As part of Information Age’s Artificial Intelligence Month, we are providing three CTO guides over the coming weeks on artificial intelligence: what it is, the industries most impacted and implementation best practices.

The first guide discussed how business leaders and CTOs understand artificial intelligence; and how they define the technology in the context of business. Opinions ranged from AI being just an algorithm, to a spectrum of technologies that are already active in everyday life.

What is artificial intelligence? Defining it in business — a CTO guide

In this guide, seven CTOs and AI experts provide their view on what is artificial intelligence; and how they define the technology in the context of business. Read here

The second guide focused on the industries that AI will impact the most, with insights from CTOs and AI experts. We concluded that it’s unfair to single out any one sector.

“Each month brings another exciting development, whether it’s optimising pricing or inventory in the retail sector, or making efficiencies in the oil and gas exploration and production life cycle,” found John Gikopoulos — Global Head for automation and AI at Infosys Consulting.

What industries will AI impact the most in the next few years — a CTO guide

In this guide, seven CTOs and AI experts provide their view on what industries will be most impacted by artificial intelligence. Read here

This is the third and final guide of Information Age’s Artificial Intelligence Month; and will focus on our experts best practice tips surrounding integrating AI into both internal operations and external facing products.

Build data into the DNA

Greg Hanson, CTO & VP at Informatica, believes that businesses — before anything — can’t forget about the data.

“When integrating AI into internal operations or products, businesses must not forget the data, because data is going to be the single biggest determinant of success in any AI project,” he says.

“Before AI can be fully integrated, it is crucial to build data into the DNA of your organisation. To do this, you need to have the right people, processes and technology.”

Artificial intelligence: Data will be the differentiator in the marketplace

Kicking off Information Age’s Artificial Intelligence Month, we look at the importance of data in the future adoption of AI; with the help of Greg Hanson, CTO & VP at Informatica. Read here

“C-level support is needed for building a strong data management strategy, with investment required in data-based roles – analysts, scientists, the CDO – and an embedded culture that understands and embraces the importance of complete data across the organisation. Your business may have the technology, but without high quality data, it will struggle to be successful in the future.”

Secure the data first

Hari Mankude, CTO at Imanis Data, says that it’s important to “take human error out of the loop by protecting the data itself before even looking to how AI can help analyse it.”

“If the data’s not safe to begin with, there’s no worthwhile forward path for AI within organisations. Forward-facing products? These are the result of corporate safety with valuable data, but also the willingness to have AI control it at the outset.”

The rise of AI as a business tool — eliminating human error

Driven by the complexity of today’s IT infrastructure and the volumes of data being managed, AI is finding its center in organisations that want to eliminate human error — according to Hari Mankude, CTO, Imanis Data. Read here

Don’t be afraid

Ed Bishop, Co-founder and CTO at Tessian, suggests that organisations shouldn’t be afraid to release a product that isn’t using much machine learning to begin with.

“It’s more important to get the product out there and start learning, than to wait until the algorithm is perfect,” he says.

Applying machine learning to products — Tessian CTO

Tessian creates machine intelligence to secure enterprises from threats executed by people, to keep sensitive data and systems private and secure. Read here

The Five Whys

Steve Ritter, CTO at Mitek, points to the Five Whys technique.

“It is a popular technique for root cause analysis,” he says. “I advise teams to use this technique to determine the best solutions to the problem. It may – or crucially may not – be AI. AI has tremendous value for many applications, but it is not a panacea for every technical challenge.”

The artificial intelligence journey: From biology to business to society, and beyond

The natural complexity of the human brain has paved the way for artificial intelligence, and now the applications of AI are being used to break boundaries in business; with insights from Steve Ritter, CTO at Mitek. Read here

Not a magic box

John Gikopoulos, Global Head for automation and AI at Infosys Consulting, shares Ritter’s opinion. He explains that AI isn’t a magic box that you can bolt onto existing systems and expect it to come out with transformational results.

