How Organisations Are Accelerating Their AI Plans with AI Consultancy Services

Recent world events have shown us the damaging effects that can result from a lack of data due diligence in Autonomous Information Systems (A/IS) Programme development, but how should digitised organisation’s go about leveraging the benefits of A/IS?  Cyber Smart Consulting’s Lead Data and AIS Consultant, Shirley O’Sullivan explains:

Digitalisation drives the need for organisations to invest in Autonomous and Intelligent Systems (A/IS). However, while technology executives recognise the need to accelerate their pace of AI adoption, a lot are dissatisfied with their product group’s current approach to AI. Whether this is due to scale, investment, inertia, or lack of specialist talent, businesses are currently struggling to embrace AI technology. Often this is down to their AI strategy, or rather a lack of one. Having an AI strategy is crucial to winning buy-in from executives, leading to investment, resources, and implementation. Change Organisations are looking for ways to overcome the inertia and allow them to start the journey using successful, proven techniques.

AI Strategy

Many Companies are already using AI to give them a competitive edge, by reducing costs and increasing productivity. Many more are exploring opportunities in AI to help them stand out in a crowded market and rejuvenate their business model. While it is easy to assume that the technology can simply be adopted without reshaping the business, this is just a notion. When an Organisation is planning to implement AI, it needs to define an AI strategy to take account of new opportunities and to plan for the fundamental changes needed.

From a technical perspective, AI strategy needs to address challenges around data, algorithms, and infrastructure. From an organisational viewpoint, it also needs to answer questions on skills and company structure. It is fundamental to the future of the business and therefore challenging to undertake.

For many organisations, the lack of AI strategy becomes a barrier to technology adoption. Without effective data due diligence and a credible strategy, it is impossible to achieve buy-in at executive level, so there is a lack of resources available to explore opportunities and technology. It becomes a vicious circle.

Breaking the circle requires a systematic approach. An A/IS Consultant can help Product Group Owners to define an AI strategy that meets strategic objectives and business requirements and is explainable to executives. To be credible, the strategy needs to focus on deliverables relevant to the business, such core key use cases, data strategy, risk, and governance.

Data Acquisition Strategy

Many AI projects fail not because of technology or resources, but through a weak or non-existent data acquisition strategy. In live operation, AI is completely dependent on the quality and quantity of data. However, this dependency is equally critical during the analysis and development phases. The team needs access to representative data so they can flesh out operational use cases, both for their own understanding and to train the AI engine.

Informed teams need to be highly experienced in data acquisition to define an effective strategy. This involves locating suitable data for training, assessing data quality, examining use case ethics, and importantly, data governance.

In AI, data governance is essential not only to the integrity, availability, and security of data but to guarantee it is not misused. Governance must ensure that data originating from different systems, possibly across many areas of the business, is clean, integrated, secured, and corrected of harmful bias. Governance is a key aspect of compliance, so it is essential that personal data is safeguarded during analysis, development, training and live use. The Chief Data Officer will therefore have a role in agreeing the data acquisition strategy.

How Will AI Affect Existing Staff?

There are two key aspects of implementing AI that relate to existing employees. Firstly, finding talent within the technical and business teams to play a part in designing and implementing AI. Secondly, how AI automation will affect the size and skills of the workforce and what the shape of the company will be after implementation.

Most firms find it a challenge to put together an internal AI task force. While there may be a good range of technical and business skills available, those people also have essential day jobs. AI also calls for a mindset that is tangential to regular business operations, for example in data acquisition.

Putting together an internal team then is not a simple choice. With foresight, the business may already have recruited individuals with AI strategy in mind. This would be an ideal situation, people with an interest, aptitude, and qualification in AI, loyal to the company and knowledgeable about its business strengths and position in the market. Unfortunately, this is not an option for many organisations.


External Specialists

Looking outside the business there is a raft of choices. It is feasible to recruit new talent at the start of an AI project, this guarantees up-to-date skills and thinking. However, those new team members may have little knowledge of the company’s business model or industry, and may have trouble integrating with existing staff, who may feel threatened or left out in this new skills area.

It is also possible to buy or lease technology directly from vendors. This may be a practical option in B2B sectors where AI already has a foothold. Even then, it is unlikely to make the business stand out in a crowded market; in fact, adoption may require changes to accommodate the tool, which is analogous to the tail wagging the dog.

The most practical answer is a combination of knowledgeable internal staff and qualified new hires but guided by AI consultancy services. These specialists can contribute to an AI project from its conceptual phase through to release and deployment. They can guide executives in scoping realistic and ethical objectives, create design metrics to verify results, lead the technical team in data acquisition and use case analysis and oversee deployment using bespoke or commercial systems.


Awareness of AI Opportunities

Where will AI achieve the most benefit? This question is certain to result in as many answers as there are respondents. The prevalence of AI in everyday consumer applications such as customer service and online shopping means most people can visualise its use in their department. Product Group Owners, or other senior staff members may see AI as an ally in achieving business objectives. Elsewhere, there will be individuals with pain points they want to address, along with AI champions who suggest quick wins that would illustrate benefits without fundamentally changing the business model.

Where To Start?

Cyber Smart Consulting’s AI Team find that carrying out an AI Maturity Assessment is the most valuable start point. Being external, these specialists can listen objectively to internal suggestions while analysing the organisation’s ability to adopt AI. The assessment will help the organisation to find the areas with greatest potential along with any actions or investment they need to undertake as part of an AI project. Actions could be focused on areas as diverse as strategy, technology, or organisational culture.

Accelerating AI Adoption

Having agreed on the most productive areas for AI, the next step is to address the technology solutions. There may already be a strong mindset in place regarding in-house development versus commercial off-the-shelf and on-premise versus Cloud and SaaS. This may result in a solution that fits the mindset, rather than the requirements and will not necessarily allow the project team to deploy the most suitable technology.

Cyber Smart Consulting’s AI services aim to accelerate AI adoption by helping the AI stakeholders to shape a solution based on requirements. Typically, this will involve assessing the most suitable technology, defining a business case, agreeing with management the KPIs or metrics and building a project plan for development and deployment.

It can be helpful to build a proof of concept (POC), so that project stakeholders can visualise the final solution. This will also tease out assumptions and enable analysts to better understand key use cases. Importantly, it will also verify the quality of the data in focus areas, thereby testing the data acquisition strategy. The final solution can be purchased, or designed and developed, based upon the findings from the POC.


It is impossible to ignore the potential benefits that AI can bring to a forward-looking business. Being late to the party means the competition will not only be more productive but they will employ the most talented AI specialists. Designing and implementing an AI solution is incredibly challenging, however. It requires specialist experience along with an objective viewpoint; qualities that few in-house staff can currently deliver.

It is vital to define an AI strategy that meets the organisation’s objectives and requirements, alongside a data acquisition strategy for data governance and a maturity assessment to define the most productive areas for automation. Crucially, existing staff need to understand what part they will play and how their future will look.

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