"Second, they need environments to understand and correct how the AI capability does its job. Scatter charts, histograms, etcetera aren't business-expert friendly.
"Even traditional dashboards with charts and graphs can be incomplete. Business intelligence, data curation and management environments will help close this gap in trusting and on-boarding AI.”
Mike Guggemos, CIO at Insight, expects that the fears will begin to subside, leading organisations to make a renewed push across all business functions to use as many AI-driven services as possible.
"2018 was the year of peak AI hype and anxiety. In 2019 we will move into the next stage of the hype cycle – nascent yet broad enterprise adoption," he says.
"We'll see an acceleration of real-world applications of AI and the technology will seep into the fabric of businesses and offices everywhere, changing business operations without most even noticing.
"AI services are becoming available to organisations from providers such as AWS and Microsoft as reference architectures which will emerge into - not as easy as it sounds - plug and play standard services.
"As AI becomes part of everyday business life, widespread and much hyped fears of job losses will subside as people realise that it mainly assists rather than replaces humans."
The talent pipeline has struggled to keep up with developments in data science tools and techniques. ABBYY’s Global VP Neil Murphy expects that this will lead one-third of organisations adopting AI to hire more IT staff in the next six months.
“The need for specialised skills to work with AI and automation technologies will drive a huge hiring spree in 2019 across the world – in Europe and the US, one in three businesses will need to hire more employees in their IT departments to accelerate their tech offerings in 2019," he says.
"Across industries from manufacturing and healthcare to non-profits, government, and financial services, the biggest challenge will be the same: upgrading their IT infrastructure and replacing legacy systems without failing on their digital transformation efforts.
"In order to achieve this, businesses will need to invest time and money into sourcing the best talent with the best skills for the job, or risk falling behind the competition."
The AI skills gap will also cause universities to rethink their strategies. Dr Greg Benson, professor of computer science at the University of San Francisco and chief scientist at SnapLogic, predicts that machine learning will soon be required in computer science degrees.
"Until recently, machine learning and artificial intelligence were offered as elective courses in undergraduate computer science programs," he says. "In addition to the emergence of data science Bachelor's and Master's degrees, the core computer science curricula will require students to take machine learning as a compulsory course.
"In addition, the introductory computer science courses will include machine learning topics as early examples and projects."
Deep learning and Python
Ben Lorica, Chief Data Scientist at O'Reilly Media, expects deep learning to gain early traction to supplement existing machine learning applications.
"Aside from new systems that use vision and speech technologies, we expect early forays into deep learning and reinforcement learning will be in areas where companies already have data and machine learning in place," he says.
"For example, companies are infusing their systems for temporal and geospatial data with deep learning, resulting in scalable and more accurate hybrid systems -i.e., systems that combine deep learning with other machine learning methods."
He adds that specialised hardware will be produced to make the technique more accessible, but predicts that hybrid models will be more commonly deployed.
"The resurgence in deep learning began around 2011 with record-setting models in speech and computer vision. Today, there is certainly enough scale to justify specialised hardware - Facebook alone makes trillions of predictions per day.
"Google has also had enough scale to justify producing its own specialised hardware. It has been using tensor processing units in its cloud since last year.
"Therefore, 2019 should see a broader selection of specialised hardware begin to appear. Numerous companies and startups in China and the US have been working on hardware that targets model building and inference, both in the data centre and on edge devices."
Exosel CTO Golombek believes that Python will emerge as the leading data science language.
“In 2019, the variety of data science languages will continue to grow. But there is also a clear trend suggesting that Python will become the leading language for machine learning, and Python-based technology, such as the deep learning library TensorFlow, will continue to proliferate," he says.
Advice for enterprises
Enterprises taking early steps into AI may find the biggest benefits in augmented analytics, which can be embedded in enterprise applications to provide automated insights and reduce the need for professional data scientists.
More advanced capabilities will require a bigger commitment.
“Think of AI as part of your human capital, not a replacement. The time, money and effort to train AI is as intensive as a human employee," says Forrester analyst Goetz.
"AI is not something students will come out of school knowing what to do even if they have had AI coursework. Employees today are learning by trial and error by just jumping in.
"There are best practices and disciplines emerging in enterprise AI endeavours that demonstrate AI needs to be developed the way we develop our human talent in the workplace.
"Firms that look for the qualities in employees and potential employees are filling their AI workforce faster and putting the time in, training in, and career path development in to become a business that benefits from AI competitively."