AI is everywhere and everyone is talking about it, but very few enterprises are currently delivering business value with AI. There is a false narrative today that many organizations are successfully adopting AI at a rapid pace when few...
AI is everywhere and everyone is talking about it, but very few enterprises are currently delivering business value with AI.
There is a false narrative today that many organizations are successfully adopting AI at a rapid pace when few are actually getting value out of the technology. In 2022, Gartner reported that on average, half (54%) of AI projects make it to the production stage. This is a slight increase from Gartner’s 2019 AI in Organizations report that determined 53% of AI projects typically don’t make it from pilot to production.
Business leaders are now skeptical of the benefits of AI because they invested time, money and other resources into onboarding AI-driven solutions, yet they have not been able to see the results they were expecting. Instead of quitting AI altogether – which most enterprises cannot afford to do – organizations should reduce investments in generalized AI and focus on adopting applied AI to achieve meaningful ROI in 2024.
The future is bright with AI – if you can get to ROI
AI will continue to play a critical role across the enterprise despite concerns about its value. Now is not the time to let up on the gas, but rather it’s a good time to course correct.
At OneStream Software, we recently surveyed 800 finance leaders around the world about their use and perceptions of AI technology in the industry, which revealed more than half (55%) of respondents agreed AI will become a core component of financial processes over the next five years. Teams must now find AI-driven solutions that can achieve significant ROI. Enter: Applied AI.
Applied AI uses pre-built functionality powered by AI to address a specific finance or business need. These solutions are faster and more efficient to deploy because they target a specific use case, generate higher ROI and accelerate time to value. Applied AI is commonly used across finance teams to accelerate the speed and accuracy of demand plans and revenue forecasts, detect anomalies in historic data and automate routine tasks. All of which are extremely beneficial in light of the ongoing accounting talent shortage.
Overall, applied AI offers valuable insights into the internal and external factors influencing business, empowering leaders to steer their organization with confidence. These insights can reduce risk, identify new business opportunities, and effectively improve overall decision-making. These purpose-built solutions stand out as powerful business tools for the modern enterprise.
Applied AI advantages: speed and accuracy
Businesses need timely and accurate insights to support confident and agile decision-making. This statement may seem obvious, but many generalized AI models cannot be deployed quickly enough to provide the insights to support decisions that need to be made today.
Unlike generalized AI, applied AI is faster to deploy and its results are often more accurate. Organizations can deploy AI-driven forecasting models in days, which gives them faster access to relevant and mission-critical insights to influence business.
On the marketing side, applied AI can provide more accurate demand forecasts by product, channel, geography and customer segment enabling more effective marketing by more precisely targeting specific market segments. This strategy maximizes the impact of campaigns and minimizes wasted resources.
In the finance department, teams can use applied AI to generate more accurate demand forecasts to provide a solid foundation for financial planning, allowing businesses to allocate budgets more effectively and make more informed investment decisions.
The AI-Driven Finance Survey also showed global finance leaders believe AI has already provided their teams with faster decision-making (49%), improved data insights (48%), improved quality of outputs (48%) and optimized resource allocation (38%). When AI is leveraged for a specific use case, it can be significantly more effective and actionable.
Clearing the course of AI challenges
While applied AI offers better ROI than generalized AI in most scenarios, there are still a few remaining challenges to be mindful of.
Business leaders have a lack of trust in AI-driven outputs because they have been burned by the lackluster results from generalized AI as mentioned earlier. Leaders may have experienced a lack of transparency in the models behind the results or failed to integrate AI into business processes due to misalignment of AI models and business values. This is where applied AI’s purpose-built functionality increases speed to value and ROI.
One solution is to provide transparency in data and outcomes derived from the applied AI model. Teams can work with technology partners to understand the model’s composition and run through scenario testing to show how they determined the most accurate model. Also, look for embedded, purpose-built AI, whether for finance or a specific business department, to enable seamless consumption and analysis.
Employee training is another obstacle when it comes to implementing AI. According to the same AI-Driven Finance Report, almost a third (32%) of finance leaders around the world named implementing AI as the top challenge, over data privacy regulations and procedures (31%). Organizations should partner with technology providers who have best practices and training materials developed to educate team members. A real partner will help address employee training needs instead of simply handing over the keys to the machine. Purpose-built Auto AI for finance or business can also support skills gaps by offering built-in workflows and drill-back capabilities so employees can have more support as they learn.
Data privacy and security may not be the top challenge for AI implementation, but it’s still high on the list. The biggest concern here is that sharing confidential data with general-purpose GenAI (Generative AI) tools such as ChatGPT could put sensitive information in the hands of competitors and the general public.
To mitigate this risk, enterprises can leverage purpose-built LLMs and GenAI tools with robust security structures that can integrate with existing systems that allow users to query “curated” data about their customers, financials, company or the software application they are using. In essence, there are ways to add guardrails without exposing highly sensitive information.
Shift business into a new gear with applied AI
The future of AI remains bright as more leaders recognize the benefits of AI for team productivity, collaboration and driving business outcomes. Many organizations will remain challenged by demonstrating ROI while also limiting non-essential spending considering the current economic landscape. Turn to applied AI and software vendors who are incorporating it into existing applications to increase productivity and solve real-world business problems.
Applied AI solutions can help enterprises achieve maximum results from their investment and gain predictive insights that help them grow profitably. Businesses will shift into a new gear with the ROI and opportunity that comes with purpose-built AI functions.
The post The ROI of Applied AI: Shifting Business Into a New Gear appeared first on Unite.AI.
