From Efficiency to Innovation: The New Roles and Projects of IT Teams
The roles and responsibilities of IT teams have evolved over the decades — and they are still changing, requiring organizations to continuously reevaluate how they utilize technical personnel.
In the early days of the PC and the web, IT was in the driver’s seat when it came to determining the types of strategic projects an organization would take on (e.g., moving into e-commerce, or digitalizing paper-based processes) and how it would do so. IT-driven spending surged throughout the 1990s, underscoring IT’s leading role during those years.
By the 2000s, though, IT had shifted into efficiency mode. The bursting of the dot-com bubble in 2001 and the lengthening lifecycles of business hardware and software meant IT was counted on to reduce costs and standardize processes. Teams concentrated on server management, application updates and patches, and end-user service requests.
Now the pendulum has swung back and IT is again focused on innovation. The growth of AI, cloud and the IoT, plus the effects of the COVID-19 pandemic on remote work, have required IT to take the lead on digital transformation for businesses. The seven “digital pivots” identified by Deloitte in a 2020 digital transformation study — ranging from building a flexible, secure infrastructure to delivering a unified customer experience — all require IT’s expertise.
The acceleration of digital transformation and IT’s new role
Companies now expect IT to enable business, not simply provide tech support. Because technology helps engineer the art of the possible for today’s organizations, IT is indeed a crucial partner whenever expanding into new markets, reaching a broader customer base, and/or modernizing product and service offerings.
A 2021 Adobe survey highlighted this new innovation-centric role for IT, as embodied in the growing prominence of the CIO. In the U.S., CIOs are now involved in virtually all strategic corporate initiatives, and most of them meet regularly with other executives like CMOs about directly commercial projects, such as improving the customer experience.
The main catalyst for innovative IT has been the acceleration of transformative business technologies since the late 2000s, with the launch of AWS (2006) and the iPhone (2007) as major inflection points. Since then, AI and machine learning, cloud computing and the IoT have all progressed dramatically.
AI and machine learning
The most recent Insight Intelligent Technology Index found that IT professionals saw cloud-delivered AI and machine learning as the technologies with the greatest impact on the future of IT. Both AI and ML open up numerous opportunities for organizations to rethink and optimize their business models, via IT-led innovation on use cases including:
- New product development: AI/ML engines can simulate and evaluate numerous possibilities for even the most complex product configurations. Siemens used AI in this way to determine the optimal choices for a railway system. Similarly, users may rely on AI to surface products and materials within a product lifecycle management (PLM) solution that match an uploaded drawing or image.
- Mass customization: Consumers are willing to pay up to 20% more for highly personalized items, and AI can fuel customization at a previously impossible scale. Well-trained AI can perform precise visual inspections to ensure that a custom product meets a customer’s specifications. For instance, the product may need to match the exact measurements from a 3-D scan of a person’s feet.
- Administrative assistance: In sectors like healthcare, AI and ML can help hospitals automate time-consuming manual processes like submitting claims to payers/insurers. Doing so reduces IT costs and user support requests while also freeing up time for technical teams to focus on other projects such as migrating more applications to the cloud.
Cloud adoption sharply accelerated in the late 2010s and early 2020s, coinciding with major changes in how organizations viewed the roles of their IT departments. Spiceworks found that 76% of businesses were planning long-term changes to IT operations in 2021, while they increased their cloud and managed services budgets and decreased their hardware purchases.
Clouds deliver scalable and elastic resources for everything from backup and disaster recovery to business analytics and supply chain planning. Given this variety in use cases, IT may juggle a variety of innovation-related tasks in the cloud, many of which now directly support business growth and evolving user needs. In Spiceworks’ survey, “growth/additional need” had nearly equaled “upgrades/refresh cycles” as a central driver of IT purchasing.
For example, IT personnel may assist marketing teams with the scaling of customer loyalty campaigns, select productivity tools for remote workforces, and update cybersecurity practices to manage user access to an expanding set of SaaS apps. IT has become a Swiss Army Knife for the entire organization.
IoT sensors in settings such as factories and retail stores have traditionally provided granular data collection capabilities, often for predictive maintenance purposes. But the convergence of sophisticated IoT hardware, advanced AI, and edge computing (the processing of data close to its original sources, instead of in the cloud) now enables IT to explore other possibilities, like using AI to proactively manage customer queues.
A combination of cameras and AI algorithms can detect customer movements, predict queue lengths and service times, and alert store managers to overcrowding, all in real time. IoT infrastructure and AI may also power in-store voice assistants, smart shopping carts, and real-time recommendations sent to customer devices based on their movements. Through such implementations, IT can drive bottom-line results for retailers.
