Day 3: Navigating the Practical Challenges of Implementing AI & GenAI, Part 1

The Hidden Realities of AI & GenAI Implementations

Welcome back to the third post of the 90-day GainX AI Blueprint and blog, AI2. We’re going to start picking up the pace now!

Building on our discussion about why GenAI matters to leaders, today we’ll begin a series of posts exploring the critical challenges that come with implementing AI and GenAI, and how a different approach to implementation will help you navigate them effectively.

While we start with data today, it’s clear that many of the biggest challenges in organisational transformation are related to culture, leadership, and group behaviour – the complex elements that naturally ebb and flow within any group. These elements have a quantifiable and measurable impact on the pathways of information flow in your organisation. These pathways are influential, complex, and highly networked – and all networks, good or bad, are inherently designed to grow. This symbiotic relationship can either create costly barriers or accelerate positive change and growth.

Why does this matter? Because for the first time, when considering transformational change in the enterprise, we can accurately measure these relationships and their impacts on risk, costs and strategies - and leverage our own data and AI to facilitate profitable, safe, and competitive AI & GenAI adoption. We’ll come back to this in future articles and posts.

The Reality of AI Implementation

Implementing AI and GenAI technologies is not just about acquiring the latest tools; it involves a comprehensive strategy that integrates technology with organizational processes, culture, and goals. The journey from pilot projects to full-scale implementation is naturally fraught with obstacles – because you have a complex business in a complex world run by humans, and humans are complex.

The Issue: Your Strategy, Data Quality, and Availability

1. Generic AI Strategies: Many organizations are trying to experiment and implement change with generic ‘AI Strategies’.
2. Data Challenges: Leaders often believe they must have high-quality data to build a robust strategy. They are inundated with data that is siloed, incomplete, repetitive, or inaccurate. While high-quality structured data is essential for many use cases, there are instances where leveraging unstructured data can provide a secret advantage, especially if executed quickly.

Solution: It seems that everyone is creating or talking about their ‘AI Strategy’. When I ask about it, I often get generic responses such as: “We are going to use AI to improve customer experience and drive better data-driven decision making.” Good – but basic, and everyone else has the same strategy.

For real transformation, start with clear, precise statements about what your AI strategy is and what it is expected to do. Why does it matter to you, your employees, your customers? Why care? You already know it needs to be measured and monitored, but the challenge will be measuring something that is organic – transforming as your company does. Can you keep your finger on the true pulse?

Understanding the ‘organic’ nature of your business will be a meaningful advantage over your competitors – it is a fundamental element of deploying an uncompromisingly effective, new approach.

Here’s an example of providing more clarity for the strategy ‘driving better data-driven decision making’:

• Data Collection and Integration: Implement systems to collect data from critical sources such as customer interactions, sales, marketing campaigns, social media, and operational processes, and the relationships between your sales and customer groups. This involves integrating data from disparate systems into a centralised data warehouse or data lake.
• Advanced Analytics: Increase the utilisation of machine learning algorithms and statistical models to analyse historical data and identify patterns, trends, behaviours, and correlations. This will help forecast demand, identify new growth and revenue opportunities, optimize pricing, and personalise marketing campaigns.
• Real-Time Insights: Deploy real-time data analytics tools to management and leadership teams, enabling them to leverage predictive AI and monitor key performance indicators (KPIs) as they change, facilitating quick adjustments to strategies and operations.
• Predictive Analytics: Use AI models to predict future outcomes based on historical data. For example, past behaviours in sales, product, and marketing teams can indicate future revenue growth, while predictive maintenance in manufacturing can foresee equipment failures before they happen, reducing downtime and saving costs.
• Decision Support Systems (DSS): Implement AI-powered DSS that provide recommendations based on data analysis, assisting in strategic planning, resource allocation, and risk management.

With these clear statements defined, you can now ask: What elements of this AI & GenAI strategy are dependent on structured data and what elements can leverage unstructured data for insights? By strategically identifying these opportunities, you will gain benefits much faster, complementing the more robust and time-consuming elements of your strategy.

To get started, conduct a thorough data audit to assess the quality and availability of the data that requires higher standards for reliable outputs and invest in data cleaning and integration tools to ensure your AI systems have access to reliable data. Once the audit is complete and strengths and gaps are identified, revisit and refine your strategy. And communicate the growth in your strategy here. While doing this, you will also need to consider the capacity of your organisation to change and adopt your new future. We’ll have more on this in the posts ahead.

Our Next Post: The Complexity, Cost, and Many Faces of Organisational Fear

In our next post, we will continue exploring the challenges leaders face today and solutions to overcome them. We will also delve much further into the closet challenge every organization is facing – fear. Fear has many masks and can be very deceiving – a wolf in a rabbit costume. Or bravado, distractions, and protectionism masking what is really happening in your company.?

Fear, with its many faces, is difficult to identify and to quantify. But not impossible. Stay tuned – we’ve got a lot more to say here.

For more information on GainX, reach out any time. All of these posts will also be on our Executive Blog, AI2, which brings together Artificial Intelligence and Anthropology Intelligence to help you really understand what is happening in your organisation at the intersection of people and strategy.

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