How to get AI sorted…….

How do you become AI-Ready, Data-Driven and Boost Productivity

AI, or Artificial Inteliigence, are tools which emulate Cognitive Human Behaviour.

In November 2022, OpenAI launched ChatGPT which brought AI to the front of mainstream thinking.

There are many strands of AI.

  • Some, like Robotics and Automation have been in general use for many years.
  • Large Language Models, and Generative AI have progressed quickly in part due to the availability of large Data Sets and increased Computer Processing Power.

There is a grave danger of businesses adding AI tools piecemeal

The best place to start your AI journey is by modelling your business and understanding what your business needs.

The chief danger of AI is many businesses are being seduced by technologies and terms with a desire to be seen to be introducing AI and therefore bolster their digital credentials.

The reality is that AI will best serve a business when it is being implemented as part of the strategic objectives and in line with clearly identified business needs.

The most simple test is to look at your Business Strategy.

Are all your KPIs measured and reported on using metrics derived from your data-sets without any human curation?

  • If the answer is “Yes, all our KPIs are measured and reported on using our own data sets without any human curation. then the chances are, you are data-driven, or, at the very least, well on the way.
  • If the answer is “No, not all our KPIs are measured and reported on using our own data sets without any human curation“, then you are not Data-Driven.

All your standard reports, both financial and others, need to be driven from your data without any human curation.

There are other guides including the detail within your Master Data Structure and your Business Glossary, and whether your reporting is available 24/7 and the percentage of reports that can only be run by designated individuals and or departments.

Lastly, a truly data-driven organization will not grapple with Data Quality Issues. It is likely that data quality is a regular agenda item for your Executive Board, with metrics routinely evaluating your data quality performance over specific periods.

The most straightforward way to drive productivity is to identify what processes can be automated.

Humans entering Data and manually driving business systems and Processes are inefficient. Computers work faster and will make fewer mistakes.

Not all processes will be ready for Automation. Identifying which processes are ready and the best order to address automation will deliver the most productivity gains.

The best way to identify potential areas for gain is by building a Process Model.

If you aim to become AI-ready, foster a data-driven culture, and enhance productivity, consider taking the following steps to initiate the process.

  • Business Glossary; you cannot be data-driven and AI-Ready without the discipline of a Glossary which defines every metric, goal, and KPI, down to the data source. Ideally linked to your Data Calatogue.
  • Business Process Model: which defines how your business actually works and includes all your People, Processes, Systems and Technologies as well as Risks, Controls and Mitigation and a full RACI matrix for every object, process and risk across the Business Landscape.
  • Business Strategy: everything is tracked back to the business strategy ensuring every “tool” is delivering value against one or more strategic objectives.
  • AI-Strategy: Having an AI-Strategy whereby all AI intiatives can be maintained within a Cost, Benefit and Risk framework.
  • Business Leadership: AI implementation with be less effective if it is not controlled within a Business needs led lens.. 

Foundations

Fitting AI into the business for the sake of it is a recipe for costly mistakes and delivery failure.

Business Needs

Identify and Communicate Business needs

Data Quality

Culture of Data Quality and Digital Awareness.

Systems

Integrated Business Systems

Reporting

Democratised Reporting Systems

Governance

Robust Data Governance Structures

Working with Intelidat

At Intelidat we supply expertise to deliver excellence.

Our experience is based on delivering Information Value.

Our focus is on data , and deriving data value.

AI, Data-driven and productivity come from knowing your data, understanding your culture and identifying opportunities from your dynamic process model.

Leadership

Providing additional Bandwidth to Business Leaders to help get AI sorted, and drive a culture of Data Quality and Digital Awareness.

Fractional, Interim, Mentor to existing teams, or even as a consultant

Business Architecture & Glossary

Help you build a Digital Twin to understand business needs and priorities, and make sure you are defining all terms to aid communications and delivery.

Digital Transformation

The secret of success lies in implementation

Digital Mentor to your Board

How to drive digital literacy and deliver the Information needs that underpin a Data Driven organisation

How do you lay your Foundations?

Drivers of Excellence

  • Leadership & culture
  • Business Architecture
  • Business Knowledge Hub
  • Master Data Structure
  • Integrated Business Systems
  • Reporting
  • Data Governance

Do you have an Information Strategy?

  • Where are you now?
  • Where do you need to get to?
  • What are your Priorities
  • How will you get there?

Key steps to getting started.

Leadership

  • Vision & Strategy
  • Digital Literacy
  • Drive Organisational Change
  • Accountable

for Information performance, data quality and integrated business systems.

Business Digital Twin

  • Identify business needs
  • Digital Priorities
  • Plans
  • Dependencies
  • Risk
  • Automation Opportunities

Business Glossary

  • Computers only work on strict definitions.
  • How can you measure a metric like No of Customers, if you have not defined a Customer, where and how it is tracked and measured?

A glossary must be accurate and kept up to date.

Master Data Structure (MDS)

Similar to how Finance relies on a Chart of Accounts for consistency, businesses should establish and adhere to a Master Data Structure.

All technologies, tools, and systems within the organization must align with and utilize this standardized structure.

Failure to do so can result in issues with data quality, hindering the ability to make informed, data-driven decisions or successfully implement AI technology.

Integrated Business Systems

The value lies in your data so all systems must use data properly.

Data should be captured once and used many times.

  • Efficiency & streamline
  • Improved decision making
  • Customer Experience
  • Cost Savings
  • Data accuracy & consistency
  • Supply chain management
  • Compliance & Risk management
  • Productivity & Scalability

Reporting

Democratic reporting systems where all reports are based on a single data source that agrees with the Master Data Structure.

All reports will have full data provenance so there are no disagreements about what data source to use.

All reports can be run 24/7 without ANY human curation.

All leaders are confident that their decisions are based on the latest business data and information.

Data Governance

Robust Data Governance Structures will help manage data quality as well as Cybersecurity and business Intelligence.

  • Data quality assurance
  • Compliance and Risk Management
  • Responsibility & Accountability for data quality.
  • Consistency and Standardization
  • Technology & data integration.

how can any business in the 21st Century compete without a Digital Twin to focus and underpin, Operational Decision Making?