Overall, IT is becoming more integrated with other departments on strategic priorities. Technical personnel drive the technology-dependent projects that now represent a major portion of all business innovation. Let’s examine a few examples of IT-led innovation in even more detail.
4 innovation initiatives redefining the role of IT
These real-world projects capture how IT has moved from efficiency to innovation over time:
Digital twins in manufacturing facilities
What are the optimal operating conditions within a factory? In the past, IT could not really answer this question. All it could do to optimize factory productivity was to ensure that technical systems stayed up to date and running, and that support requests were properly routed and fielded. But now, IT can provide a detailed answer based on digital twins that simulate the entire facility in real time.
A digital twin is a full virtual representation of a physical object, designed to be updated with changing data. Unilever has created digital twins for its factories, using IoT sensors that gather temperature and motor speed information which then relay it all to an enterprise cloud, where AI and machine learning algorithms use it for what-if analysis. Such sophisticated simulations, built on the IoT-AI-edge convergence, open up new business models.
Advanced simulation in Formula One racing and via the Unreal Engine
Simulation has also been central to Formula One racing for years. F1 drivers have only limited time and opportunity to test their actual cars on physical tracks. Driver-in-loop (DiL) exercises and computer simulations fill this gap:
- DiL works like a virtual test track, with the human driver using a steering wheel, pedals, cockpit, and chassis that are as close to the real things as possible.
- Computer simulations are run 100% virtually. Their results are useful in understanding car performance and track characteristics across numerous test runs.
Along similar lines, the Unreal Engine has been used to simulate complex environments, extending it beyond its original use case as a game engine for first-person shooter video games. Unreal Engine provides advanced 3-D modeling and a robust set of APIs for assisting with tasks such as orthopedic surgery training and driving algorithms for autonomous vehicles. Software is performing tasks that once required extensive mechanical infrastructure, showing how IT is now at the forefront of enabling innovation.
AI in financial services
Finance is one of the industries that has seen the most benefits from IT-led innovation on AI projects. Across finance, AI has influenced a variety of activities including:
- Making data-driven decisions about loan eligibility, even for borrowers with minimal credit histories.
- Using natural language processing and cloud computing to provide accurate answers to complex financial questions.
- Automating the onboarding process for new bank accounts, through bots with native optical character recognition capabilities.
Thanks to IT’s innovations, financial institutions can scale their operations and reduce associated costs, all while maintaining compliance with applicable laws and regulations.
AI-enhanced IoT sensors on the network edge
For workloads that require minimal latency and significant bandwidth, edge computing provides a viable alternative (or supplement) to cloud computing. Edge devices can perform advanced processing like AI-driven computer vision locally, close to the original data sources in question (e.g., IoT sensors). And with the emergence of 5G, they have connectivity with lower latency and greater reliability than ever before.
A converged IoT-AI-edge setup improves response times and saves bandwidth that would have otherwise been consumed by streaming all data to the cloud. Edge infrastructure can still connect as needed to cloud services, but isn’t completely reliant on them.
AR and VR benefit from the high performance achievable through edge computing. Likewise, robots can be efficiently guided across a factory, avoiding collisions along the way.
How to keep IT-led innovation initiatives on track
Although IT is now expected to innovate and not just maintain, it faces some hurdles en route to delivering tangible results for an organization. Here are some solutions:
Think product development, not software development
Projects that may seem interesting to IT might not be commercially viable. Teams can accordingly get lost in the technical weeds, working on software that won’t ever benefit business. To prevent this from happening, it’s crucial to have:
- A clear line of sight to commercialization for any new IT innovation initiative.
- Tight feedback loops to refine the direction of a project.
- Continuous tracking of metrics like recurring revenue, to gauge performance.
Have an overarching strategy, not a piecemeal process
Spotify has developed numerous machine learning applications to support its music and podcast recommendation engines. In doing so, it has also built a replicable process for moving from an identifiable problem to a commercializable solution:
- Find a specific source of friction and determine if automation can solve it.
- Ask questions and gather user feedback to create algorithms that meet actual needs.
- Start with a testable hypothesis (“This algorithm will keep listeners aware of new music they haven’t heard”) and then apply heuristics (rules, such as limiting eligible songs to ones released in the last month) to it, to see how it holds up.
From there, the machine learning application can be developed, if it addresses the identified friction and satisfies the tested hypothesis.
This approach lets organizations be systematic about how they innovate, instead of courting the common risk of implementing AI and ML piecemeal without any overarching strategy.
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