AI and Problem-First Approaches: The Art of Addressing Real Business Needs

In an era where business transformation through digital and artificial intelligence (AI) technologies faces constant change and daily stimuli from new vendors and new capabilities, it becomes critical not to put the technology before the problem. Leaders, especially those operating in complex corporate environments, have to keep in mind that ultimately what matters is solving a problem, with technology being a tool, not the end.

Focus On: Chartering a Problem-First Dos and Don'ts List

Having been through quite a few business and digital transformation initiatives, I thought of summarising some of the key dos and don'ts to watch out for:

Centering On the Core Business Problem: Embarking on your AI journey? The North Star should always be the business issue at stake. Align technology decisions with the strategic objective—whether it's enhancing customer experience or streamlining operations, the solutions should respond directly to fundamental business needs.

Escaping the Pilot Trap: Shifting from experiment to impact is a widespread challenge in digital transformations. Success isn't rooted in standalone tech advancements but in a holistic overhaul involving talent development, robust data strategies, and optimized operating models. As I mentioned previously, pilots are the seeds of long term change, but they need to be planted keeping in mind scalability and compliance at the very beginning.

Talent: In the digital revolution, talent is a key success factor. Future-proofing your organization involves cultivating an environment that nurtures skills, fosters innovative thinking, and embraces modern tools. This inspires tech talents to join and contribute meaningfully to your initiatives, but wise managers will invest even more in existing teams, leading to round professionals with company knowledge paired to the latest subject matter expertise.

Tackling Operational Transformation: Deploying sophisticated AI solutions isn't the finish line; it's a step in a process. Operational habits must evolve in tandem with technological progress. Pinpoint areas where secondary setbacks could arise—unravel these kinks to ensure the technology delivers its envisioned value. Wrapping processes and governance around new technology is key to ensure effective change management and long term adoption.

The In-House Imperative: While external partnerships can bring in fresh perspectives, the core of technological innovation must come from within. Crafting your tech stack in-house embeds a layer of authenticity and innovation that cannot be replicated by outsourced alternatives. It is also important to preserve "tech heritage", and in-house teams are best placed to do so.

Funding Innovation Continuously: The traditional project-based funding model is giving way to a more dynamic, persistent funding approach that supports a multitude of small, agile teams. By focusing on portfolios rather than isolated projects, finance departments can drive forward more sustainable digital advancements. Setting shorter term metrics, especially at pilot phase, will allow teams to move faster and receive funding in smaller, yet more frequent increments.

Distributed Digital Innovation: The goal is to cultivate an environment where innovation isn't the sole province of a dedicated tech team but is embedded across the organization. This approach empowers various departments to develop tech-based solutions tailored to their unique challenges and opportunities. It can be as simple as an idea generation platform open to everyone and parsed by a central team, or as ambitious as empowering individual business lines to run their own pilots.

The Changing Landscape of IT and Beyond: In a rewired organization, IT transitions from being the sole tech innovator to a foundational platform that enables security, distributes essential tools, and facilitates innovation across the business. This model requires a radical transformation of traditional IT roles and responsibilities, embracing modularity of every component to enable agile rollouts.

Data-Driven Culture: As generative AI is changing the game, ensuring that your data is ready to support such technologies is paramount. A forward-thinking approach to data architecture, rigorous quality control, stringent regulatory compliance, and a robust talent strategy are fundamental.

Real-Time Performance and Value Tracking: Performance metrics are more than indicators; they are catalysts for action. Implementing systems that provide live feedback on your data processes helps you to appreciate the efficacy of your technology, measure progress, and promptly pivot when necessary. Similar to the point around funding, performance monitoring has to happen in real time to inform individual conversations as well as product development.

By looking at this list, it is clear that my view is that the technology itself is just a tool—it transforms into an invaluable asset when wielded with a strategy grounded in value creation, persistent adaptation, and a distributed model of innovation that pivots around people and robust change management processes.

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Disclaimer: The views and opinions expressed in Chronicles of Change and on my social media accounts are my own and do not necessarily reflect the official policy or position of S&P Global.