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Making the Business Case for Investing in AI and Intelligent Automation

by Bruce Orcutt, Chief Marketing Officer

Amidst the hype and rapid evolution of AI, it can be daunting for organizations to upend existing processes in favor of AI-augmented solutions, particularly when 31 percent of global businesses indicate a clear preference for maintaining ‘the old way of doing things.’ But when considering the long-term value of intelligent automation, it’s clearly worth the tribulations of implementation.

This September was Intelligent Automation Month, where ABBYY collaborated with industry leaders to educate enterprises on the potential for accelerated value and operational excellence enabled by AI-enhanced solutions. This initiative included webinars that offered exclusive firsthand insights from analysts and solution providers, exploring current challenges faced in the industry and strategies to mitigate them.

Considering various global pressures over the past year, one such challenge is the hesitance of c-suite executives to invest vital resources in innovation. As an IT manager, what is the strongest argument for investing in innovation? I joined ISG Analyst Ashwin Gaidhani for an in-depth conversation considering the business case for intelligent automation.

Artificial intelligence (AI) continues to be a major priority among IT leaders—a recent ABBYY survey revealed that 82 percent of enterprises increased their AI budgets in 2023, despite economic uncertainty and other volatile market factors. On a broader scale, McKinsey estimates that generative AI could add up to $4.4 trillion annually to the global economy.

When examining the data , the rationale for AI investment becomes obvious:

Nearly half (47 percent) of survey respondents reported an ROI of at least twice their initial investment, up from 43 percent in 2022
51 percent reported faster delivery of products and projects
62 percent described an increase in employees’ happiness

As businesses continue to find new opportunities for leveraging advanced AI platforms like large language models (LLMs), AI’s role will shift further from exclusively productivity-driven strategies and towards impacting stakeholders throughout the entire chain of operation.

Many internal automation problems have been solved, and now there is clear value in streamlining customer-facing scenarios such as onboarding and account management. No longer is automation about low-hanging fruit and small, siloed automation—it has expanded to decision making, and orchestration across multiple platforms to impact the experiences of end users.

Maximizing employee potential: Synergizing skill and talent

Today’s rapid pace of business demands a constant influx of skilled developers and technologists to support daily operations, but there isn’t always enough talent to go around. In lieu of an unending supply of top-tier talent, enterprises should strive to empower employees with tools and tasks that complement their innate abilities and existing skillsets.

If talent is defined as natural ability, and skill is cultivated through training, education, and practice, enterprises should focus on a combination of the two that drives innovation strategy. For example, a conversational specialist with strong linguistic and communication abilities could be highly suitable for prompt engineering. In process optimization, someone with extensive knowledge of larger business contexts will make more informed and effective decisions than a skilled developer that lacks this perspective.

Attempting to drive efficiency and innovation purely from a technological perspective will have limited returns when compared to including professionals that can interpret the implications of data, apply them to business situations, and make decisions with respect to timing, market factors, and other variables. Designing tools that are accessible to those beyond the IT department enables this inclusivity of broader perspectives. Low-code / no-code intelligent automation solutions with intuitive and visually oriented interfaces help democratize data and technology, enabling organizations to drive value more briskly and effectively.

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Keeping pace with regulation: Metering societal and ethical impacts of innovation

Environmental and social governance (ESG) is not only an ethical responsibility of organizations, but also a legally mandated one, particularly as it relates to AI use, data ownership, and other privacy-related issues. While the c-suite might initially see this as added liability, the increasing maturity of AI is yielding standard industry practices with regulatory compliance in mind.

Large sets of enterprises are increasingly adopting intelligent automation frameworks that reflect a commitment to preventing or reducing negative environmental and social consequences of innovation. Moreover, most regulatory policies related to AI are still in the process of being formalized and are not yet enforceable, meaning enterprises have time to ensure that their applications of AI are entirely ethical and compliant. One strategy for ensuring compliance is championing an advocate for ethical artificial intelligence within your organization. ABBYY, for example, is supported by AI Ethics Evangelist Andrew Pery , who is an expert in trustworthy AI and AI bias. Pery helps customers and clients understand how data is being shared, what it’s used for, and how ABBYY ensures transparency, fairness, and privacy. Providing a platform to professionals in this facet of artificial intelligence conveys to stakeholders that ethics and responsibility are taken seriously.

Automating with intention: Understanding your organization’s needs

Implementing intelligent automation can be a daunting task. It’s a significant investment of time, money, and other resources, and it can be ambiguous as to whether an automation initiative will succeed or not. However, IT leaders can drastically improve their odds with clear goal-setting and high visibility into their processes—after all, most automation goals fail due to unclear goals .

