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3 Common Barriers to AI Adoption and How to Overcome Them| The Xperts Pakistan

Written by Azhar Farooq


There is a growing consensus about the need for businesses to embrace AI. McKinsey estimated that generative AI could add Between $2.6 and $4.4 trillion in value Annual, and of Deloitte “The State of AI in the Enterprise” report It found that 94% of executives surveyed “agree that AI will change their industry in the next five years.” The technology is here, it’s powerful, and innovators are finding new use cases for it every day.

But despite its strategic importance, many companies are struggling to make progress on their AI agendas. In fact, in the same report, Deloitte estimated that 74% of companies were not getting adequate value from their AI initiatives.

Even so, companies sitting on the sidelines can’t afford to wait any longer. As Reported by Bain & CoA “big wedge” is being driven between organizations that have plans. [for AI] And those that don’t — increase leverage and put early adopters in stronger positions.”

So, what’s stopping companies from capturing the value of AI? While there are many barriers to AI adoption, from our experience, three are the most common causes of concern. Here’s what those obstacles entail, and why leveraging automation as the ‘muscle’ that allows you to run the ‘brain’ of AI is the most effective way to realize value from technology.

Three Common Barriers to AI Adoption

1. Lack of Roadmap to Get Value from AI

Over the past few years, executives have been inundated with headlines proclaiming the transformative power of AI. Most recognize the need to implement AI in their organizations but lack a clear strategy to quickly derive tangible value from it. In a ___ A recent McKinsey surveyA significant portion of respondents (39%) stated that strategy, adoption, and scaling issues were their biggest barriers to getting value from AI.

While there is much more to creating an AI strategy and roadmap, an important first step is to identify the most valuable and transformative AI use cases to focus on. This is an area where many companies stumble: they don’t know enough about processes at a granular level to begin to quantify the potential benefits of inserting AI at critical points in those processes. Leave the

But there is a way around this roadblock. Instead of manually sifting through countless business workflows, process discovery capabilities offer a more efficient way for organizations to identify their most attractive AI opportunities.

Here are some ways you can benefit from the discovery process:

The mining process

The mining process Analyzes the digital footprint left by your organization’s software to understand your business processes from start to finish. It uses these footprints to create a detailed process map, then identifies the parts of the workflow where AI can add the most value.

Imagine a package moving from place of order to delivery. An online ordering system, inventory management software, and various other applications are on its way. Process mining can reveal that slow inventory updates are a root cause of downstream shipping delays—something that manufacturer AI and automation can solve.

Task mining

Task mining focuses on employees’ desktop activities to see where improvements can be made in a specific activity. By capturing all the variations of a task and integrating them into a comprehensive task graph, task mining can identify bottlenecks and other inefficiencies.

For example, we have used UiPath Task Mining To review different methods of filling out UiPath employee expense reports. Task mining mapped the process, highlighting redundancies and bottlenecks. Knowing where these problems were allowed us to solve them later with automation.

Communication Mining

Communication mining employs powerful AI, including large language models (LLMs), to process and understand unstructured data in emails, Slack messages, tickets, customer call transcripts, and more. This information can be used, for example, to better understand customers and their needs, visualize processes that serve them, and uncover opportunities to leverage high ROI. Business leaders can then use these insights to make informed decisions about where to deploy AI.

Unlock significant value by using British Airways. UiPath Communications MiningThrough the UiPath business automation platform, to help manage hotel inventory in its trip planning division. With hotels in over 100 countries and no standard inventory format, staff were spending a lot of time sifting through emails and manually updating availability.

Communication Mining Changed the process By automatically extracting key information such as room types, hotel names, and dates from emails, then feeding that information into email automation. This capability, along with others in the UiPath platform, saved the group countless hours of manual data entry.

Read the eBook: The Ultimate Guide to Communications Mining

These process discovery capabilities take the guesswork out of implementing AI, giving companies a specific set of use cases that will deliver immediate value. These tools can benefit all organizations, regardless of their AI experience – startups can identify low-hanging fruit, while more mature companies take their AI and automation efforts to the next level. can take up to

If you’re trying to figure out how AI can add value to your organization, or just don’t know where to start, process discovery tools can help you get tangible results quickly and efficiently. can help create a blueprint for

2. Limited AI skills and expertise

Many executives are apprehensive about an enterprise-wide rollout due to a lack of in-house AI expertise. In fact, it was the most cited barrier. IBM’s Global AI Adoption Index 2023. Bain & Co Also reported That, “over 50% of respondents highlighted ‘lack of internal expertise or knowledge’ as their most important barrier. [to AI adoption]”

Top barriers preventing enterprises from successful AI adoption IBM screenshot

Source: “IBM Global AI Adoption Index”.

