AI For Business

The Current State of AI Adoption

AI adoption in the UK is growing but remains shallow in its integration. The Office for National Statistics (ONS) found that the use of AI in businesses with 10 or more employees increased from 12% to 35% between late 2023 and June 2026. While many firms use at least one tool, the average number of AI technologies used per business only rose from 1.4 to 1.6 in that period.

This data suggests that most businesses are using AI for incremental gains rather than fundamental transformation. The ONS reports that improving business operations is the most common use case, cited by over 60% of larger businesses.

How to Bridge the Gap Between Technical AI and Business Strategy

Managers must act as the bridge between technical capabilities and organisational goals to avoid deploying technology for its own sake. The University of Leeds defines this role as the ability to evaluate, adopt, and govern AI across core functions such as strategy, innovation, and people management.

To move from a technical tool to a business strategy, a manager must handle three specific transitions:

1. From Task to Workflow: Instead of using AI to write a single email, redesign the entire communication pipeline to automate data gathering and draft generation.
2. From Output to Outcome: Shift the metric of success from "time saved per document" to "increase in lead conversion rates" or "reduction in customer churn".
3. From Tool to Governance: Move from allowing "shadow AI" (unauthorised employee use) to a formal Acceptable Use Policy.

Framework for an AI Acceptable Use Policy

A formal policy prevents the unauthorised disclosure of confidential information and protects intellectual property. Arbor Law suggests that a policy should clearly distinguish between pre-approved tools and those that require a formal request process.

Every business policy should include these specific rules for inputs and outputs:

Input Constraints

• No Proprietary Code: Do not input source code that is not open-source.
• No PII: Do not input personally identifiable information of customers or co-workers.
• No Third-Party IP: Do not input copyrighted text or images without the right to use them.

Output Requirements

• Human-in-the-Loop: A human must review and verify the accuracy of all AI outputs before they are published or shared to prevent hallucinations.
• Transparency: AI-generated content should be clearly identified as such, even for internal use.
• Final Decision Authority: A human must make the final decision on any AI-assisted output that impacts a living person, such as a job applicant or a customer.

Practical Applications for Growth and Efficiency

AI is most effective when applied to specific, high-friction business processes. Business Academy identifies several high-value areas for AI implementation:

Source: business.gov.uk

Managing the Technical Risks of AI Models

Business leaders do not need to write code, but they must understand the failure modes of the models they buy or build. Worcester College at the University of Oxford highlights three critical technical risks that managers must monitor:

Algorithmic Bias AI models train on pre-existing data. If that data contains historical prejudices, the AI will reproduce and enhance those biases in its predictions.

Data Drift A model that works today may fail tomorrow because the real-world data it encounters changes over time. This is known as drift. Managers must implement monitoring frameworks to ensure the model remains relevant.

The Black Box Problem Many AI systems lack transparency in how they derive a recommendation. The University of Leeds emphasises the importance of Explainable AI (XAI) to make systems transparent and compliant with regulations like GDPR.

AI Skills for Management

Technical literacy for managers differs from technical literacy for developers. The University of Edinburgh suggests that future business managers must be able to allocate resources to buy AI applications and translate generic ethical guidelines into concrete organisational practice.

Essential managerial AI competencies include:

• Evaluation Criteria: Determining which metrics prove an AI tool is delivering value.
• Resource Allocation: Deciding whether to build a custom model, buy a SaaS product, or use a hybrid approach.
• Ethical Translation: Converting high-level principles into daily operational constraints for staff.

Portfolio Approach to AI Projects

Companies avoid failure by balancing high-risk transformative bets with low-risk operational wins. Wharton Executive Education recommends a portfolio approach where "quick wins" build internal confidence and fund more ambitious, long-term projects.

A balanced portfolio typically distributes effort across:

• Efficiency Gains: Automating a repetitive report (Low risk, immediate ROI).
• Product Enhancement: Adding a predictive feature to a customer product (Medium risk, growth ROI).
• Business Model Pivot: Using AI to enter a new market or change a revenue stream (High risk, strategic ROI).

Determining AI Value Creation

Value is created when AI reduces the cost of a cognitive task or increases the quality of an outcome beyond human capacity. Imperial College London notes that AI should be viewed not just as an automation tool, but as a strategic enabler that transforms how a business decides and innovates.

The three primary paths to value are:

1. Cost Reduction: Reducing the man-hours required for a specific output.
2. Revenue Growth: Using AI to find new customers or increase the lifetime value of existing ones.
3. Risk Mitigation: Using predictive models to identify fraud or operational failure before they occur.

Discovery and Baseline

Identify a specific business problem. Define the current baseline metric (e.g., "it takes 4 hours to produce a monthly report"). Establish the ethical boundaries based on the Data and AI Ethics Framework provided by the Government Analysis Function.

Prototype and Governance

Select a tool or develop a model. Create a restricted environment for testing.

# Example: Using a controlled API environment to test a prompt 
# rather than a public web interface to ensure data privacy.
curl https://api.provider.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $API_KEY" \
  -d '{
    "model": "gpt-4",
    "messages": [{"role": "user", "content": "Analyse this anonymised dataset for trends."}]
  }'

Evaluation and Scaling

Compare the AI output against the human baseline. Check for accuracy, bias, and time saved. If the results are positive and safe, scale the tool to the wider team under the Acceptable Use Policy.

The Future of Work and Management

AI does not cause social or economic change on its own; human decisions about how to implement it do. The University of Leeds argues that the future of work depends on whether AI is used to replace personnel or to enable them to do more creative and rewarding work.

Future managers will be judged not by their ability to use AI tools, but by their ability to allocate resources to the right applications, translate ethical guidelines into practice, and decide which criteria define the success of an AI deployment.

Sources

• Artificial intelligence in UK businesses - Office for National Statistics
• Use AI to drive your business’ growth – Business Academy
• AI for Business MSc | University of Leeds
• How to Develop an AI Acceptable Use Policy | Arbor Law
• AI for Business MSc - Postgraduate taught programmes | The University of Edinburgh
• AI for Business Course – Wharton
• AI for Business Transformation | Imperial College London
• Artificial Intelligence (AI) guidance, best practice and standards – Government Analysis Function