Report Review: Optimising AI for Business

In an insightful study by BARC on the current state of AI for business, several key outcomes were highlighted. Here’s a quick rundown of the essential findings and recommendations for businesses aiming to leverage AI effectively:

Key Recommendations from the Study in AI for Business

1. Address the Skills Gap Early. One of the most significant challenges small businesses face in adopting AI for business is the lack of expertise. Engaging your workforce with free training and certifications early in your AI for business journey is a cost-effective method for staying in front.

2. Keep Calm and Carry On. The AI market is still in its early stages, and it’s essential not to let fear of missing out (FOMO) drive your strategy. Expect some unfulfilled expectations as the technology develops. Focus on creating a measured plan in adopting AI for business, and be prepared for a learning curve.

3. Start with Trusted Suppliers. Leveraging existing, trusted suppliers can provide a significant advantage in your AI for business journey.

4. Prioritize Compliance and Responsible AI. Compliance and regulatory concerns should be a top priority, but don’t overlook the importance of a comprehensive Responsible AI strategy.

5. Enhance Business Intelligence with AI. Small businesses can achieve immediate returns on investment by integrating AI into their existing workflows. AI can automate time-consuming tasks and provide smart insights through dashboards and narratives, driving efficiency without significant new capital investments.

Additional Insights from the Report

  • AI Adoption Plans: Only 5% of businesses surveyed were not yet discussing their plans for AI adoption. However, over half were still in the early stages of planning.
  • Skills Shortage: Lack of skills in AI was cited as the most frequent obstacles (39%) for all firms, even those considered high readiness, in fact they were even more likely to name this as a key obstacle (overcoming Dunning-Kruger effect).
  • Buyer’s Remorse: Many firms experience ‘buyer’s remorse’ with early AI technology adoption. Careful vetting of new purchases relative to your plans and projects is essential to mitigate this risk.
  • Integration with Existing Systems: Every new AI project will draw on, integrate with, and often improve existing systems. Ensure your vendors are capable and involved in your AI strategy.
  • Costs. The cost of implementing advanced AI techniques like RAG (see below) and fine-tuning can be significant. Small businesses must balance these costs with the clear ROI of more accurate and relevant AI responses. Leveraging third-party platforms and cloud environments can help manage these expenses  – although cloud options can quickly become expensive if not managed properly.

Popular Use Cases for AI in Business

  • AI Chatbots and Intelligent Assistants: Popular for automating customer interactions and providing personalised experiences.
  • AI-Infused Business Intelligence: Enhancing business intelligence with AI is critical for gaining insights and driving decision-making.
  • AI-Driven Data Management: Effective data management is foundational for any AI initiative, with many businesses prioritising this use case.
  • Recommendation Engines: AI improves user experience through recommendation engines and personalised data. Early adopters report positive ROI on this use case, with 23% planning to adopt recommendation engine technology in the next 12 months.

Other industry specific use cases – AI for business:

  • Sales Forecasting
  • Fraud Detection
  • Predictive Maintenance
  • Robotic Process Automation
  • Supply Chain Management
  • Price Optimization
  • HR Management
  • Inventory Management

Emerging Trends in AI Technology for Business

  • Multi-Model Environments: Early adopters are moving towards environments utilising multiple AI models tailored to specific tasks. This approach ensures optimal performance and relevance of AI outputs.
  • Retrieval-Augmented Generation (RAG): Enhances AI model knowledge by integrating proprietary enterprise information, reducing errors, and improving relevance.
  • Prompt Engineering: Developing skills in prompt engineering significantly improves the quality and accuracy of AI outputs. Many businesses are choosing to handle this in-house.

For a deeper dive into these insights, read the full report here.

If you’re interested in how to integrate AI, automation and other software into your business, get in touch. We offer a free digital transformation audit and initial consult to help you identify the most impactful changes you can make in your business.

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