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Pragmatic AI: The Key to Building Business Resiliency During Economic Uncertainty

As part of Solutions Review’s Contributed Content Seriesa collection of contributed articles written by our enterprise tech thought leader communityChloe Stephenson, the Senior Product Strategy Manager at Certinia, explains why a pragmatic AI strategy might be the key to maintaining business resiliency amidst economic uncertainties.

The global economy is currently facing many challenges, including rumblings of a potential recession. In the United States, the Federal Reserve has been raising interest rates to combat inflation, but this is also hurting economic growth. Meanwhile, Germany is on track to enter its second recession this year, and the Institute for Fiscal Studies predicts that Britain will see a “moderate” recession in 2024. As a result, many businesses in the United States and those with global operations are looking to profitability rather than growth to drive earnings. They are looking for innovative ways to mitigate the risks of an economic downturn 

Even during the most challenging economic times, technology investments can be crucial for businesses that want to stay ahead of the curve and emerge from the downturn stronger than ever. Companies are more likely to experience dramatic gains or losses during economic downturns than in normal years because of strategic investments or inaction. Investing in AI and automation technology could prove fruitful through efficiency-related gains, especially if done pragmatically targeting your business’s most impactful use cases.  

Current State of the AI Investment Landscape 

Integrating AI into core operations—like service delivery and resource planning—can significantly transform organizations’ operations. These efficiency and productivity improvements can result in significant cost savings. AI can also help companies better understand customers’ needs and preferences to improve sales and customer satisfaction KPIs. AI has growing adoption rates, but there is still room for increased adoption and usage in the enterprise. According to McKinsey’s State of AI report, 7 percent of business and professional services respondents use AI regularly for work, while 41 percent have tried it at least once. 

Despite this business potential, AI can also be risky, especially during economic uncertainty. The cost of developing and implementing AI solutions can be high, and there is no guarantee the investment will pay off. PWC’s most recent Pulse Survey ranked “investing in new technologies” as the top strategic business change for the next 12 to 18 months, with 46 percent of respondents saying they’ll invest in generative AI specifically. Interestingly, the same report found that 88 percent of executives struggle to capture value from their technology investments.

Common challenges include a lack of understanding of AI technology, a shortage of skilled AI talent, and a lack of integration between AI systems and existing business processes. The risk of AI investment is compounded by the fact that many businesses are not well-equipped to adopt AI effectively. With a healthy dose of pragmatism and proactive skepticism (no one wants to fall foul of AI washing), you can mitigate that risk and focus on emerging AI use cases that help your services business gain a competitive edge in the face of economic uncertainty. 

The Pragmatic Approach to AI 

What is a “Pragmatic AI approach”? Pragmatic AI targets real-world, tangible problems businesses face today rather than aspirational or flashy use cases. By leveraging curated datasets, AI models, and expert support, organizations can unlock hidden insights, predict trends, and make informed decisions that drive growth and streamline operations. It is a user-friendly approach designed for success, with measurable outcomes that help organizations achieve accuracy, efficiency, and confidence to act with certainty and allow businesses to calculate their return on investment. 

Businesses can use this approach to implement game-changing technologies while minimizing risk concerns and have much-needed savings in times of economic uncertainty and beyond. Some service-specific examples of unlocking efficiency and cost-savings for areas include enhanced resource allocation, improved service margins and efficiency, and accurate cash flow forecasting. 

To ensure a successful pragmatic approach to AI adoption, companies should take the following steps: 

1) Ensure a “Clean” Data and Governance Plan 

Before jumping in, teams must take a temperature check on their current assets. A clear signal a business might not be AI-ready is if it lacks “clean” data and a governance plan. Pragmatic AI is only as good as the data it is trained on. Therefore, businesses must have clean, high-quality data before implementing pragmatic AI solutions. This means cleaning up data for errors and inconsistencies and ensuring it is properly formatted and labeled. A governance plan is also imperative to manage AI data. This plan should include policies for data collection, storage, access, and use. It is also essential to have a process for monitoring and updating AI models as the data changes. 

2) Take Inventory of the Business Problems 

In a pragmatic approach, the key to success is to focus on solving current business problems. Businesses should start by identifying the most important challenges they face and then look for solutions that help solve them. For example, an embedded services organization in a software development company, like any, wants to ensure margin and utilization targets are met when assigning resources to work. However, it also values the career aspirations of its employees, the type of work they enjoy, and their working style to build great implementation teams of driven, satisfied individuals. The AI model analyzes historical data on resource allocation and metrics associated with delivery success, then matches that with forward-looking data to find the best consultant-project match. 

3) Look for Integration Opportunities in Existing Workflows 

To ensure ease of adoption and effective use among teams, pragmatic AI solutions should be user-friendly and straightforward to integrate into existing workflows. This means that the solution should be able to seamlessly connect with the company’s existing systems and tools, such as their enterprise resource planning (ERP) system. This allows employees to access and use pragmatic AI solutions without switching between different systems, which can be time-consuming and disruptive. These solutions should also be designed to be easy to use and understand, even for employees who are not technical experts. This will help ensure employees can quickly learn how to use pragmatic AI solutions and benefit from them as soon as possible. 

Preparation for Future Economic Uncertainty 

With economic uncertainty looming, it is more important than ever for businesses to build resilience through investment in innovative ways to mitigate risks and improve profitability. Pragmatic AI is a low-risk approach to AI adoption that can help companies achieve these goals by improving efficiency, reducing costs, and increasing revenue. By taking steps toward implementing this pragmatic approach, businesses can prepare and be ready for success despite the economic challenges the future may bring.

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