by Michael Bertha

3 ways for CIOs to improve their positioning with AI

Nov 10, 2020
Artificial IntelligenceDigital TransformationIT Leadership

Artificial intelligence is changing the pace at which CIOs can achieve an enviable position on their leadership teams

virtual brain / digital mind / artificial intelligence / machine learning / neural network
Credit: MetamorWorks / Getty Images

Stephen de Campos, CIO at Hunt Consolidated, co-authored this article.

Understanding the role of IT through the eyes of organizational stakeholders is an effective technique for determining how IT may need to change. For the longest time, IT was viewed as a cost center, with a primary emphasis on performance and cost. Over the past 10 years, IT’s role has been elevated in many organizations. IDG’s 2020 State of the CIO survey personifies this trend: 75% of surveyed CIOs identified themselves as business strategists or transformation agents, and 67% claim revenue generation among their job responsibilities.

However, in the era of digital transformation, CIOs need to work harder (and smarter) to secure or maintain the right to be viewed (and funded) as a differentiator. Enter artificial intelligence. AI is changing the definition of “doing the basic things right,” blurring organizational boundaries, and changing the pace at which CIOs can achieve an enviable position on their leadership teams.

Stephen de Campos, the recently appointed CIO at Hunt Consolidated, a multibillion-dollar oil and gas exploration and production company based in Dallas, has partnered with me on this article to illustrate how CIOs can use AI to optimize IT operations, create new ways to win for their organizations, and boost perception of their company in capital markets.

Step 1: Reinvent the basics with AIOps

Many CIOs graduated from service provider to business partner by getting the basics right: providing high-quality service in traditional IT Ops domains such as network, infrastructure, and help desk. However, those traditional strategies cannot keep up with the exponential demands of cloud, IoT, and big data that far exceed human capacity. To prevent IT Ops from becoming a limiting factor on your digital transformation, you have to reinvent the basics.

AIOps, which represents the union of AI and IT operations, is how several CIOs are doing just that. AIOps platforms aggregate data from various monitoring and service management sources and apply machine learning to contextualize data, identify patterns, and unlock new levels of intelligence and automation for IT Ops. Early adopters may initially focus on identifying patterns in monitoring data to proactively deploy patches to prevent unexpected downtime. More advanced organizations may deploy virtual service agents, or “chatbots,” that automate key IT service management functions such as ticket analysis and password resets.

In either case, the deployment of AIOps can save time for IT by automating commoditized tasks, lowering mean time to resolution, and limiting unplanned work. The business also gets higher productivity by avoiding manual reboots and receiving higher quality service that is enhanced by AI. In a Forrester study, a composite telecommunications company deployed AIOps and experienced approximately $7 million in labor savings over three years. These savings create a self-funding mechanism that can extend IT’s brand and influence across the organization.

Step 2: Conduct self-funded pilots and deliver surprising value

With a self-funding mechanism established, look to your IT business partners to start a consultative dialogue with their business stakeholders. Start by asking about the key metrics they are targeting. For example, a sales function may be focused on increasing conversion rate, while a customer service function may want to optimize average handle time. Work with business stakeholders to deconstruct metrics into their underlying processes to identify steps that can be enhanced by AI.

Start by looking for steps in the process that can be automated. These are typically steps that are highly dependent on repetitive manual data entry or require toggling between multiple applications to look up different types of information. For example, a health insurance client was able to reduce the time to price complex inpatient hospital claims by more than 1000% by automating the entry of claims medical codes into a pricing application.

After you have identified areas to automate, ask questions to uncover opportunities to make processes and experiences more intelligent. A good way to broach the topic is to ask questions such as “what information do you wish you had?” or “what information would make the customer’s experience better?”

Alternatively, ask if there are certain “plays,” or repeatable patterns, that they would like to scale.  Home Depot, for example, reduced average handle time in customer care by 4% and improved customer experience by 6% in 10 weeks by leveraging machine learning to analyze call center data and identify behaviors linked with positive outcomes. It also deployed AI bots to guide customer care associates toward actions that are most likely to drive those outcomes.

Next, create an inventory of the various opportunities you gather from the business and score them in terms of impact and feasibility. This will allow the team to identify one or two use cases to invest in further with your self-funding mechanism. Impact assessments will cover the degree to which the opportunities will drive business outcomes such as reduced cost, increased revenue, or improved experience; while feasibility assessments identify the availability of data, technology, and skillsets to execute. Those opportunities with high impact and high feasibility are typically the best for pilot projects.

Once those assessments are complete, conduct a rapid discovery session with business stakeholders to hash out a rough business case. This provides an opportunity to make sure the benefits justify the cost, align on an MVP solution, and get to work. Remember to baseline targeted metrics before you begin so you have a strong point of comparison and evidence to support future scaling if your MVP is successful.

Step 3: Make the case for your formal AI program

With one or two successful pilots completed, you are ready to formalize your AI program. IT should partner with the beneficiaries of IT-funded pilots in the business to communicate the success of the pilot, articulate other promising use cases, and make the case for a dedicated AI center of excellence. This group, which is common in organizations with an AI-first strategy, focuses on identifying AI use cases, conducting pilots, and scaling those cases that improve outcomes. Success from those early pilots can help CIOs sell the hard benefits that AI can deliver across products, processes, and experiences if there were dedicated full-time resources.

Also consider how AI can strengthen the perception of your company in the capital markets through Environmental Sustainability and Governance (ESG) reporting. In a world where digitization has outpaced human capacity, AI is the only way to effectively secure your organization’s data and technical estate. It is also, in many situations, a viable solution to make inroads against threats associated with climate change. A Bank of America study found that companies in the S&P 500 that scored in the top fifth of ESG ranking outperformed their counterparts in the bottom fifth by at least three percentage points annually from 2014-2019.

Assess your positioning and make your move

Be honest with how IT is perceived in your organization – are you viewed as a service provider responsible for keeping the lights on, or are you seen as a critical part of the business to enable scale, growth and differentiation? If you are the former, consider how AI can serve as a self-funding mechanism to greener pastures. If you are the latter, I believe the old adage is “Stay hungry. Stay foolish.”