Fortunately, most organizations don’t need expensive AI talent in their ranks to create value from the technology. Low-code and no-code tools enable your workforce to deploy, train, and fine-tune powerful AI models themselves, helping you bridge that skills gap and start seeing results right away. are

Among the many value-adding applications for no-code GenAI tools, Intelligent Document Processing (IDP) stands out for its popularity and impact. In industries like insurance that manage millions of unstructured documents, being able to extract useful information in less time is a huge win.

For Hub International, a leading North American insurance brokerage, codeless intelligent document processing was a game changer. They deployed fine-tuned generative AI models in-house. Understanding UiPath documentation, available through the UiPath platform, to process millions of documents every year. These included complex unstructured documents such as customer feedback forms, proof of insurance, applications and more. With Document Understanding, Hub teams can now extract useful information in less than five minutes with near-perfect accuracy. They have reported a 63% improvement in the speed at which they deliver information to their tens of thousands of brokers.

IDP’s existing capabilities have yielded remarkable results for organizations like Hub International, and enterprises can now add active learning to accelerate time to value. No-code tools that work. Active learning Accelerate the model training process benefiting technical and non-technical employees alike. Instead of the significant manual data labeling efforts that model training required, active learning focuses on the most informative and relevant data points, reducing the need for vast datasets and data science expertise. A human in the loop is still needed to query the AI ​​when it’s unsure about some instances, but it takes care of most of the work.

Finally, as UiPath AI evangelist George Roth Put it, “AI models built with active learning can be trained faster, with fewer labeled examples, and without sacrificing accuracy or performance.” Together, active learning and no-code GenAI tools allow organizations to bypass their lack of internal AI expertise and put AI to work faster.

3. Concerns about trust, privacy and security

Since when Release of ChatGPT Open to the power of AI, many corporate leaders have expressed concerns about trusting these systems with sensitive data. gave UiPath AI and Automation Trends 2024 The ebook reports that, while enterprise leaders recognize the potential value of AI, they are also “very aware of its potential risks. AI governance has been a hotbed of activity this year, and will continue into 2024.” will remain Salesforce data also showed that that nearly half of executives believe that a lack of AI risk management can negatively impact organizational trust;

Salesforce Guardrails Around AI Screenshot

Source: Sales force

For multinational companies like Intel, being able to trust GenAI models is essential. With the help of AI-powered automation from UiPath, the company de-risked its complex trade compliance process. They developed a model that predicted the correct codes for their millions of cross-border product shipments, which often require customs clearance. That model was able to classify 56,000 products with 99% accuracy, saving billions of dollars, according to Intel’s chief trade officer.

As more organizations bring AI into their operations, they want assurance that it is transparent, reliable and secure. To help address these concerns, we introduced UiPath AI Trust LayerIncluding security measures that ensure comprehensive AI management, control and data protection.

Here’s how it works:

Promoting data privacy and security

Through advanced encryption, the UiPath AI Trust Layer protects personally identifiable information (PII) both at rest and in transit. Sensitive data filtering also prevents unauthorized access and use.

Comprehensive AI management and governance

The AI ​​Trust Layer also offers robust GenAI controls, ensuring that models are developed and used in compliance with ethical standards and company policies. This allows organizations to protect their proprietary data from being used for unauthorized AI model training.

Transparent operations and user control

AI Trust will give layer leaders full transparency into their AI usage, data interactions and costs, promoting trust and operational integrity. Through dashboard audits and cost controls, leaders gain a global view of how GenAI models are performing in their organizations.

Organizations’ concerns about trusting AI models with their sensitive data are valid. To ensure you’re not compromising privacy or security, you should only use AI-powered tools that have strong guts built on the principles of trust, transparency and control.

EdChills AI Trustquote screenshot

Leverage AI-powered automation to overcome these barriers and realize value from AI today.

These barriers are significant, but they pale in comparison to the risk of delaying AI adoption. Early adopters are finding new use cases for AI and increasing their lead over the competition every day.

There’s a lot to do to prepare your organization for this new era, but there’s also plenty of value and benefits for you during your AI adoption journey. Automation can do a lot to help you move faster to realize the value of AI throughout your organization.

Did you miss the UiPath AI Summit? You’re in luck — for a limited time, you can access all recordings from the virtual event (for free!). Register once. And choose from keynotes, industry-specific breakout sessions, and product deep dives. All are available upon your demand for viewing at your convenience.

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