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Episode 1: The intereach AI Use Case

In this episode we will explore the definition of Artificial Intelligence (AI) in a business environment, clarify terms in the AI landscape and set out the most likely ways intereach will start to use AI in the short to medium term. To provide clarity this Episode will use the Signal vs. Noise structure and also include a list of AI prompts for further exploration.

Signal

For established organisations achieving ROI from AI adoption is a non trivial task and based on recent market analysis should not be underestimated. For intereach the clear AI adoption focus is buying rather than building AI capability. A key barrier to ROI through AI investment is that... "Most GenAI systems do not retain feedback, adapt to context, or improve over time." - State of AI in Business 2025 For intereach seeking to achieve back office cost reduction a longer term view on AI implementation is required (see AI Prompt below). "Companies should approach making a financial justification for exploring gen AI initiatives by focusing on cost savings, revenue growth, competitive advantage and risk mitigation." - The 5 biggest AI adoption challenges for 2025 IBM intereach will benefit from the adoption of highly adaptive Agentic AI solutions but must go through an initial journey of business process & data refinement and automation adoption first.

Noise

As a new technology area there is significant AI hype with the short term benefits oversold relative to the costs required to achieve them. "92% of companies plan to invest more in gen AI over the next 3 years. 1% believe their investments have reached maturity" - Superagency in the workplace McKinsey Jan 2025 Another natural source of noise (with all emerging technology) is acronym overload, AI is referred to in urgent terms by stakeholders lacking a clear understanding of the technology foundations. Being clear about the 3 implementation levels helps to orient AI related conversations (see AI Prompt below): Level 1: The AI engine being used e.g. Neural Network. Level 2: The teaching approach used to train the engine e.g. Machine Learning. Level 3: The end user's solution e.g. Copilot, other Agentic AI solutions.

Watch

Shadow AI Economy "While only 40% of companies say they purchased an official LLM subscription, workers from over 90% of companies we surveyed reported regular use of personal AI tools for work tasks" - State of AI in Business 2025 In more technology centric organisations employees are using personal AI accounts to automate their work (beyond using ChatGPT for writing and research). This process is inevitable at intereach and should be viewed as an opportunity to organically transform the organisation if it can be fostered in a transparent and safe fashion.

Act

For intereach the strategic focus on AI adoption should be on longer term efficiency targets with a clear ROI (cost out focused). Essential foundation activities should be the focus now (see AI Prompt below). 1. Map repetitive tasks particularly those that are organisation wide and/or high volume. 2. Highlight processes where manual data intervention and transcription is required. 3. Model the costs and timings of these processes as input into the business case for change.

AI Prompts for further research...

"What are the key dependencies for deploying an AI solution offering built-in continuous learning capabilities that can be configured to optimise back office operations from a staff and customer perspective?"
"Can you explain the difference between AI model types (like neural networks), implementation approaches (like machine learning), and business AI categories (like generative AI) in simple terms with examples?"
"What are the essential first steps and foundational activities that organizations should prioritize to successfully implement ROI-generating adaptive AI models in back-office operations?"