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AI Results Are Falling Short

When clients approach us for support, many have already experimented with AI & ML solutions. Some have built their own models, and others have tried to integrate foundation models into their workflows. But often, those projects have not moved past a proof-of-concept stage and do not provide the desired business value.

Some of those projects have been initiated with unrealistic expectations, fueled by the ongoing hype around Generative AI and fantastical product demos. Other times, they fall short due to high running costs and an ill-defined business case. And while the buy-in around AI in organizations continues to grow, a successful AI transformation demands cultural readiness and data literacy. In organizations where data literacy is high, machine learning algorithms can draw from the high-quality data necessary to drive the most valuable use cases.

Regardless of where you are in your AI journey, methodically evaluating your options, mapping out potential AI use cases, and assessing their feasibility and business value will help you prioritize the most valuable initiatives. Those will be the cornerstones of your organization’s AI strategy.

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Developing an AI Strategy

Over the last few years, AI has become a C-level matter. However, defining an AI strategy and adopting AI tools and ML solutions cannot simply be mandated top-down. AI initiatives need to come from the ground up. All organizational units should assess their AI readiness and how they can contribute to overall strategic goals by adopting AI tools and developing data-driven solutions.

So how should you approach your AI strategy? How does your use case contribute to the strategic goals of your organization? Does the project require training your own model? Are today’s foundation models reliable enough for your use case? What kind of effort and investment is required to implement an AI use case? How will it affect your existing processes?

A methodical approach can help answer these questions and develop a clear roadmap for implementation.

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intive’s AI Framework

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Stage 1: Discover

‍The first step is the discovery stage. In a four-week process, we dive deep into gaining a common understanding of business objectives and functional requirements, as well as the pain points we wish to address with AI.We ideate AI use cases with the help of our cross-functional teams, including AI experts, business analysts, product designers, and solution architects. We collaborate with your visionaries, decision-makers, and domain experts and build a roadmap of initiatives, from low-hanging fruit to strategic initiatives.We assess their feasibility and business value to provide a solid basis for prioritization. Up to eight initiatives will be prioritized to be validated in a proof-of-concept.

‍Stage 2: Leverage

‍Stage two is all about validating the prioritized solution proposals with proof-of-concept implementations.One approach we like to follow is doing a so-called Sprint Zero for each of the prioritized use cases. Our team will build a high-level solution architecture and MVP implementation plan, define the initial backlog, and build clickable mockups and a functional prototype. The tangible outcome of these sprints will allow informed decision-making when prioritizing the most valuable use cases for full-scale implementation.Depending on the complexity of the prioritized problem statements, we may then continue to implement a proof-of-value or start working on the first MVP directly.

‍Stage 3: Productionize

‍Once we’ve validated the feasibility and value of a solution, we move to the productionize stage. This is where we integrate the solution into new or existing infrastructure and deploy AI models into productive environments, ranging from edge devices to cloud platforms. Our focus here is on working together in cross-functional teams to build end-to-end AI applications.While there are foundational pillars to developing AI solutions, we adapt the timeline and finer details of this framework to suit each individual client. Whether you need end-to-end support from ideation, or you already have a use case in place and need help implementing it, we can support you at any stage.Read more on how to kickstart your AI project here.

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