Companies across industries recognize the necessity of integrating AI into their business operations. Eighty-seven percent of organizations believe that AI gives them a competitive edge over their rivals, and research suggests that AI could boost employee productivity by 40% by 2035.
But AI is not a magic bullet and leaders’ expected ROI often falls short. According to a recent Gartner report, 49% of leaders report that their organizations “struggle to estimate and demonstrate the value of AI.”
It is tempting to jump right in and kickstart an AI project. But without doing the due diligence on the why and how, organizations across the board are missing out on its full potential for transformation and optimization. The key is crafting a roadmap that keeps AI efforts aligned with strategic goals.
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.
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.
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.
To share just one example of the intive AI framework in action, consider how we leveraged Generative AI and large language models for a leading EdTech company to enhance their publishing operations and improve service quality to gain an edge in a competitive industry.
After a discovery phase with over 30 stakeholders and identifying over 160 findings and pain points, we prioritized a set of eight problem statements. We assessed the feasibility in a Sprint Zero for each problem statement and crafted implementation plans and functional prototypes.
Problem statements included AI-assisted content creation as well as retrieval augmented generation (RAG) use cases. Eventually, the team prioritized the AI-assisted processing of manuscripts to be implemented. The developed solution is highly aligned with the challenges and opportunities prioritized in our discovery phase and significantly reduces the time editors spend on tedious editing tasks.
AI has gone from being a nice-to-have to a necessary investment for modern businesses – and it’s those organizations that leverage an AI strategy that will see the most success. If you’re interested in maximizing the ROI in AI investments, get in touch with one of our experts today.
Sources:
Expanding AI’s Impact With Organizational Learning, MIT Sloan Management
2024 AI Business Predictions, PwC
The Pillars of a Successful AI Strategy, Gartner