6 Pillars of AI & Automation Driven Business Transformation
The technology landscape is constantly evolving and integrating artificial intelligence (AI) and automation has become a strategic imperative for companies looking to keep pace with change. As we discussed in our previous article, "AI Boardroom to Operation”, AI and automation have the potential to revolutionise the way organisations operate.
However, implementing AI successfully requires more than adopting the latest buzz-worthy technology. It requires a thoughtful approach that marries strategic and tactical elements to leverage AI in a way that transforms your business.
From our perspective, this requires a focus on six pivotal areas:
1. Purpose: Defining Your AI-Driven Automation Goals
Before diving into the world of AI-driven automation, a clear sense of purpose is crucial. Here are four primary areas to consider. Remember – they are not exhaustive, nor are they mutually exclusive:
Increase Top-Line Revenue: AI & automation can open doors to new revenue streams, help you target the right customers, and elevate your market positioning.
Reduce Costs & Increase EBIT: By automating tasks and optimising processes, AI significantly reduces operational costs, leading to increased Earnings Before Interest and Taxes (EBIT).
Increase Productivity and Capacity: AI-driven automation empowers your workforce to focus on high-value tasks, thereby enhancing overall productivity and capacity.
Enhance User and Customer Experience: Personalised recommendations, quicker response times, and improved customer interactions can elevate the user and customer experience.
Key: AI and automation must elevate productivity, foster customer-centricity, which in turn will positively impact profitability.
2. Team Empowerment: Maximising Human & AI Collaboration
Once you've defined your AI purpose, the next step is to determine how to achieve it. Here's how:
Enable Your Team: Equip your team with the tools and training needed to harness AI's power, by de-mystifying the genre of AI. Highlight how the company expects to benefit from new ways of working and tie these to individual job roles.
Accelerate Workflows: Identify areas within your processes where automation can streamline, optimise and accelerate your workflows across systems reducing bottlenecks and increasing efficiency. Not sure where those bottlenecks are? Process Mining will play a key role.
Infuse Intelligence into Tasks: Advances allow AI to be inserted into places where work gets done. Intelligent assistants can provide helpful insights and recommendations that extend human potential.
Re-think roles: Implementing next-gen technologies will see some current roles morph into new ones and skills gaps will need to be bridged. Stay alert to new roles such as Business Technologists to link business and tech, Data Scientists to derive insights from data, Process Specialists to optimise workflows and System Managers to oversee integration.
Key: Empower teams to go faster with AI & Automation
3. Technology Stack: Building the Foundation for AI-Driven Automation
To implement AI-driven automation successfully, you need to wrap your core technology stack with best-in-class fabric. This includes:
Orchestration with a Hyper Automation Platform: Look for a single platform that seamlessly integrates with your existing investments. Recently, AI & automation platforms such as Automation Anywhere have been re-imagined, creating a platform that can autonomously orchestrate AI-driven processes across the organisation.
Business Process Intelligence Know your as-is and your to-be processes in-depth, whilst surfacing further automation candidates with Process Mining. Toolsets such as Software AG’s ARIS take this further, with the ability to model any process and simulate variations and scenarios, ensuring permutations are stress-tested.
ML, LLM & Data Science: Machine Learning (ML), Large Language Models (LLM), and data science play pivotal roles. They enable intelligent decision-making and predictive capabilities. But not all foundational models are equal, the right model and right logic is essential for tasks and workflows throughout different parts of the business.
Computational Infrastructure, Governance, and Security: Ensure you have the infrastructure to support AI, with robust governance and security measures to protect your data and AI models. All AI & automation activity requires computational and cloud space.
Key: Implement an AI-powered layer that seamlessly spans discovery to orchestration and on to governance, while offering connectors for core systems, data and applications.
4. Data as the foundation
Data serves as the foundation for AI driven models. Having high-quality data readily available across your functions and operations positions you well to leverage AI for a competitive advantage and financial benefits.
However, if this isn’t in place at your organisation, don't be discouraged. Remember automation can reap benefits without torrents of data, automating away waste, whilst boosting the experience of customers and staff alike. Then supercharge your automations with data when it is available, enabling continuous improvement.
If you are in need of a vehicle to move data instantly between applications, we increasingly see RPA being used. Autonomous bots can be more accurate than a human and implemented far quicker than an API.
Key: It is all about data value, rather than data volume.
5. Realising Quick Value: Use Cases & Micro-Transformations
The use case approach is all about transforming, focused on time to value. Here's how it works:
Identify Use Cases: Begin by identifying small, well-defined use cases that align with your AI purpose. These are tasks or processes that can deliver quick wins when automated but keep your tank topped up. Retain a live log of automation opportunities that are re-prioritised often and continually implemented.
Implement with Agility: Instead of waiting for a grand transformation, micro-transformations implement these use cases with agility.
Verify with Business Users: Involve your business users and customers in the process to ensure that the automation aligns with their needs and enhances their experience.
Centre of Excellence: Getting started can be the easy part but keep an eye on moving into business as usual. Appoint change champions in your organisation and lean on your technology partner to incorporate best practices.
Continuous Improvement: Each micro-transformation is a starting point. Embrace a culture of continuous improvement and monitor results to assess the impact.
Key: Implement ROI focused iterative cycle of micro-transformations
6. Safety Measures: Navigating AI-Driven Automation Safely
As we delve deeper into the transformative world of AI-driven automation, it's crucial to keep an eye on potential pitfalls and challenges. Here are five watch-outs that businesses must navigate wisely:
·Cyber Security Risk: The integration of AI brings with it a heightened risk of cybersecurity threats. Prioritise robust security measures and protocols to safeguard sensitive information and look out for ISO 270001, SOC2 & SOC 3 certified solutions.
IP & Data Concerns: In the realm of AI, intellectual property (IP) and data ownership is critical. Ensure you have a clear strategy in place to protect your IP and manage data responsibly, especially avoiding public models.
Model Hallucinations: AI models, while powerful, are not infallible. They can sometimes produce unexpected results or "hallucinate" patterns that don't exist. Continuously monitor and validate AI outputs to prevent misleading decisions.
Legal Liabilities: The use of AI in decision-making processes can raise legal concerns, especially when errors or biases occur. Stay informed about relevant regulations and ensure your AI implementations comply with legal requirements.
Balancing AI & Human Expertise: Over-reliance on AI decision-making without human oversight can lead to flawed logic and poor outcomes. Maintain a balance between AI automation and human expertise to avoid costly mistakes.
Key: Maintain a balance between AI automation and human expertise
The Future of AI-Driven Automation: Marrying Intelligence with Execution
We will leave you with a thought… If AI is to be the brains, automation will be the brawn. All the intelligence AI will provide is nothing without the capability to put those smarts into action autonomously.
