Of the many technologies likely to deliver significant value in the near future, artificial intelligence (AI) seems firmly entrenched at the top of the list for CIOs. Indeed, almost all (95%) CIOs, CTOs and technology leaders queried by IEEE agreed that AI will drive the majority of innovation in nearly every industry sector over the next five years.
“In 2022, expect AI engagements to become larger, more strategically meaningful, and more mission-critical — with a focus on long-term scalability.”
2022 will be a year in which AI goes from experimental to essential. “The focus will be more on AI-driven transformation that solves bigger business problems with enterprise-focused solutions,” says Jerry Kurtz, executive vice president, Insights & Data, at Capgemini Americas. “AI is a powerful enabler and capability, but the time for proof-of-concepts and science projects is rapidly coming to an end. In 2022, expect AI engagements to become bigger, bigger on the strategic plan and more critical – with a focus on long-term scalability.”
This will challenge most CIOs. Technology powerhouses aside, many enterprise IT organizations are relatively new to AI. “AI adoption continues to gain momentum, but it is still in its infancy,” says Yugal Joshi, Partner at Everest Group. “One of the biggest challenges CIOs face is ensuring they’re investing in the right use cases that deliver the maximum return on investment, especially because the applicability of AI is quite broad. .”
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]
AI trends to watch
Against this backdrop, there are a number of other developing trends in AI that IT leaders will be wise to watch this year.
1. The fight against data tops the agenda
Most companies are relatively early in their AI journey. Unlike the Googles or Facebooks of the world, they spend the majority of their time and resources managing data. They need to build modern data pipelines.
[ How can public data sets help your AI work? Read also: 6 misconceptions about AIOps, explained. ]
“Most AI models crave massive amounts of data, and organizations need to create flexible data pipelines that can scale to support thousands of sources, incorporate structured and unstructured data, and deliver it to data. scientists in a meaningful and reliable way,” says Erik Brun, Senior Technology Partner at West Monroe. “Traditional ETL (extract, transform, and load) and relational stores must be supplemented for more scalable data lakes, and in many cases data streams must be provided to be processed in real time.”
2. Automated Process Discovery Boosts RPA Efforts
The future will be streamlined. Business leaders can visualize their organization’s automation potential using new process discovery technologies. “While not solely focused on automation opportunities, these technologies will provide process insights that are not obtained by any other means,” says Wayne Butterfield, director of ISG. Booming process mining, task mining, and conversation mining are “running on steroids,” Butterfield says, giving the company more self-sustaining ways to develop a pipeline. robotic process automation (RPA). “These technologies will really come to the fore in 2022 and amplify the use of intelligent automation in the process.”
[ Read also: 4 Robotic Process Automation (RPA) trends to watch in 2022. ]
3. AI enables efficient supply chains
Smart supply chain applications are expected to become the rule rather than the exception in the future. “From supply and demand planning to digital manufacturing and logistics, supply chains in 2022 will need to be continuously transformed, enabled by AI and, most importantly, given all the recent disruptions, sustainable,” said Kurtz of Capgemini Americas.
4. Customer-centric AI is moving forward
“The pandemic has seen the adoption of AI in customer-facing roles such as virtual agents increase,” says Everest Group’s Joshi. “It will continue but with more maturity and complex use cases.”
5. Natural Language Generation (NLG) is Going Widespread
OpenAI recently made its large GPT-3 language model, already used by hundreds of applications, available via API. The most public example of the power of NLG, GPT-3 can be used in applications that require a deep understanding of the language, from converting natural language into software code to generating answers to questions.
“NLG, which historically focused on transforming numerical data into textual information, now generates text from textual data points and is beginning to be a game changer in creative writing,” says ISG’s Butterfield. “The possibilities are endless, with GPT 3 also being used to create unique training datasets for NLP, real-time generation of unique responses in conversational AI platforms, and the capability is even being used to generate 2022 should see even more uses of NLG and really propel it to the masses.
6. Talent Shortage Threatens Progress
Given how rapidly the AI landscape is changing, effective talent management has become a strategic differentiator for enterprise IT organizations. “This will need to consist of world-class recruitment and retention initiatives that foster inclusivity and a culture of lifelong learning,” says Kurtz of Capgemini Americas. “The market has never been more competitive for people with AI skills, and this trend is set to continue for years to come. As such, strategic partnerships will also be essential across organizations and industries.”
7. AI is transforming IT productivity
The increasingly complex and powerful computing environment of the future is too much for human technology professionals to handle. “Another area of increased AI adoption will be managing the modern systems CIOs are building,” says Everest Group’s Joshi. “These systems cannot be managed by humans alone. The observability, intervention and in-depth analysis necessary for these systems will be enabled by AI. Joshi seeks actionable, real-time interventions.
Expect help from the app development side as well, given the growing hype for generative AI. “In 2022, CIOs will also begin to assess the applicability of AI in the engineering organization to fundamentally transform developer productivity,” says Joshi. This area has been researched for a long time, he notes, but significant progress has been made recently.
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