programme. Almost half (49 per cent) said their company offered “in-house on-the-job training”, while a third (35 per cent) indicated their company used either formal training from a third party or from individual training consultants or contractors.
The other side of the coin – the business rationale for AI – requires management to identify use cases and find a sponsor for each specific project, ensuring there is a clear business case that is served by the technology.
Th role of data
Another key challenge to successful projects is ensuring that the data used is completely accurate and up-to-date. Machine learning and AI technologies can be used to automate – in full or in part – many enterprise workflows and tasks. Since these technologies depend on pulling information from an array of new external sources, as well as from existing data sets held by different internal business units, it’s obviously essential that this data is properly labelled.
The first step in this process is to establish which tasks should be prioritised for automation. Questions to ask include whether the task is data-driven, whether you have enough data to support the task, and if there is a business case for the project you plan to deliver.
Enterprises must remember that while AI and ML technologies can work “off-the-shelf”, to get the most out of them requires them to be tuned to specific domains and use cases, perhaps involving techniques such as computer vision (image search and object detection) or text mining. Tuning these technologies often – essential to delivering accurate insights – demands having accurately labelled large data sets.
Designating a Chief Data Officer (CDO) is key to solving the challenge of accurate data. A CDO is responsible for thinking about the end-to-end process of obtaining data, data governance, and transforming that data for a useful purpose. Having a skilled CDO can help ensure that AI initiatives deliver their full capability.
Introducing deep learning
Returning to our research, three quarters of respondents (73 per cent) said they’ve begun playing with deep learning software. TensorFlow is by far the most popular tool among our respondents, with Keras in second place, and PyTorch in third. Other frameworks like MXNet, CNTK, and BigDL have growing audiences as well. We expect all of these frameworks—including those that are less popular now—to continue to add users and use cases.
Ultimately, each and every single business is capable of implementing AI successfully. With a healthy dose of determination and the right level of investment in training, AI will flourish. However, it’s important for businesses to set clear objectives and goals from the outset. In doing this, it is possible to ensure the team encompasses the right level of expertise and skills to make a success of the company’s first steps into the use of AI.
Ben Lorica, Chief Data Scientist at O'Reilly Media
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