Three typical obstacles to the use of AI and solutions
Introduction#
There is increasing agreement that corporations must use AI. In addition, Deloitte's "State of AI in the Enterprise" research revealed that 94% of questioned CEOs "agree that AI will transform their industry over the next five years." McKinsey predicted that generative AI could add between $2.6 and $4.4 trillion in value annually. The technology is here, it's strong, and every day, creative types discover new applications for it.
However, despite AI's strategic significance, many businesses are finding it difficult to advance their AI initiatives. In fact, Deloitte calculated in that same survey that 74% of businesses weren't getting enough value out of their AI investments.
What, then, is preventing businesses from realizing the potential of AI? Although there are many obstacles to the widespread use of AI, these 3 are typically the most prevalent reasons to worry, in our experience. These are the obstacles to overcome, and the best way to get the most out of the technology is to use automation as the "muscle" that lets you operationalize the "brain" of artificial intelligence.
1. Absence of a strategy for maximizing AI's potential#
Executives have seen countless headlines in recent years praising AI's revolutionary potential. The majority acknowledge that their companies must use AI, but they don't have a clear plan in place for rapidly obtaining measurable benefits from it. In a recent McKinsey poll, a sizable fraction of participants (39%) indicated that the major obstacles to realizing the benefits of AI were related to strategy, adoption, and scalability challenges.
Selecting the most beneficial and revolutionary AI use cases to concentrate on is an essential initial step, even though developing an AI strategy and roadmap involves many other factors as well. Many businesses run into trouble in this area because they don't have enough detailed knowledge of the processes to even begin to evaluate them, much less calculate the possible advantages of integrating AI at pivotal points in the processes.
Here are a few strategies for utilizing process discovery:
Process Mining
Process Mining examines the digital traces that your company's software creates in order to comprehend your business processes from beginning to end. It then determines which stages of the workflow AI can most effectively contribute to by using these footprints to build a comprehensive process map.
Consider a package being delivered after an order has been placed. Its journey involves a number of apps, including inventory management software and an online ordering system. Process mining could reveal that downstream shipping delays are primarily caused by slow inventory updates, a problem that generative AI and automation can solve.
Task Mining
Task Mining looks at what workers do on their desktops to identify areas where a certain activity might be improved. Task mining is the process of identifying bottlenecks and other inefficiencies by collecting all the variations of a task and combining them into an extensive task graph.
For example, we have examined the many methods that UiPath employees complete expenditure reports using UiPath Task Mining. Redundancies and bottlenecks were highlighted in the process map created by Task Mining. We were able to use automation to handle these problems after determining their location.
Communications Mining
Large language models (LLMs), one type of potent AI used in Communications Mining, are used to process and comprehend unstructured data found in a variety of sources, including emails, Slack chats, tickets, customer call transcripts, and more. For example, this data can be utilized to examine customer operations, better understand customers and their demands, and identify potential for high-return use cases. Then, business executives may utilize these insights to decide where to implement AI with confidence.
With the help of these process discovery capabilities, businesses can use AI with confidence, since they provide a targeted set of use cases that yield quick returns. All enterprises, regardless of level of AI knowledge, can benefit from these tools; more experienced businesses can use them to further their automation and AI initiatives, while newer ones can use them to find low-hanging fruit.
2. Inadequate knowledge and experience with AI#
A large number of executives are concerned about an enterprise-wide implementation due to a lack of in-house AI competence. Indeed, IBM's Global AI Adoption Index 2023 listed it as the most often mentioned obstacle. Additionally, according to a survey by Bain & Company, more than half of the participants cited a "lack of internal expertise or knowledge" as the biggest obstacle to the adoption of artificial intelligence.
Thankfully, most businesses can reap the benefits of AI without investing in expensive AI experts. Your staff can use, train, and fine-tune strong AI models themselves with the help of low- and no-code solutions, which will help you close the skills gap and get results straight away.
The ubiquity and effect of intelligent document processing (IDP) make it stand out among the many value-adding applications for no-code GenAI solutions. Efficiently extracting valuable information from millions of unstructured documents is a significant advantage in businesses such as insurance.
3. Issues with security, privacy, and trust#
Many business executives have voiced reservations about entrusting these systems with sensitive data ever since ChatGPT's debut opened their eyes to the potential of AI. This year, AI governance has been a hive of activity, and in 2024, that will not change. According to Salesforce statistics, almost 50% of executives think that an absence of AI risk management can have a detrimental effect on corporate trust.
Fostering security and privacy for data
The UiPath AI Trust Layer protects personally identifiable information (PII) while it's in transit and at rest by using cutting-edge encryption. Unauthorized access and usage are also prevented via sensitive data screening.
Comprehensive governance and control of AI
Strong GenAI controls are another feature of the AI Trust Layer that guarantees models are created and utilized in accordance with business guidelines and moral principles. This enables businesses to prevent unlawful AI model training from using their confidential data.
Open processes and user authority
In order to foster trust and operational integrity, the AI Trust Layer will provide leaders with complete transparency on their AI usage, data exchanges, and costs. Leaders obtain a comprehensive understanding of how GenAI models are operating within their firms through dashboard audits and expense controls.
It is reasonable for organizations to be wary of entrusting AI models with their confidential information. You should only employ AI-enabled solutions with strong safeguards based on the concepts of trust, transparency, and control to ensure that you aren't jeopardizing privacy or security.
Conclusion#
While these obstacles are substantial, the danger of postponing the deployment of AI is even greater. Every day, early adopters are increasing their advantage over competitors by discovering new applications for AI.
While there is much work to be done in order to get your company ready for this new era, there are also many benefits and benefits to be gained from adopting AI. Automation can greatly assist you in making rapid progress toward realizing the benefits of AI throughout your company.