How decision intelligence can put AI at the center of every business

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This article was contributed by Atul Sharma, co-founder and CTO of Peak.

Data collection is skyrocketing. The amount of data created, consumed and stored worldwide will increase by more than 50% between now and 2025. Companies understand that evaluating their data more effectively delivers a competitive advantage and that it will be artificial intelligence, not business intelligence, that will unlock this potential, but there is a striking gap between the scale of AI investments and the tangible returns delivered.

Fortune 500 companies spend an average of $75 million on AI talent. But only 26% of AI initiatives go into production on a large scale 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 adopt DI within the next two years.

The impact of DI on technical teams

While AI can be a somewhat vague concept, decision intelligence is more concrete. That’s because DI is results-oriented; a DI solution is built to meet a business objective. As such, it can help CTOs and technical teams execute data projects that deliver measurable results for their business.

Today, commercial AI strategies are plagued by numerous problems that limit their effectiveness. One is the fact that data scientists are trained to think ‘bottom-up’ – to understand what data they have available and come up with a solution from there. More often than not, this results in lengthy engineering projects addressing data issues, rather than commercial needs.

By turning this approach on its head and building it with a result in mind, decision intelligence solves many of the pain points that keep companies from quantifying the value of their AI investment. By working backwards from an objective, engineering teams can build the solutions they need and extract value from AI faster. By embedding these solutions into the decision-making processes that drive every aspect of an organization, DI can deliver commercial benefits across the business.

An intelligence trained on marketing data and intended to optimize the marketing funnel will do just that. An intelligence trained on an organizational dataset and designed to holistically optimize operations is not so limited.

Bringing a DI mindset to data architecture

The way we design data architecture is key to maximizing ROI. The core principles of Decision Intelligence can help technical teams build architectures built to deliver actionable solutions and results for the business. There are three key things technology teams and CTOs need to consider when creating formatting and organizing data:

Maneuverability: Ask yourself, are you fluent enough to adapt to changing business needs? Fixed rules and fixed modeling are not good. The solution must be able to change with the business. integration: You need to make sure you’re set up to integrate more data as it becomes available. You may not be multichannel now, but you may be in the future. Start small and make sure you can add more data to your architecture as needed. Objectively: Always keep an eye on the operating result. Consider running two-week sprints with a focus on the end user. Ask yourself, “How can I improve the end user’s life 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 get results quickly.

Living up to expectations in a demanding world

The ability of engineering teams to build with purpose in mind and deliver results is critical in today’s world. Every company is now a technology company, and the expectations of CTOs and technical teams to drive commercial growth are growing.

Decision intelligence can help teams break technical silos and develop the connections they need to meet these expectations. Rather than being an obscure IT project, DI adoption will be fostered in partnership with other departments — from marketing to manufacturing — allowing engineering teams to secure valuable internal buy-in. In addition, DI’s results-driven approach can help technical teams build highly focused projects that deliver results faster and monetize existing infrastructure and data investments.

With departments across the company relying on it, DI will put AI at the center of any business. This puts CTOs on a path aligned with business leads, not just technical teams. Technology is becoming more than a support function, it is becoming a core function of the business.

Atul Sharma founded Peak in 2015 with Richard Potter and David Leitch.

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