1. There’s a growing consensus about the need for businesses to embrace AI. McKinsey estimated that generative AI could add between $2.6 to $4.4 trillion in value annually, and Deloitte’s “State of AI in the Enterprise” report found that 94% of surveyed executives “agree that AI will transform their industry over the next five years.” The technology is here, it’s powerful, and innovators are finding new use cases for it every day.
2. But despite its strategic importance, many companies are struggling to make progress on their AI agendas. Indeed, in that same report, Deloitte estimated that 74% of companies weren’t capturing sufficient value from their AI initiatives.
3. Nevertheless, companies sitting on the sidelines can’t afford to wait any longer. As reported by Bain & Company, a “larger wedge” is being driven “between those organizations that have a plan [for AI] and those that don’t—amplifying advantage and placing early adopters into stronger positions.”
4. So, what’s holding companies back from capturing AI’s value? While there are plenty of barriers to AI adoption, from our experience, three tend to be the most common causes for concern. Here’s what those barriers entail, and why leveraging automation as the ‘muscle’ that allows you to operationalize the ‘brain’ of AI is the most effective approach to realize value from the technology.
1. LACK OF A ROADMAP FOR CAPTURING VALUE FROM AI
1. In 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 for quickly realizing tangible value from it. In a recent McKinsey survey, a significant portion of respondents (39%) cited that strategy, adoption, and scaling issues were their biggest roadblocks to capturing value from AI.
2. While there’s a lot that goes into building out an AI strategy and roadmap, a critical first step is to identify the most valuable and transformative AI use cases on which to focus. This is an area where many companies hit a stumbling block: they don’t know enough about processes at a granular level to begin to assess them, let alone quantify the potential benefits of inserting AI at critical junctures in those processes.
3. But there’s a way around this roadblock. Rather than manually sifting through countless business workflows, process discovery capabilities offer a more efficient way for organizations to pinpoint their most attractive AI opportunities.
HERE ARE SOME OF THE WAYS YOU CAN LEVERAGE PROCESS DISCOVERY
1. Process mining analyzes the digital footprints 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.
2. Imagine a package moving from order placement to delivery. An online ordering system, inventory management software, and various other applications are involved in its journey. Process mining might discover that sluggish inventory updates are a root cause of downstream shipping delays—something that generative AI and automation can address.
3. Task mining focuses on employees’ desktop activities to see where improvements in a specific activity can be made. By capturing all the variations of a task and merging them into a comprehensive task graph, task mining can identify bottlenecks and other inefficiencies.
4. Task Mining mapped out the process, highlighting redundancies and bottlenecks. Knowing where these issues lay allowed us to subsequently address them with automation.
5. Communications 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 to, for instance, better understand customers and their needs, look at the processes serving them, and reveal opportunities for high ROI use cases. Business leaders can then use these insights to make informed decisions about where to deploy AI.
2. LIMITED AI SKILLS AND EXPERTISE
1. A lack of in-house AI expertise has many executives apprehensive about an enterprise-wide rollout. In fact, it was the most cited barrier in IBM’s Global AI Adoption Index 2023. Bain & Company also reported that, “Over 50% of respondents highlight a ‘lack of internal expertise or knowledge’ as their most significant impediment [to AI adoption].”
2. Fortunately, most organizations don’t need costly AI talent within their ranks to generate value from the technology. Low- and no-code tools enable your workforce to use, train, and fine-tune powerful AI models themselves, helping you bridge this skills gap and start seeing results right away.
3. Of the many value-adding applications for no-code GenAI tools, intelligent document processing (IDP) stands out due to 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.
4. IDP’s existing capabilities have led to remarkable results for organizations like Hub International, and enterprises can now add active learning to further accelerate time to value. No-code tools that employ active learning speed up the model training process, benefiting technical and non-technical employees alike. Rather than the significant manual data-labeling effort that model training used to require, 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 required for the AI to query when it’s not sure about certain examples, but it takes care of most of the work.
5. Together, active learning and no-code GenAI tools allow organizations to bypass their lack of internal AI expertise and start operationalizing AI quickly.
3. CONCERNS AROUND TRUST, PRIVACY, AND SECURITY
1. Ever since the release of ChatGPT opened their eyes to the power of AI, many corporate leaders have expressed concerns around trusting these systems with sensitive data. AI governance has been a hotbed of activity this year, and that’s going to continue in 2024.” Salesforce data also showed that around half of executives believe that a lack of AI risk management can negatively impact organizational trust.
2. The AI Trust Layer will give leaders full transparency into their AI usage, data interactions, and cost, promoting trust and operational integrity. Through dashboard audits and cost controls, leaders gain a universal view of how GenAI models are working in their organizations.
3. Organizations’ concerns about trusting AI models with their sensitive data are valid. To be sure you aren’t putting privacy or security at risk, you should only use AI-equipped tools that have robust guardrails built on the principles of trust, transparency, and control.
