Gen AI, The CTO and The Board - Managing Expectations!

The Scenario

Following numerous discussions with IT leaders in organizations across EMEA and North America, we've drafted an imaginary CTO report to a board. The report reflects the optimism and concerns expressed to us by this group about Generative AI and the current state of organizations' data models. It is not exhaustive, but it reflects the desire of CTOs and IT leaders to rightly manage carefully the expectations of their stakeholders. As our CEO said at the recent Legal Geek London event "... go hug your CTO, they have a tough task on their hands ..." We've used Kim as an example of a no-regrets decision in this environment (of course, other tools may well fit this bill, but Kim is best!).

A CTO Briefing Paper to The Board

With the business environment shifting so quickly, I recognize why many want us to adopt Gen AI capabilities to enhance our productivity and margins. I share your enthusiasm for leveraging the potential of Gen AI technologies to enhance competitiveness and future-proof our operations.

As Chief Technology Officer, one of my responsibilities is to ensure that we are set up for success in securely using all technologies, including Gen AI. We need to address some critical challenges as we embark on this transformational journey. We need to be candid.

Our existing technology stack is stable and complex. Our business processes are mixed, and as a result, data models are incomplete, and data quality is inconsistent. Our integrations are patchy because we have too few standard operating procedures, and we are struggling to shut down ‘shadow IT’ solutions. These issues are compounded by the significant amount of data rekeying in our operations, leading to errors and inefficiencies. We have budget constraints that we need to navigate carefully.

To ensure the successful adoption of Gen AI, we need a parallel strategy that considers both our current state while introducing and testing Gen AI tools in selected use cases. This approach will help us demonstrate the value of Gen AI and avoid making expensive mistakes backing the wrong tech while addressing our fundamental structural challenges in data management and data structures that do not currently support Gen AI capabilities.

We all know that all roads lead to the data. We all also know that while we use this phrase internally, operationally, we are loose in enforcing data accuracy. This must change.

Proposed Vision for Our Gen AI Transformation

Our vision is to gradually integrate Gen AI tools while addressing the underlying structural challenges of poor data management and inadequate data structures. We aim to become more data-centric, agile, and competitive in the Gen AI era. To achieve this vision, we will adopt a multi-faceted strategy, as outlined below.

1. Data Foundation: With the support of the board and all senior stakeholders, we will prioritize improving our data management and data quality processes across all our people and locations. This includes establishing standard business operating procedures, data cleansing, normalization, and documentation. Establishing a robust data foundation is crucial as it forms the backbone of Gen AI applications.
2. Incremental Gen AI Adoption: Rather than a big-bang approach, we will begin by implementing Gen AI tools in a few select use cases that provide the highest potential for enhancing the customer experience, avoiding the rekeying of data and generating productivity gains and cost savings. These pilot projects will help us validate the technology's effectiveness within our organization.
3. No-Regrets Decisions: Alongside Gen AI adoption, we will make immediate "no-regrets" decisions that address our current pain points. These include:
   1. Document Automation and Data Capture: Implement document automation tools to streamline data input processes, reduce errors, and improve operational efficiency. This will eliminate the need for manual data entry, saving time and reducing data rekeying issues.
   2. Data Quality Improvement: Invest in data quality initiatives that involve data cleansing, enrichment, validation, and staff training. This will ensure that the data we feed into Gen AI tools is of the highest possible quality.
   3. Integration Optimization: Focusing on optimizing our integration processes to eliminate redundancies and data silos. Confirm critical integration points within our tech stack and prioritize efforts to stabilize and enhance them. Seamless data flow between systems is crucial for Gen AI tools to function effectively.
4. Technology Agnosticism: Recognizing the evolving nature of Gen AI technologies, we will avoid putting all our resources into one particular platform or software. Instead, we will maintain flexibility and evaluate multiple options as they emerge.
5. Continuous Learning: We will invest in training and upskilling our teams to ensure they understand the importance and power of accurate data and so that they can effectively utilize Gen AI tools. A culture of continuous learning will be crucial in keeping us at the forefront of Gen AI adoption.
6. Monitoring and Evaluation: Throughout this journey, we will establish clear KPIs to measure the impact of Gen AI adoption on productivity and margins. Regular evaluations will help us make data-driven decisions and adjust our strategy accordingly.

The above approach addresses both the urgency to adopt Gen AI and our structural challenges. By focusing on data improvement, making no-regrets decisions today, and gradually introducing Gen AI in selected use cases, we can navigate the Gen AI landscape effectively while minimizing risks. Our commitment to staying technology-agnostic ensures that we remain agile and adaptable in this rapidly changing environment.

A good example of this approach is the recent purchase and deployment of Kim Document. We see Kim as a ‘no-regret’ decision in the context of Generative AI. Tactically, Kim’s Applications solve real-world data capture, data management and document automation problems today. Strategically, because Kim is deterministic and provides certainty (i.e., it does not hallucinate), and because of its powerful API integration layer, Kim is a key part of any organization's future AI tech stack.