Artificial Intelligence (AI) is transforming businesses by automating tasks, improving decision-making, and enhancing customer experiences. Here's a quick look at how AI is being used today and how you can get started:
- AI in Action: Industries like banking, real estate, and home services are already benefiting. For example, Starling Bank uses AI to detect fraud in real time, while businesses like Golden Rule Plumbing optimise operations with AI tools.
- Key Benefits: AI reduces manual errors, saves costs (up to £4,000 annually for small businesses), and helps businesses grow.
- Getting Started: Evaluate your data, train your team, and set clear ethical guidelines. Start small with a pilot project and scale as you see results.
- Choosing Tools: Select AI tools tailored to your needs, such as customer service automation or sales process optimisation.
AI is not just about technology - it's about rethinking how you work. Read on to learn how to prepare, implement, and maintain AI in your business.
1. AI Basics
What AI Does
Artificial Intelligence stands out from traditional software because it can learn, reason, and make decisions based on data patterns. Unlike fixed-rule programmes, which follow predefined instructions, AI adapts and improves by learning from data.
While traditional software is designed to calculate, AI identifies trends, predicts outcomes, and delivers more precise recommendations over time. These capabilities make AI a powerful tool for various business applications.
Types of AI Systems
Here are some of the main types of AI systems and their applications:
| AI Type | Primary Use Cases | Business Advantages |
|---|---|---|
| Machine Learning | Data analysis, predictions | Better decision-making, deeper customer insights |
| Natural Language Processing | Customer service, communication | Streamlines customer interactions, automates replies |
| Computer Vision | Quality control, security | Minimises errors, enhances workplace safety |
| Robotic Process Automation | Administrative tasks, data entry | Boosts efficiency, cuts operational costs |
A recent survey revealed that 82% of tech business leaders plan to increase their AI investments in the next year, with 72% of employees already incorporating AI into their daily tasks.
AI in Business Today
AI's practical use is already delivering results across various industries:
- Banking and Finance: Financial institutions utilise AI to monitor transactions in real time, improving security and protecting customers.
- Property Services: Major property platforms now use conversational AI to simplify home searches. Instead of relying on traditional filters, users can describe their ideal property, and the AI processes these natural language queries to deliver tailored results.
"It's all about speeding up the clock of the enterprise", says Seth Earley, CEO of Earley Information Science and author of The AI-Powered Enterprise.
AI adoption is proving beneficial for businesses of all sizes. Over 90% of small business owners report improvements in reducing manual errors and driving growth, with 28% estimating savings of at least £4,000 in the next year thanks to AI.
KPMG's recent survey highlights that 77% of business leaders believe generative AI will have the most impact among emerging technologies.
2. Getting Your Business Ready
Check Your AI Readiness
Start by evaluating your current capabilities and infrastructure. Here are the key areas to focus on:
| Assessment Area | What to Evaluate | Importance |
|---|---|---|
| Data Maturity | Quality, accessibility, and storage of your data | Ensures reliable AI training and results |
| IT Systems | Existing systems and their integration potential | Determines if your tech setup can support AI |
| Ethics Framework | Safety protocols and governance structures | Safeguards your stakeholders and reputation |
| MLOps Capability | Scalability of infrastructure and monitoring tools | Helps maintain effective AI operations |
AI readiness assessments typically cost between £300 and £1,450 per unit per day. Once you've identified your current standing, it's time to focus on building your team's expertise.
Train Your Team
The UK government highlights the importance of AI skills with its 2024 AI Upskilling fund pilot scheme, which offers SMEs up to 50% match-funding for AI training.
Training should cover:
- Practical use of AI tools
- GDPR-compliant data handling
- Problem-solving with a focus on business value
- Recognising and addressing bias in AI systems
"As AI transforms the workplace, one thing should not change: an organization's commitment to doing business the right way. Training on proper AI use is critical now more than ever." – NAVEX
After training, establish clear guidelines to ensure ethical and effective AI use.