Every process, enterprise, and industry have their own nuances. It’s important to understand how and where you’re starting your automation journey, and why you’re doing it in the first place. Breaking it down helps to comprehend business, corporate, and technological processes, which is a key step towards scalability. IT leaders should ask: “How are we automating across a broad set of processes rather than one part of a portfolio? Then, how do we recreate it?”

Using an AI-enhanced tool like process intelligence gives enterprises the visibility they need to cultivate this thorough understanding of their operations. From there, decision makers can make informed decisions and test process improvements before implementation, reducing the risk of wasting innovation budget on a failed automation attempt.

Maximizing your investment: Best practices

Making a sustainable investment in automation requires thorough understanding of customer segments. Consider the expectations of every stakeholder and every office in an enterprise, as well as the deliverables, objectives, and other points of interest for every office. Taking input from all c-levels establishes more sensible automation, and thus supports sustainability.

Ashwin believes that enterprises have graduated from the concept of a “center of excellence,” moving instead towards a “center of value.” We should start thinking about how value is generated and delivered to our whole ecosystem, beyond just customers. Centers of excellence are purely technical, trying to deliver outcomes—but centers of value are more focused on making an impact on both internal and external entities.

A center of value should strive to establish a scalable roadmap that considers multi-dimensional integration. This leads to the connection and collaboration of various entities and attributes, reminding decision makers of competitive position, status, and maturity.

It is also imperative to use the right framework consistently across all automation solutions used by an organization, and to consider the lifespan of automation cycles; or, how long an automation cycle can operate without disruption. Metrics like these will allow automation initiatives to be further measured and optimized accordingly, ensuring you get the most out of your investment.

Communicating these priorities and establishing a clear roadmap towards operational excellence through intelligent automation is an essential step to garnering executive support for such initiatives.

Tips for driving executive buy-in

There are many factors to consider when considering or proposing an automation strategy, but IT professionals can identify certain key aspects when pursuing executive buy-in:

Show the end-to-end scope of automation and its overall impact on specific use cases
Articulate the technology elevation that impacts stakeholders’ experiences and gains a more competitive market position
Showcase domain and function-specific enhancements, such as in ESG
Provide peer references from industry verticals; both within your vertical and adjacent ones
Present a readiness roadmap that illustrates the timeline from implementation to value

For conveying the end-to-end scope of automation, focus most on how work methods and processes are being elevated beyond automation. It’s crucial to highlight how the technology transforms entire industry practices, and it is necessary to keep up with the pace of innovation. Also, focus on the solution scale across verticals, as communicating the cross-pollination effect is a tantalizing point of value to executive management. The roadmap can consider quantitative benchmarks that provide higher visibility into ROI translation, making the value appear as tangible as possible.

The speed of innovation shows no signs of slowing. If decision makers are to keep their organizations up with the pace, it’s crucial for them to understand the value of intelligent automation, and ultimately see to its implementation.

My conversation with Ashwin was an illuminating illustration of just how powerful intelligent automation can be for businesses if approached, adopted, and implemented in the right way. Our Q&A session with live attendees further delved into the nuance of company cultures and tech stack logistics. If you’re looking to gain further insight into making the business case for intelligent automation, you can watch the full webinar recording here .

Bruce Orcutt

Chief Marketing Officer at ABBYY

Bruce Orcutt is a passionate marketing and leadership executive focused on driving market growth and awareness in digital transformation and intelligent automation. As Chief Marketing Officer at ABBYY, Orcutt leads the global product strategy, go to market, launch, pricing, competitive analysis, analyst relations, communications and thought leadership, win/loss analysis, and sales enablement for the entire ABBYY intelligent automation portfolio. His goal is to help global enterprises understand the hype from reality of the latest artificial intelligence (AI) technologies and ensure they leverage purpose-built AI to achieve their transformation goals.

Orcutt was previously SVP of Product Marketing where he led the product strategy transforming ABBYY from an OCR technology vendor to ISVs to the leading provider of intelligent document processing (IDP) for global enterprises. He helped conceive, develop, and launch two new SaaS platforms for IDP and process mining with vertical focus areas in financial services, insurance, healthcare, government, and transportation and logistics. Additionally, Orcutt helped migrate the company and portfolio to subscription and recurring revenue models.

He is a thought leader with market domain expertise in AI, machine learning, process and task mining, document and data capture, digital transformation, intelligent document processing, and hyperautomation. He also has deep experience with mobile platforms, customer experience, and has a passion for customer excellence.

Connect with Bruce on LinkedIn .

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