Conclusion: Artificial intelligence technologies are available in various places such as robotic process automation (RPA), virtual agents and analytics. The purpose of this paper is to provide an AI maturity model in the analytics space. The proposed maturity model can be applied to any type of industry. It provides a roadmap to help improve business performance in the following areas:

  • Running the business (RTB): Provide executives with sufficient information to make informed decisions about running the business and staying competitive.
  • Growing the business (GTB): Provides information about growing the business in various geographies without changing the current services and products.
  • Transforming the business (TTB): Provides information to develop and release new products and services ahead of competitors.

Observations: The current status of AI1 from a buyer’s perspective is summarised as follows:

  • Every AI application requires its own set of tools and algorithms, which makes it difficult to determine a unified way to deploy AI in an organisation.
  • There is a need to provide AI education for executives and key staff across all levels.
  • There is a need to embrace AI experimentation instead of deploying off-the-shelf solutions that may only create limited business value.
  • There is a need for a thorough evaluation of solutions and vendors.
  • Business priorities should be defined to justify AI funds.
  • The current deployment strategies are unclear. For example, should AI be applied to a specific function or across the enterprise?
  • There is no utility approach that can be used across organisations. Every organisation has its own unique AI approach.

IBRS AI maturity model: IBRS AI maturity model provides the foundation to apply the existing AI technology where it matters to the business, and recommends guidelines to evolve into the future, whereby only limited data is available to make informed decisions (refer to maturity level 4). It is based on the following maturity levels:

  • Level 1: Adhoc – This state is characterised by the lack of AI strategy and initiatives to deploy AI.
  • Level 2: AI at the business function level – At this level, organisations have started the evaluation of some available technology related to a single business function such as HR, inventory, virtual agents.
  • Level 3: AI at the business unit level – At this level, organisations have already established a reasonable data repository such as big data environment and are capable of running their business effectively.
  • Level 4: AI at the enterprise level – At this level, organisations are effectively running, growing and transforming their businesses. They can answer the following hypothetical questions to make informed decisions. At this level, AI can be based on historic data, defined business principles or both:
    • What will be the effect on sales if the price is increased by 10 % as of the next quarter?
    • What would have happened if we had increased the price by 10 % six months ago?
  • Level 5: Human-like-intelligence – Ability to deal with new situations whereby only little data is available and business principles are vague. At this level, organisations can sustain or create a competitive advantage.

To determine the maturity level, the following criteria should be assessed:

  • Education – Assess the level of AI awareness in the organisation.
  • Application – Determine which type of AI technology has been deployed or is being considered.
  • Experimentation – Assess proof-of-concept initiatives’ effectiveness.
  • Evaluation – Assess the readiness to go-to-the-market to acquire, deploy and govern AI technology.
  • Business priorities – Assess the status of business cases to define business priorities and determine the availability of funds to deploy AI across the enterprise.
  • AI capabilities – Determine the use of AI capabilities in the analytics space.
  • Business processes – Assess the effectiveness of areas where traditional business processes were replaced by AI.
  • Governance – Determine organisations’ ability to manage AI services within business lines and IT departments.

IBRS AI maturity model is illustrated as follows:


Level 1:

Level 2:
AI applied at a business function level

Level 3:
AI applied at the business unit level

Level 4:
AI applied at the enterprise level

Level 5:
Human-like intelligence



Educating AI capabilities in specific business functions (e. g. HR)

Educating key staff on the use of AI data capabilities

Educating key staff on business principles that can be used to power AI

Educating key staff on how to create business principles applicable to business transformation


Starting market awareness

Considering AI technologies for certain functions

Defining AI models for the business unit

Defining AI models at the corporate level

Defining AI models to TTB


Defining proof-of-concept terms and reference

Proof-of-concept testing already happening

Proof of concept for the business units completed

AI solutions in place to forecast from business principles

TTB models tested


Preparing expression of interest (EOI)

RFP released to the market to acquire, deploy and govern certain AI solutions

AI contracts in place

AI facilities already deployed

TTB AI deployed

Business priorities

No business cases in place

Limited AI budget due to lack of business buy-in

Business cases are in place

Business cases are in place covering the corporate needs

TTB business cases in place

AI capabilities


Establishing AI data for specific functions

Adequate capability to run the business is in place

AI provides RTB, GTB, TTB capabilities on the basis of data and/or business principles

AI RTB, GTB and TTB capabilities in place

Business processes automation


RPA solutions being considered

Key business unit’s processes automated

Key corporate processes automated

TTB key processes in place



Procurement processes in place. AI services management after contract signature is lacking

All AI governance processes are in place

AI governance processes efficiency improved

AI governance processes optimised

Business impact

Business is not benefiting from AI potential capabilities

Partial AI capability to run the business

Adequate AI capability to RTB and GTB on the basis of available data

Adequate capability to RTB, GTB on the basis of data and/or defined business principles

Adequate AI capability to TTB

Benefits: IBRS AI maturity model can assist IT organisations in:

  • assessing the AI maturity level of an organisation and providing AI roadmaps
  • developing AI strategies and business cases.

Next Steps:

  • Maturity Level 1 organisations should launch an AI awareness program to ensure business lines benefit from AI potential capabilities.
  • Maturity Level 2 organisations should educate executives on AI and develop business cases. The rationale is to gain executives’ sponsorship to extend AI scope from a function level such as HR virtual agents to cover the whole business unit processes where AI can add value.
  • Maturity Level 3 organisations should develop a methodology to derive the business principles across critical processes. The rationale is to predict ways to grow the business in situations where critical information is missing.


  1. Preparing for the shift from digital to AI-enabled transformation”, IBRS

Wissam Raffoul

About The Advisor

Wissam Raffoul

Dr. Wissam Raffoul was an IBRS advisor between 2013 - 2021 who specialised in transforming IT groups into service organisations, with particular expertise in IT Service Management (ITSM), process optimisation, outsourcing and Cloud strategies, enterprise systems management solutions and business-centric IT strategies. Prior to joining IBRS in August 2013, he was General Manager strategic consulting in Dimension Data advising clients on applying technology to improve business performance. Prior to joining Dimension Data, he was a Vice President in Gartner/META Group and issued various research publications covering service delivery processes, centre-of-excellence models, managing outsourcing vendors, benchmarks, maturity models, IT procurement evolution and supply/demand models. In previous positions, he headed HP ITSM consulting Practice in Australia. He also acted as an infrastructure manager, reporting to the CIO at a number of large organisations in government and in the financial and petrochemical industries.