“These tools are incredibly powerful and sometimes convincingly wrong,” says EY’s Diasio. Unfortunately, though, there’s a tendency for people to go on autopilot. Humans need to work with the tools and review the output, not just casually, but in detail. “You need to plan the time for that,” he says.
Srivastava says most projects keep a human in the loop to make final decisions, but follow through is key. “How do you go from data, to insights, to action in a continuous loop?” he asks. “That’s the number-one reason why people don’t get economic outcomes.”
Data prep matters, except…
In areas such as supply chain and analytics, having all of your data in a form readily available to an AI model is essential. “Data is the lynchpin to AI success,” says Nafde. “Start with your data strategy before your AI strategy, and align your AI strategy with your business strategy.”
Diasio agrees. “Make sure the data you have is discoverable by AI systems, which might mean building an enriched catalog using generative AI or using it to build an ontology on top of structured data,” he says. “In many instances, it’s a significant improvement in productivity when using AI to streamline these workloads. In some data migration activity we’ve observed a 40% increase in various steps along the way and an increase in speed.”
Lilly is already using AI-enabled tools to speed the ingestion and cleaning of the data used to train and fine-tune its pharma models, Rau says, and Genpact also uses AI to prepare its data for consumption by its AI models. “We have a ton of data and two-thirds of it is unstructured,” says Srivastava. “You can use generative AI to auto build a semantic layer on top of your data. You need to understand what data sits where, how it’s linked to something else, what the quality is, the lineage, and where else it’s being used.”
That work is difficult and requires highly skilled talent, which is why many enterprises bring in a partner to help with the work. But AI can automate the creation of that semantic layer for you. It’s not perfect, but it might get you to 80%, Srivastava says.
However, Diasio says you don’t always need to organize internal data to leverage AI. “For example, with generative AI and the pre-trained models available on the market, creative tasks like product development, or summarization tasks such as contact center transcripts, may work effectively out-of-the-box in the appropriate contextual setting and with clever prompting,” he says. “This can help companies accelerate the use of AI while they continue to curate their internal data and harvest their expertise.”
Ensure suitability of AI capabilities before turning them on
“CIOs should invest in new or upgrade existing CRM, IoT, ITSM and business intelligence tools that include AI/ML,” says Jevin Jensen, research VP at IDC. “Time to value is dramatically reduced when you select a solution from an existing off-the-shelf vendor that has added AI features to software you’ve already implemented.” You may simply need to turn on the feature or add a plug-in. Just check to make sure you can opt out of having your data used to train the vendor’s models, he says.
While new AI capabilities in enterprise software such as those offered by Salesforce and ServiceNow promise substantial workflow productivity benefits, you shouldn’t just turn them on without fully understanding how they fit with your workflows. “We recently had a deep-dive session with ServiceNow on how to use intelligent prediction, virtual chat, and other capabilities in alignment with our business strategy,” Nafde says. For example, the bank’s virtual chat function includes a few dozen use cases. Some may be able to use it right out of the box, some will require customization, and some won’t be fit for purpose. “We need to decide which capabilities will be useful,” he says.
Eaton has already turned on some AI features in ServiceNow, with encouraging results so far. “It’s helping from a case management perspective, finding threads of defects we can improve, finding the root cause, and offering solutions that can reduce case counts,” Redmond says.
The conundrum with embedded AI in enterprise software, though, is it may not offer a compelling solution today for your organization’s needs. In this case CIOs, especially if they face competitive pressures, may find themselves in a dilemma: “Should you wait for your line of business application vendors to incorporate AI and sacrifice time to market while you wait for the vendor to build it, or should you build an enterprise architecture strategy where you have your own custom implementation and infrastructure around it, but it’s expensive and needs ongoing investment?” asks Srivastava. “Therein lies the challenge.”
Lilly is also leveraging AIOps capabilities in its IT operations. AI-enabled tools include an incident detection and response system that swiftly detects anomalies, predicts potential problems before they can escalate, determines root cause of failures, and assesses the business impact of technical issues. “For example, if the order processing system experiences delays, AIOps can quantify the impact on revenue and customer satisfaction,” says Rau. This enables the team to prioritize and resolve the most critical issue faster.
What to do – and not to do
While Webster Bank is still in the early phases of its AI journey, Nafde has learned a few things along the way so far: Get your data in order. Align your AI strategy with your business strategy. Put the right KPIs in place before you start. Then start small, show proof of value, scale gradually, and educate and communicate with your stakeholders every step of the way, he says.
Equally important is to partner to get off the ground, but build out your team with the tools and expertise to develop and maintain new AI capabilities. And don’t underestimate the need to build trust. “Stay ahead on your messaging,” he says. “Expect skeptics, do town halls, and have leaders step in.” After all, there’s a lot of fear and general reluctance to accept change when new technology is introduced. “The challenge here isn’t just about AI,” he adds. “It’s a classic change management problem.”
Be strategic and limit the number of projects you take on, adds Redmond. “Focus on a few things and go deep,” she says. Find trusted partners to help you get started, and take advantage of AI capabilities your SaaS vendors have introduced into their products — when they make sense. Don’t overlook what’s already in your ecosystem, she adds.
“Culture matters,” adds Rau. “Change is tough, so CIOs need to lead a cultural shift by demonstrating the innovative open-minded behaviors you’re looking for, and creating an environment that encourages learning and innovation around AI. Our biggest risk is if our employees don’t use AI as much as they could.”
Getting some wins under your belt, like stakeholders using it until they’re comfortable with the new technology, is a real confidence boost, says Redmond. “That gets the fear factor down,” she says.