Set AI Guidelines
With 73% of businesses already using AI, having clear and enforceable guidelines is crucial. A strong framework ensures ethical use and aligns AI practices with your business goals.
Key components of AI guidelines include:
-
Ethical Standards
Develop policies that prioritise fairness and transparency. Regular algorithm audits can help maintain compliance and uncover any biases. -
Data Governance
Put in place robust data management policies to protect customer information while enabling efficient AI operations.
"The most impactful frameworks or approaches to addressing ethical AI issues … take all aspects of the technology -- its usage, risks and potential outcomes -- into consideration." – Tad Roselund, managing director and senior partner at Boston Consulting Group
- Monitoring Systems
Set up continuous oversight processes to ensure your AI systems remain aligned with ethical standards and your business objectives.
Recent data reveals that 65% of businesses across various sectors actively use generative AI in 2024.
How to Implement AI in Your Company: The 5-Pillar Framework
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3. Steps to Add AI
Once you're ready to move forward, these steps can guide your AI integration process.
Pick the Right Projects
When selecting AI projects, weigh their potential business impact against technical feasibility. A recent survey found that 57% of enterprises are either implementing or expanding AI initiatives, with 70–75% reporting clear benefits.
| Evaluation Factor | Key Considerations | Success Indicators |
|---|---|---|
| Business Impact | Problems solved, ROI potential | Tangible outcomes, cost reductions |
| Technical Feasibility | Data availability, infrastructure | Quality of existing data, system compatibility |
| Implementation Speed | Time to value, resource needs | Quick wins, minimal disruption |
| Scalability | Growth potential, flexibility | Usable across departments |
"Don't end up with an answer looking for a question. Projects focused on business outcomes that start with a question rather than an answer generally do well."
- Sanjay Srivastava, Chief Digital Strategist at Genpact
Select AI Tools
The UK offers a variety of AI tools tailored to specific industries. For instance, Babio provides AI software designed for solar companies, offering instant quotes and system designs while integrating seamlessly with existing CRMs.
Here are some trusted AI tools that might align with your business goals:
-
Lead Management and Customer Service
CloseGPT offers a range of AI-driven services for £237 per month plus usage fees. These include handling inbound and outbound calls, lead qualification, appointment scheduling, and automating customer service. -
Sales Process Automation
Phostra Digital's Voice AI Assistant, priced at £99 per month plus usage fees, supports 24/7 lead qualification, automated appointment booking, CRM integration, and thorough call logging and follow-ups.
After choosing your tools, the next step is to test and refine your implementation.
Test and Grow
Start small to ensure your AI solutions work as intended before scaling up.
"If you move the needle by 3% during your initial pilot you're doing well. Deploy, find the gaps, deploy again, and keep moving up incrementally."
- Ganapathy Krishnan, VP of Engineering at Flipkart
-
Start Small
Focus on a proof of concept (PoC) that addresses a specific business challenge. This approach helps secure support across your organisation while reducing risks. -
Measure Progress
From day one, track key performance indicators (KPIs) such as process efficiency, cost savings, customer satisfaction, and team productivity to gauge success. -
Scale Strategically
Once the pilot achieves its goals, expand step by step. As Sunil Dadlani, SVP and CIO at Atlantic Health System, explains:"A pilot must deliver the perceived measure of success. Only when we see a promising result do we say, 'What would it take to scale this up, how much time will it take, what will be the time to value, what investments will be needed for tech infrastructure resources, and how will we operationalize it.'"
4. Solving AI Problems
After planning how to integrate AI, the next step is addressing challenges that could hinder its success.
