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Data and technology operating model impacts
The evolution of data and technology operating models requires a continued shift toward fluid, cross-functional and agile product delivery. AI presents paradigm shifts in how organizations achieve business outcomes. The most effective way to enable your technology teams to work on the highest and best value outcomes is through cross-functional teams incrementally moving towards that objective, regardless of the technology used.
However, in reality, existing technologies present a fundamental constraint to how these teams approach their work. Therefore comparatively, you must approach your teams’ mandates with your eyes wide open. You must accept that while AI will certainly be integrated to improve on existing outcomes, it may also fundamentally replace or transform outcomes. Situations like these might need more of a project pattern or temporary body to address this to avoid self-interested behaviour. An example of this is Google’s decision and need to cannibalize its traditional search capabilities with Gemini’s AI overviews or funnelling active users from Google Search to Gemini.
The next-generation data and technology function is rapidly evolving into a strategic orchestrator of autonomous systems, human-machine teams and intelligent workflows. This is driven by the emergence of agentic AI — autonomous agents that can plan and execute complex, multi-step workflows with a different type of human involvement. This represents a paradigm shift beyond simple automation.
This new reality necessitates key changes within your data and technology teams:
- They’ll need to incorporate new talent archetypes like Prompt Engineers, who translate human intent into machine instructions, and MLOps / AIOps roles, who manage the lifecycle of AI assets in production. To access these skillsets means partnering with HR teams to ensure role profiles and job architectures are kept modernized to attract the right talent, along with thoughtful use of vendors and service providers to access the talent needed.
- These teams must also lead by example, aggressively adopting AI in your organization’s own core business processes. Consider embedding AI throughout the software development lifecycle in areas such as:
- AI-enabled requirements gathering: for example, generation of user stories and PRDs
- Code generation or debugging: such as more, better, faster code
- Quality assurance: probabilistic systems instead of deterministic systems require a different type of quality assurance
- Areas of IT operations such as automated IT service management or AI-powered cybersecurity, for example to match a new level of AI-powered threats
To do this, maturity and documentation of these processes and expected outputs will help enable the use of AI. But perhaps even more important, your data and tech leaders will need to provide the appropriate guardrails and space within that rigour to enable their teams to experiment with different ways to achieve these AI-enabled outcomes.
- As organizations establish their AI operating models, IT’s role cannot be confined to a control function. Instead, it must work in tandem with the business and people leaders, including your talent/HR teams, to bring innovative ideas to the table and serve as a catalyst for innovation, all while balancing speed of adoption and availability of skillsets.
IT should focus on creating the platforms and foundations for innovation while establishing guardrails that protect the organization — inevitably, IT will be looked at if and when things go wrong — without stifling creativity or speed. Governance assets — including standards/policies, working committees and frameworks — need to be quickly yet carefully updated or crafted and rolled out, with mechanisms for continuous improvement, to enable the organization to start delivering.
As we face this technological revolution, the journey toward a next-generation data and technology operating model is not without its challenges.. But it also presents a unique opportunity for growth and innovation. Like previous waves of technological change — such as outsourcing, SaaS adoption and cloud migration — the imperative for data and technology leaders within businesses is clear: embrace the change but maintain a strategic posture, foster and enable cross-functional collaboration, and lead by example from within.