CIOs are under tremendous pressure from their companies to use AI for business benefit. So, it was not surprising...
that readers gravitated to our expert advice on AI. Indeed, the technology, in its many forms, dominated 2018's 10 CIO tips, which cover a broad selection of advice on AI challenges and opportunities.
The top CIO tips for 2018 range from how to use machine learning to improve customer experience, to the multiple ways in which AI is being applied to IT service management, to how to get the most out of robotic process automation, or RPA. The latter, touted as a must-have tool for digital transformation projects, is evolving from software that automates rules-based, repetitive tasks to "intelligent process automation," or IPA, which uses AI to continuously optimize business processes.
In addition to our coverage of AI challenges and rewards, this year's top 10 CIO tips include an examination of the most common open source software problems -- and how to manage them, as well as detailed advice on how to overcome the governance and risk management challenges associated with multi-cloud environments.
Here is a countdown of 2018's most popular CIO tips, from 10th to 1st place:
10. Glorified screen scraper no more, RPA keeps adding use cases
Contributing writer George Lawton interviewed a raft of experts on RPA this year, as he traced the rapid adoption of this labor-saving technology. As he reports in "Using RPA development to bridge the gap between legacy and cloud apps," one emerging use of RPA is as a tool to knit data from legacy apps to cloud-based systems. Many legacy apps are only accessible through a GUI app interface, making them less-than-ideal candidates for API integration. But, as experts explain, there are caveats, including RPA's tendency to break in certain environments.
9. Machine learning is the customer's new best friend
With the near-universal adoption of digital technologies, customer experience is fast-becoming a key CIO responsibility. According to research outfit Gartner, 50% of customer experience professionals are using digital analytics or big data to enhance the customer experience, but only 20% are using AI or machine learning. That's a mistake, according to Bill Delrieu, a Gartner research director specializing in the technical aspects of digital analytics. In "4 ways to use machine learning to improve customer experience," SearchCIO's Brian Holak breaks down a presentation by Delrieu on the four ways to add AI-rich tools to enterprise analytics platforms. Read about how to apply AI to: augmented/predictive analytics; segment discovery; experience personalization and customer journey orchestration.
8. BPMS and RPA join forces
Using business process management software to model, analyze, measure and optimize business processes is familiar territory for many CIOs. In "Digital transformation plan: RPA and BPMS tools, better together," business improvement expert Dan Morris lays out a case for using RPA and BPMS in tandem to drive digital transformation. Each technology's unique ability will help CIOs create interfaces for many different types of applications, giving companies the means to change quickly. One caution: Morris believes it's essential to optimize the business process or operation before automating with BPMS or RPA. "It's not a good idea to make a broken activity work faster. You just get errors faster and make everybody look bad," he said. (For a counterargument, read "Implementing RPA at John Hancock ushers in new wave of IT innovation."
7. Ignore these eight AI topics at your peril
Internationally recognized data scientist Anthony Scriffignano has his finger on the pulse of the current state of enterprise AI challenges and where the technology is headed. In "Dun & Bradstreet's chief data scientist: Don't ignore these 8 AI topics," SearchCIO's Nicole Laskowski asked Scriffignano to talk about the AI challenges CIOs needed to pay attention to in 2018. The list includes: not waiting to deal with shadow AI; how cybercriminals will use AI to sharpen cyberattacks; and why the use of AI in IoT will make devices smarter, more autonomous and more dangerous. Oh, and finding AI talent will continue to be challenge (forever?). Check out the entire list to ensure you're up to speed.
6. Are neural engines a thing? Do CIOs need to know about them?
One thing CIOs can count on in the rapidly evolving state of IT is the industry's appetite for jargon. The resurgence of AI has fueled a gold rush of new lingo, including terms that were coined by vendors mainly to differentiate themselves from the competition. Case in point: the vaunted neural processing unit, or NPU. In "Don't get caught up in the neural processing unit hype," Laskowski interviews experts on what neural processing units refer to and why CIOs should remain skeptical of the term, especially when used by a vendor whose name is not Nvidia or AMD.
5. Mastering multi-cloud management
The virtues of a multi-cloud strategy are well-known: Doing business with multiple vendors can increase agility, enable the business to tap into new services and break the dependence on a single vendor. But that flexibility and cost-savings are not without management risks, including challenges related to security, privacy and compliance. In "Overcoming multi-cloud's risks, regulatory compliance challenges," read what a CEO, a COO and two CIOs recommend doing to mitigate those risks.
4. Open source benefits vs. problems
Open source code can save IT shops time and money. But organizations still struggle with how to best manage the adoption of open source, according to industry experts. In "5 open source software problems -- and how to manage them," contributing writer Mary K. Pratt interviewed a wide range of experts to surface the five most common open source problems. In a nutshell, more rigor is required to reap the benefits of open source. "Companies are still ramping up their governance processes," Paul Welty, a VP at consulting firm North Highland, told Pratt. The tip lays out the five problems in detail and offers expert advice on how to fix them.
3. AI traits applied to ITSM
The delivery of IT services that enable and drive the business remains job No. 1 for CIOs. In "Prepare now for an AI-assisted ITSM strategy," change management expert Jennifer Wels urges CIOs to start strategizing now on how to apply AI to ITSM processes. A management consultant at training and research outfit Pink Elephant, Wels, trained in COBIT, ITIL and Lean, among other disciplines, breaks down seven AI capabilities and how ITSM can utilize them.
2. Use 'layered design model' for best RPA
Interest in robotic process automation grew dramatically in 2018, according to research from Gartner, as did vendor hype. In "How to create the best RPA architecture," authors David Brain and Phil Fersht lay out the structure and design CIOs will need to have in place to get the highest ROI possible from their RPA deployments -- minus the hype. Step No. 1 is using a layered design model that makes a clear distinction between the processes to be automated and the ones that will require human intervention. Read about two more must-haves in this primer by Brain, the co-founder and COO at Symphony, a Sykes Enterprises company, and Fersht, CEO and chief analyst at HFS Research.
1. AI and machine learning in ITSM is bleeding edge and growing fast
The most popular of our CIO tips in 2018 once again covers two areas of utmost concern to IT leaders: AI and ITSM. While the field is described as bleeding edge and the vendor offerings as immature, experts agree that the volume of data produced by IT systems makes ITSM a promising area for AI and machine learning applications. As contributing writer George Lawton reports, the use cases typically combine natural language processing and AI-infused data mining of ITSM data. Read the details on how CIOs and their teams can use AI to gain a richer understanding of their IT infrastructure and processes in our top CIO tip of the year, "10 ways to use machine learning and AI in ITSM to improve processes."