There’s no shortage of excitement surrounding artificial intelligence (AI), and rightfully so. It represents the swiftest technological evolution in my lifetime, with the capabilities of AI evolving almost weekly. When leveraged properly, AI can slash the time spent on manual, repetitive tasks, and allow service teams to prioritise work that adds value. However, despite the considerable buzz, it’s evident that many businesses are grappling with the practicalities of integrating AI into their daily operations.
AI barriers are stifling innovation
The journey to implementing AI is fraught with obstacles. There are valid concerns regarding security and compliance, as organisations must safeguard data and adhere to regulations. Securing board approval can be daunting, particularly when there are doubts about the return on investment. Selecting the appropriate AI tools for specific tasks is yet another hurdle, necessitating a deep understanding of the options available.
Moreover, IT systems and data must be primed for AI, a significant challenge if they are antiquated or disorganised. Internal resistance, whether from employees or entrenched practices, also poses a significant barrier. Addressing these challenges requires meticulous planning and a coherent strategy.
Understanding the purpose of AI is crucial before overcoming these barriers. Possessing groundbreaking technology is one thing, but without a clear application in mind, progress will be stunted. Knowing what you aim to achieve with AI enables you to build a compelling case for its use and address any arising issues.
What problem are you aiming to solve?
Identifying the problems you wish to solve with AI is crucial, rather than merely being swayed by the allure of new tools. In my view, the most effective way to pinpoint AI applications is to thoroughly examine your end-to-end service processes, especially those that are manual and repetitive, and consider how an AI co-pilot could be beneficial.
For instance, if managing numerous forms is part of your workflow, adopting Intelligent Document Processing to automate data entry could be a game-changer. Similarly, for handling large volumes of service emails, email triage, and sentiment analysis can significantly enhance efficiency.
Distinguishing between AI and other forms of automation
AI has become a catchall term, but it’s essential to differentiate between artificial intelligence and other automation technologies. From my perspective, AI can be categorized into three types: AI models developed by data scientists for predicting outcomes, narrow-field AI found in products like invoice data extractors, and generic generative models like ChatGPT that serve various purposes. Understanding these distinctions clarifies the unique applications of AI.
If you’re contemplating implementing RPA, Rule Engines, iPaaS, or Low Code solutions, it’s important to recognize that these do not fall under the AI umbrella and warrant a separate strategy.
Safely embracing GenAI
While caution is advisable when deploying AI in frontline operations, there are roles and departments where its use carries minimal risk. For creative professions such as Graphic Designers, Coders, or Copywriters, embracing GenAI is a low-risk endeavor. In our organization, for instance, the Content Team relies on AI for proofreading, while our Coders use it to write their first draft of code. These teams, which have established procedures for testing, quality control, and validation, find AI invaluable for accelerating routine tasks.
I recommend approaching GenAI in three steps.
First, identify all employees within the organization whose roles involve the creation, be it writing, designing, or coding.
Next, form task forces for each skill set and empower the employees to find the best AI co-pilot for their specific tasks. For example, Graphic Designers might find tools like Midjourney useful, whereas Copywriters could benefit from ChatGPT.
Finally, procure low-risk AI tools to assist individuals across the organization in their creative projects. This method facilitates the precise integration of AI co-pilots, boosting productivity and creativity while mitigating risks.
For roles associated with ‘delivery,’ ‘process,’ or ‘execution,’ it’s crucial to establish safeguards around GenAI and manage risks, which is where orchestration comes into play. It’s necessary to have a method for measuring expected outcomes and a clear policy regarding data management and the use of organizational data in training other models.
To make AI implementations a priority across the organization, businesses should empower their employees to take the lead on these projects. The key is to move beyond seeing AI as just a task for the IT department. When off-the-shelf AI models are made accessible to business users, they can directly apply these tools to their work areas, enabling a deeper understanding and ownership of the technology. By enabling those who are directly impacted by AI to lead its operationalization, businesses can create a more inclusive, transparent environment that encourages everyone to engage with and understand AI technologies.
Managing through a single source of truth
For service delivery to be a success, three essential steps need to be mapped out: 1. understanding the request (Identifying the need), 2. gathering the necessary data (Securing the required information), and 3. executing the task (Implementing the solution). Precision in the initial steps is crucial for the effective execution of the final step. The adoption of a sophisticated business orchestration tool is the foundational step needed to align operations and identify where automation can provide the most benefit.
By adopting orchestration, you can oversee, execute, and manage comprehensive processes through a unified source, seamlessly integrating various automation and AI technologies.
When effectively utilised, AI can streamline manual tasks, save time, and empower service teams to focus on more valuable work. Taking the time to reassess current processes makes it significantly easier for businesses to identify inefficiencies and determine how AI and automation can make a difference. The impetus for businesses to evaluate their AI strategy stems not only from the need to remain competitive but also to drive innovation and meet evolving customer demands.
