Leading organisations are becoming increasingly mature in their artificial intelligence (AI) and intelligent automation strategies, and many are starting to see the benefits. Over the years, the evolution of AI has enabled organisations to be more proactive in optimising processes and improving decision-making. It is no longer difficult to find automation opportunities and monitor processes, all of which enhance overall productivity.
While considerable interest is trending high due to the many positive impacts for businesses, AI adoption can face some obstacles, ranging from high costs to security concerns and organizational cultures resistant to innovation. Consequently, ISG research has noted that leaders are urging providers to investigate potential use cases and strategies within current services to prove tangible business value.
For those businesses that have not yet fully embraced the technology, there are three essential factors to consider when developing an AI and automation strategy.
1. Understand where your business needs AI and intelligent automation
The wide skepticism of enterprise-grade use of generative AI tools makes diving headfirst into adopting a generalist tool risky. This approach can lead to a convoluted tech stack, and technical debt, and cause frustration and confusion among employees. To ensure a successful AI strategy, it’s crucial for organizations to fully understand the process and the value it can deliver.
Before selecting AI tools, identify the specific areas that need improvement and the expected outcomes. This involves pinpointing the organization’s major pain points using process discovery and determining the best automation opportunities and necessary solutions so businesses can adapt to evolving customer and employee needs.
Advanced process intelligence tools develop a digital twin of your processes and provide valuable insights that include predictive capabilities and process simulation. This allows businesses to test digital transformation initiatives before full implementation, minimizing costly failures.
Cost considerations are important. Many AI tools can be costly. When developing an AI strategy, consider the business use case and the anticipated ROI to guide you to the appropriate solution. More often you will find that there are existing, proven technologies that can do the same function better than generative AI solutions.
After selecting, testing, and implementing AI tools, ensure they serve their intended purpose. Monitor progress, analyse bottlenecks, and make the necessary adjustments to optimize outcomes for both businesses and customers.
2. Zero in the strategy to purpose-built AI solutions
Putting generic AI tools such as ChatGPT at the center of a business strategy – while useful in some contexts – has its downsides. These large language models (LLM) tools can create complexity in achieving the output you want and waste energy. There is a risk of compliance issues, due to the lack of transparency about what is within the AI model or how it was trained. Also, there is potential for hallucinations. We’ve all heard the stories of generative AI making up answers to queries that are not real.
Companies looking to improve their AI and automation strategies need to minimize risks and costs. This is where purpose-built AI comes in. Organizations need small language models specifically built to understand, interpret, and act upon complex data with incredible accuracy and efficiency.
Purposeful AI is designed and developed for specific uses, narrowing the context and task to the core of what the business needs to achieve the required results. Specialized AI tools, such as intelligent
document processing (IDP), can be trained over time to read and understand a specific type of document, regardless of format, layout, size, or language.
Document-heavy functions, such as accounts payable, transportation and logistics, and customer onboarding particularly benefit from purpose-built AI like IDP. The technology enables easy deployment, and efficient straight-through processing and can swiftly and accurately extract essential insights from documents whilst saving time through minimal human intervention.
Purpose-built AI can increase transparency, allowing business leaders to effectively and accurately describe how clients’ data is put to work. This will ultimately help reduce the risk of noncompliance with regulations that are increasingly coming in, such as the EU’s AI Act. Organizations will also be less likely to use and store customer data in ways that weren’t disclosed with a clear purpose.
Businesses that adopt this highly intentional approach to their AI and intelligent automation initiative will almost immediately feel the benefits: increased operational efficiency and cost savings.
3. Ensure sustainability is a top consideration
By adopting a purposeful approach to the deployment of AI technologies, businesses can align their AI and intelligent automation with sustainability goals, ethical considerations, and social responsibility. This ensures that the integration of AI not only enhances operational efficiency but also contributes positively to broader ESG objectives.
A key problem with generative AI has traditionally revolved around the vast repositories of data that need to be sifted through to extract value, which uses a significant amount of energy. This could pose environmental challenges, contributing significantly to an organization’s carbon footprint.
Furthermore, it’s crucial to address issues such as bias, transparency, and accountability to ensure that AI systems are fair and trustworthy for all. For example, a generative AI tool could pose the threat of breaches of privacy, or unauthorized access and misuse of personal and sensitive data. Hence, it is imperative to address these concerns proactively and ensure that AI is developed and deployed responsibly. ABBYY, for example, has clearly stated its trustworthy AI policy. Shifting towards more targeted, purpose-built AI that is specialised for specific tasks can help alleviate some of these issues.
With legislation in the EU expected to have a sweeping impact on companies globally throughout the next year, innovation leaders should take proactive measures to be compliant. Companies should seek the guidance of non-profit organizations such as ForHumanity, whose aim is to help organizations meet the requirements of regulators and uphold the laws designed to protect consumers. They offer audit services for Artificial Intelligence, Algorithmic, and Autonomous (AAA) systems related to financial services Model Risk Management (SR11-7), Fair Housing, and Fair Credit that will give compliance officers and the C-suite peace of mind.
AI and automation are continually evolving as new challenges emerge with economic, technological, and societal changes. However, arbitrarily throwing technology at business challenges is rarely the solution. Innovation leaders must discern which technologies are essential and prioritize adoption to achieve business goals. Outcome-focused investments in purpose-built and proven AI tools will yield the best results and return greater value.
