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This article was contributed by Atul Sharma, cofounder and CTO of Peak.
Data collection is skyrocketing. The amount of data created, consumed and stored worldwide is set to increase by over 50% between now and 2025. Businesses understand that evaluating their data more effectively provides a competitive edge, and that it will be artificial intelligence, not business intelligence, that will unlock this potential — but there’s a striking gap between the scale of AI investment and tangible returns delivered.
Fortune 500 companies are spending an average of $75 million on AI talent. But only 26% of AI initiatives are put into widespread production with an organization. Decision intelligence (DI) is now helping companies bridge the gap between theoretical AI and commercial AI, with Gartner predicting that more than a third of large organizations will be using DI within the next two years.
The impact of DI for technical teams
While AI can be a somewhat nebulous concept, decision intelligence is more concrete. That’s because DI is outcome-focused; a DI solution is built to deliver against a business objective. As such, it can help CTOs and technical teams run data projects that deliver quantifiable results for their business.
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Today, commercial AI strategies are plagued by a host of problems that limit their effectiveness. Among them, the fact that data scientists are trained to think “bottom-up” — to understand what data they have available, and devise a solution from there. More often than not, this results in lengthy technical projects that address data problems, rather than commercial needs.
By flipping this approach on its head and building with an outcome in mind, decision intelligence addresses many of the pain points that hinder businesses from quantifying value from their AI investment. Working backward from an objective, technical teams can build needed solutions and unlock value from AI faster. By rooting these solutions in the decision-making processes that drive every aspect of an organization, DI can deliver commercial benefits across an entire business.
Intelligence trained on marketing data and intended to optimize the marketing funnel will only ever do that. Additionally, intelligence trained on an organizational dataset and designed to optimize business operations holistically is not so limited.
Bringing a DI mindset to data architecture
The way we design data architecture is key to maximizing ROI. Decision intelligence’s core principles can help technical teams construct architectures that are set up to deliver actionable solutions and results for the business. There are three key things that technology teams and CTOs should consider when building formatting, and organizing data:
- Agility: Ask yourself, are you working fluidly enough to adapt to changing business needs? Fixed rules and fixed modeling are no good. The solution needs to be able to change with the business.
- Integration: You need to make sure that you’re set up to integrate more data as it becomes available. Perhaps you’re not multichannel now, but you might be in the future. Start small, while making sure you can add more data to your architecture if necessary.
- Objective: Always have your eye on the business outcome. Consider running two-week sprints with a focus on the end user. Ask yourself — “how can I make the end user’s life better in the next two weeks?” Of course, it’s not always possible to do everything in this time frame, but it forces you to think about how to achieve outcomes quickly.
Delivering on expectations in a demanding world
Technical teams’ capacity to build with outcome in mind and deliver results is critical in today’s world. Every business is now a technology business, and the expectations on CTOs and technical teams to drive commercial growth are mounting.
Decision intelligence can help teams to break down tech silos and develop the connections they need to meet these expectations. Rather than being some obscure IT project, DI adoption will be driven in partnership with other departments — from marketing to manufacturing — enabling tech teams to secure valuable internal buy-in. Additionally, the outcomes-focused approach of DI can help tech teams build very targeted projects that deliver results faster, monetizing existing investments in infrastructure and data.
With departments throughout the business relying on it, DI will put AI at the center of every business. This sets CTOs on a path aligned with business leads, not just technical teams. Technology becomes more than a supporting function, it becomes a core function of the business.
Atul Sharma founded Peak in 2015 with Richard Potter and David Leitch.
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