The C-Suite’s Guide to Breaking AI Barriers

Artificial intelligence has dominated industry spotlights for over a year now, introducing both hype and skepticism to organizations of all varieties.

Amidst the many promises of value, cautionary tales of hallucination, and exaggerated anxieties of an AI takeover, decision makers may have a difficult time cutting through the cacophony and forming an accurately informed perspective on AI adoption.

However, now that organizations have had time to grapple with artificial intelligence and determine how it fits into the scope of their business and automation strategies, effective AI use cases have arisen. By carefully considering your businesses’ services and needs, leaders can take a strategic approach to AI-powered intelligent automation that makes effective use of recent advancements without throwing away valuable resources.

Avoid overkill by focusing on purpose-built AI

The driving force behind AI’s explosion in popularity was the advent of consumer-accessible generative AI platforms like ChatGPT. Citizen developers were granted access to robust AI capabilities that could be wielded with plain-language commands at the tip of their fingertips, causing enterprises to consider how such agile AI platforms could transform their business.

Unfortunately, generative AI comes at a cost. As the dust began to settle, many organizations realized that truly effective uses of generative AI have yet to impact business outcomes and been unprofitable, introducing a slew of previously unforeseen costs and challenges. Although price reductions could be on the horizon with OpenAI announcing a price reduction on ChatGPT (albeit an older version), it’s unclear if this will tip the scale towards increased business value. Notwithstanding the uncertainty associated with major creators’ leadership change, generative AI remains in its infancy and has yet to prove its reliability in navigating its own massive stores of data without hallucinating misinformation.

Still, facing this reality shouldn’t dissuade leaders from adopting intelligent automation with a different approach. Organizations are able to achieve better results at a more sustainable cost by using purpose-built AI tools that are designed for specific tasks. By focusing the AI tool’s scope on specific business outcomes, it can learn quickly and yield demonstrable outcomes without the cost and risk associated with generative AI.

An example of purpose-built AI is intelligent document processing platforms that utilize pre-trained document skills geared towards specific documents, such as invoices, 1099s, etc. Since the tool is designed for the singular purpose of processing, understanding, and extracting the key data contained in these documents, accuracy is near 100% out-of-the-box. Adopting this highly intentional approach to AI helps ensure that organizations aren’t taking on undue stress for diminished results in their intelligent automation strategy.

Setting goals and monitoring key outcomes

McKinsey reports 70% of automation projects fail, with failure mainly attributed to a lack of clear KPIs. Considering the rapid proliferation of unproven AI tools, diving headfirst into adoption without understanding the best processes in which to leverage AI and the true value it will deliver usually causes a convoluted tech stack and organizational frustration.

Before choosing AI tools to implement, identify the business outcomes you want to achieve and the processes that need improvement. Do you need to revitalize your finance department to expedite invoice processing times? Do you experience high drop-out rates during customer onboarding, indicating the need for streamlined onboarding processes? Identifying pain points will guide you to the appropriate AI solution that will excel in the area you need without introducing undue complication.

Monitoring progress and results of intelligent automation efforts will also keep you on track. Real-time monitoring of performance metrics like straight-through processing rates, causes of bottlenecks, time between steps, quantity and reasons for exceptions, and other indicators enable leaders to scrutinize processes and adjust as necessary to yield the most optimal results. Advanced process intelligence should include predictive capabilities like process simulation, allowing line-of-business managers to test an intelligent automation initiative before investing fully in its implementation to avoid expensive failures.

Connecting AI and business expertise for effective use

While expertise in artificial intelligence is a highly sought skill among employable talent, tech expertise alone isn’t enough to guide a valuable intelligent automation strategy. Failing to consider larger business contexts will render true business value elusive.

When forming teams, organizations must synergize key stakeholders within a Center of Excellence that includes AI experts and business leaders. Without the latter, insights generated by AI teams will be distinctly less actionable or applicable to operational contexts. Structuring AI teams to incorporate business knowledge helps your intelligent automation strategy delivers optimal value.

Approaching AI with prudent ambition

Despite the recent AI hype, arbitrarily throwing technology at business challenges is rarely the solution. However, leading organizations are becoming increasingly mature in their intelligent automation strategies and AI applications, yielding actionable outcomes and inching towards consistency and sustainability. Leaders must keep pace to stay afloat – but doing so with outcome-focused investments in purpose-built and proven AI tools will yield the best results.

Written by Ulf Persson.

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