Nonprofits should focus on incremental gains for AI success

Now is the time for nonprofits to embrace artificial intelligence (AI) .

<![CDATA[ <article> <p>Now is the time for nonprofits to embrace <a href="https://www.techradar.com/best/best-ai-tools">artificial intelligence (AI)</a>. </p><p>There is a distinct opportunity to significantly improve their operations using the latest developments in AI and automation. </p><p>We are seeing confidence grow in the third sector as organizations come to understand the potential of AI, but now we are entering a crucial phase as they look to implement it. </p><p>Given the complex technology involved, it may appear daunting to get the right strategy in place, but a disciplined approach to adoption that is focused on incremental gains - not radical transformation - will ensure a positive outcome. </p><p>This will mitigate significant disruption by avoiding large number of components being upgraded at the same time, as that increases the risk of something going wrong. </p><h2 id="a-pragmatic-approach-combined-with-thinking-big">A pragmatic approach combined with thinking big</h2><p>Getting to this point is perhaps easier said than done, as many <a href="https://www.techradar.com/best/best-nonprofit-software">nonprofits</a> rely on purpose-built applications and manual processes. It is incumbent on vendors to articulate a pragmatic approach to integrating AI with these existing systems, focusing on the idea of incremental improvements to processes and ways of working.</p><p>In parallel, nonprofits must think big about the potential for positive change. Vendors must work with their customers to help them understand that transformation entails more than just a <a href="https://www.techradar.com/pro/best-ai-chatbot-for-business">chatbot</a> or another point solution being added to the technology stack. </p><p>Done right, AI will reshape how workflows are triggered, data is interpreted and insights are generated. It will become an intelligence layer acting autonomously to interrogate data and provide teams with cognitive support to make more effective and real-time decisions.</p><p>Therefore, if nonprofits are to maximize their AI investments, they must ask themselves big questions: how does AI change the ecosystem the organization operates in? And how can it be used to improve the nonprofit’s mission? </p><p>If nonprofit leaders can think ambitiously about these answers, it will empower teams to become true knowledge workers. AI will help them uncover information to inform actions or give workers more time to focus on problem-solving, as the technology is designed to automate repetitive tasks that traditionally consume cognitive bandwidth. </p><h2 id="the-right-data-strategy-is-crucial">The right data strategy is crucial </h2><p>A key challenge for nonprofits is data governance, as the AI tools will only be effective if they have access to the right data. In a recent study we conducted called “Closing the Gap: Nonprofit Finance Software’s Crucial Role in Today’s Landscape”, 61% of <a href="https://www.techradar.com/best/best-personal-finance-software">finance</a> professionals in US nonprofits admitted they still rely on generic <a href="https://www.techradar.com/best/spreadsheet-software">spreadsheets</a> for core financial management. </p><p>The combination of specialist applications and manual processes makes it difficult to connect information across different applications and workflows. Data must be extracted, transformed and loaded so that it can be analyzed. The extraction process is also challenging because the data is often not in the same format and must be loaded in a particular format for ease of use by the finance team.</p><p>To address the data challenge, nonprofits must decide how to make the right data accessible to the AI. The data must be set up in a way that can be interrogated in plain language, and it must be based on good data inputs. Aside from reliability, the data also requires a common analysis framework so that users can understand its origins and who is accountable for it. Then, the organizations must be able to analyze the data chain and how AI is interacting with the data to make decisions. </p><p>This requires a detailed appreciation of the semantics of data. For example, the term “project” means something different in the world of nonprofits compared a professional services company or a public sector organizations . The AI must be able to interpret the word correctly to avoid hallucinations. </p><p>Equally important is the ability to monitor, evaluate and adjust data as it goes through the data supply chain to avoid inaccuracies in areas such as when it moves from the record to report phase. Nonprofits must work closely with their technology suppliers to ensure everyone in the organization, whether at the leadership, program or project level, can monitor the data for compliance with expectations. </p><h2 id="incrementalism-is-better-than-quantum-leaps">Incrementalism is better than quantum leaps</h2><p>The concept of incrementalism, rather than the “Big Bang” or quantum leap approach to transformation is the best way to address these fundamental questions. </p><p>Most successful IT implementations follow a similar incremental path. For organizations embracing this approach and wanting to ensure operational integrity while transforming IT systems, it is important to start with an evaluation of where the organization is relative to the desired end state. The planning objective is to get to the end state through a process of continuous, incremental adoption of new functionality and applications. </p><p>Most importantly, this approach allows staff to build their confidence in collaborating with AI tools. Imagine a team operating in a remote location providing medical assistance and the head of the team realizes they need to order supplies. Using incremental innovation an organization could use a combination of AI and automation to alleviate the burden of replacing missing stock. </p><p>In the first phase, an AI tool could monitor existing stocks and prompt staff when it is running low. This will give employees the chance to train the AI tool when it is urgent to replenish stocks. The AI could also use contextual information such as historical usage data and information such as weather to identify likely peak demand. As the AI becomes more autonomous, it could make recommendations to a human co-worker suggesting it completes an order form for new supplies which is reviewed by an employee. </p><p>Again, this acts as a training opportunity for AI so that in time employees will have confidence it can autonomously complete such tasks in the background. In time, the whole process will minimize interruption for employees and reduce the burden on them to complete such administrative tasks. </p><p>What this example shows is that nonprofits do not have to achieve fully autonomous systems straight away. At each stage, the AI is providing workflow improvements that empower knowledge workers by saving them time to focus on what is important to deliver their missions. </p><p>Incremental change will see nonprofits delivering value to their organizations very quickly, if they are able to address fundamental questions around their approach to data, while also thinking big about what AI can enable them to do better.</p><p><em></em><a href="https://www.techradar.com/best/best-data-visualization-tools"><em>We've featured the best data visualization tools.</em></a></p><p><em>This article was produced as part of </em><a href="https://www.techradar.com/pro/perspectives" target="_blank"><em>TechRadar Pro Perspectives</em></a><em>, our channel to feature the best and brightest minds in the technology industry today.</em></p><p><em>The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. 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