Handle Team Concerns
Currently, only 14% of small businesses use AI compared to 34% of larger organisations. This gap, combined with declining interest among senior executives, highlights the importance of overcoming resistance within teams. Building on earlier efforts like training and clear AI guidelines, the following strategies can help:
| Common Concern | Solution Strategy | Expected Outcome |
|---|---|---|
| Job Security | Regular training and upskilling programmes | Boosted confidence and team engagement |
| Technical Complexity | Phased implementation with hands-on workshops | Better understanding and smoother adoption |
| Workload Impact | Clear demonstration of time-saving benefits | Increased acceptance and productivity |
| Decision Authority | Transparent AI guidelines and human oversight | Greater trust in AI systems |
"AI adoption is not a one-time event that companies need to plan for. It is more of an ongoing mindset shift where we look at all processes with an AI-first lens." - Vrinda Khurjekar, senior director at Searce
Protect Customer Data
For 58% of users, data security is a major concern, especially in industries dealing with sensitive financial or property data. Here's how to address it:
1. Implement Robust Security Measures
Build strong data protection protocols including:
- Two-factor authentication (2FA) for accessing AI systems
- End-to-end encryption for data both in transit and at rest
- Regular security audits and penetration testing
- Data masking and pseudonymisation techniques
2. Enforce Strict Data Governance
Only 10% of organisations currently have a formal policy for generative AI. Establish clear guidelines that address:
- How data is collected and stored
- Access controls and user permissions
- Routine compliance audits
- Incident response plans
Keep AI Working Well
With less than 40% of organisations successfully deploying AI projects, maintaining system performance is critical. Focus on these areas:
| Maintenance Area | Key Actions | Monitoring Metrics |
|---|---|---|
| Data Quality | Regular validation and cleaning | Data accuracy rates |
| Model Performance | Periodic retraining and drift monitoring | Prediction accuracy |
| System Integration | API health checks and updates | System uptime |
| User Feedback | Regular stakeholder surveys | Satisfaction scores |
"Without clean, correct, and accessible data, even the most advanced AI models are destined to fail." - Paul Pallath, vice president of applied AI at Searce
Continuous monitoring systems are essential for tracking performance metrics and catching issues early. Regularly retraining models ensures they stay relevant as user behaviour, market trends, or conditions evolve. For example, Phostra Digital's Voice AI Assistant uses automated call logging and continuous monitoring, demonstrating how proper upkeep guarantees reliable performance.
5. Next Steps
Start your AI journey with these key actions:
| Priority Area | Immediate Actions | Expected Impact |
|---|---|---|
| Team Readiness | Schedule an AI foundations workshop | Boost team confidence and ease adoption |
| Data Preparation | Audit the quality of your existing data | Achieve greater accuracy in AI models |
| Implementation | Launch a pilot project | See measurable ROI within 12–14 months |
These steps are designed to help you take immediate action. Companies that reach digital maturity typically see a 4.3% return on their AI investments within 13 months. Here's how to get started:
-
Begin with Core Training
Build your team’s confidence by providing hands-on experience with AI tools and strategies. For instance, Smith's Pet Foods successfully integrated AI into their operations after investing in foundational training. -
Choose Strategic Projects
AI is already transforming customer relationships for 64% of businesses. Focus on areas like:- Automating lead engagement
- Deploying customer service chatbots
- Enhancing data analysis and reporting
- Streamlining process automation
"When learning is embedded into our daily routine, the uptake of new AI tools becomes a habit rather than a hurdle." - Stephen McClelland, ProfileTree's Digital Strategist
- Review and Scale
Only 53% of AI projects make it from prototype to production. To improve success rates, implement:- Weekly performance reviews
- Monthly team feedback sessions
- Quarterly retraining of models
- Annual reassessment of your strategy
"Embedding AI into the fabric of our teamwork allows us to achieve a balance between human creativity and machine efficiency. This synergy drives businesses forward in the digital age." - Stephen McClelland
For tailored advice, get in touch with Phostra Digital:
- Phone: (+44) 07400 461212
- Email: hello@phostra.digital
- Office: 4th Floor, 14 Museum Place, Cardiff, CF10 3BH
Keep revisiting and improving your approach as you integrate AI into every aspect of your business.