“Organisations need to think very carefully about where AI can bring real operational and organisational value; this requires them to justify why it should be deployed, to measure the expected value against the ease and speed of deployment, and to have a very clear idea of what success looks like,” says Gikopoulos.

Artificial intelligence will lead to a ‘positive shift in the work people do’

The debate around artificial intelligence and jobs won’t go away. However, the most likely outcome of AI — looking at other industrial revolutions — will be a positive shift in the nature of the work people do. Read here

“It’s easy to have an ‘AI ambition’, but to put it into practice means convincing multiple stakeholders – including budget holders. The latter will likely be less impressed by claims of AI’s ability to deliver revolutionary new insight and improved processes. To convince them, you need to be able to give a good idea of the bottom line benefits. And that’s how it should be: AI is nothing if it cannot deliver greater efficiency and profitability to the organisation. That’s why there should always be a clearly-mapped business case for any AI implementation.”

“At the same time, it’s important to manage expectations. AI is a journey; it starts with a few small steps,” he continues.

“In the future, businesses will have AI running through them like a stick of Brighton rock; to get to that stage, however, takes small-scale experimentation to prove the concept in every application. As a result, organisations should expect small wins in the short-term, and this should be communicated within the organisation.”

Investing in artificial intelligence: What businesses need to know

To make artificial intelligence work for a business, leaders need to ensure that employee skills are honed in line with technological investments; according to John Gikopoulos, Global Head for automation and AI at Infosys Consulting. Read here

Artificial intelligence will become pervasive

Kalyan Kumar, Corporate Vice President and CTO at HCL Technologies, believes that artificial intelligence will likely become pervasive in the years ahead.

“Today, technology companies are using AI and cognitive technologies such as computer vision and machine learning to enhance products or create entirely new product categories,” he says.

“Technological progress and commercialisation should expand the impact of these technologies on organisations over the next decade. A growing number of organisations will likely find compelling use cases for these technologies. Those that become leaders will likely find innovative applications that dramatically improve their performance or create new capabilities while enhancing their competitive position.”

The AI roadmap: Ensuring adoption drives the desired business outcomes

How can organisations ensure that the adoption of artificial intelligence will drive the desired business outcomes? Kalyan Kumar, Corporate Vice President and CTO at HCL Technologies, provides his insight. Read here

“AI-led technologies are already foraying into internal operations, specifically IT operations. Industry experts envision that AI-driven management software will monitor and control IT infrastructure and applications, seamlessly and completely. Compute, power, storage and networking will be controlled dynamically to achieve maximum efficiency, productivity and availability,” Kumar continues.

“Meanwhile, human operators will be free to do what they do best, find further innovative solutions and plan for new capabilities. AI-driven automation’s long-term goal is to drive IT managed services towards zero downtime. As infrastructure becomes increasingly vital and complex, resource intensive models won’t work, which is where AI and machine learning will prove invaluable. Rapidly growing numbers of smart sensors are becoming available to pull in information and data from multiple sources, both external and internal, which can further be used to derive insights using sophisticated algorithms.”

CTOs: AI doesn’t replace jobs, it makes them more strategic

The fear that humans are having their jobs snatched away by AI is misplaced, according to Kalyan Kumar, Corporate Vice President and CTO at global IT services company HCL Technologies.The debate continues! Read here

“Integrating AI into any kind of operations begins with defining and implementing the automation strategy, and then picking the right use case. Adopters can either focus on rules-based systems to act on correlations and patterns, or follow a machine learning path to develop predictions, and then automate actions based on those predictions.”

“Humans will need to be in the loop to ensure that machine learning models are delivering the desired results. Time consuming and repetitive tasks are the ideal use cases to start with.”

“Organisations should ensure they have a scalable plan with that begins with some initial features and then add on further capabilities as time and budget allow. Another important element for ensuring AI-based models work is to ensure the quality and quantity of data at hand. If you don’t have good data, you can’t have good AI. It serves as the oil for any AI engine and the output is dependent on the training that the model has gone through.”