AI is going to fundamentally change business.
As AI consultants, our job is to remain on the cutting edge of AI advancements and whilst developments are becoming more and more routine, if we step out of the bubble, we’re able to see just how quickly AI is progressing.
With that in mind, we’ve put together a guide on the future of AI in business and how it might impact you.
Predictive Analytics
One of my largest assumptions is that predictive analytics will become easily accessible to all businesses, not just the largest companies.
That means, your business will be able to employ algorithms based on historic data to help ‘predict’ a wide range of critical operational and market factors. This includes:
Supply Chain Disruptions and Inventory Needs: Foreseeing potential bottlenecks in the supply chain, predicting future inventory requirements to avoid stockouts or overstocking.
Customer Behaviour and Demand: Anticipating what products or services customers will want next, when they are likely to purchase, and which customer segments are most likely to churn or respond to specific marketing campaigns.
Sales Trends and Revenue: More accurately forecasting future sales figures, identifying periods of high and low demand, and understanding the potential revenue impact of different strategies or market changes.
Market Fluctuations and Opportunities: Identifying emerging market trends, shifts in consumer preferences, and potential new market opportunities before they become widely apparent.
AI Agents Will Revolutionise Operations and Workflows
It’s no secret that one of the limiting factors of AI is the amount of time it can spend autonomously on any given task. My prediction is that by the end of 2025, autonomous AI agents will be able to go off for hours at a time completing a task with minimal supervision.
This could mean…
Comprehensive Market Research and Analysis: An AI agent could be tasked with conducting in-depth market research, continuously monitoring competitor activities, analysing evolving consumer sentiment across various platforms, identifying emerging trends, and compiling a detailed report with actionable insights—all over several hours without needing human intervention until the final review.
Automated Content Creation and Campaign Management: AI agents could autonomously draft multiple versions of marketing copy, create variations of social media posts, design basic visual assets, schedule content across different channels, monitor campaign performance in real-time, and even make minor adjustments to optimise reach and engagement based on incoming data.
Advanced Data Processing and Reporting: Businesses could delegate tasks like cleaning and processing large datasets, performing complex data analysis, identifying anomalies or key patterns, and generating comprehensive operational or financial reports, with the AI agent working independently for extended periods.
Proactive Customer Relationship Management: An AI agent could manage a significant portion of customer interactions, from initial lead qualification and personalised follow-ups to resolving complex customer service issues by accessing and processing information from various internal systems, all while learning and adapting its approach over hours of operation.
Streamlined Recruitment and Onboarding Processes: AI agents could autonomously screen a large volume of job applications against complex criteria, conduct initial rounds of candidate assessments or interviews via text or voice, schedule follow-up interviews with human recruiters, and even manage the initial stages of employee onboarding by providing information and answering common queries.
Enhanced Software Development and Testing: AI agents could spend hours writing or refactoring code for specific modules based on detailed specifications, performing extensive automated testing routines, identifying and flagging bugs, and even suggesting potential fixes or improvements to development teams.
Sophisticated Supply Chain Monitoring and Optimisation: An AI agent could continuously monitor global supply chain data, track shipments, predict potential delays, identify alternative sourcing options, and even initiate adjustments to logistics plans to mitigate disruptions—all with minimal human oversight for extended durations.
Personalised Learning and Development Pathways: For internal training, AI agents could dedicate hours to curating personalised learning materials for employees based on their roles, skill gaps, and career aspirations, track their progress, and suggest new learning modules or resources.
The key shift would be from AI as a tool requiring constant human prompting for discrete sub-tasks to AI as a more autonomous partner capable of managing significant, multi-step projects over extended periods, thereby dramatically increasing efficiency and freeing up human capital for even more strategic initiatives.
The Workforce Will Undergo a Significant Transformation Towards Human-AI Collaboration
A lot of people in the AI space (including Bill Gates) theorise that AI will be the cause of mass unemployment.
Whilst that may be the case much further down the line (not least because of manufacturing bottlenecks to create the infrastructure to allow this to happen), I do think that the workforce will undergo a significant change within the next 3 years.
My prediction is that we will shift from holding AI’s hand through tasks to a more collaborative relationship.
Examples of which could include:
Strategic Brainstorming and Idea Validation: A marketing team could collaborate with an AI that generates multiple campaign concepts based on broad strategic goals. The human team then refines, critiques, and selects the most promising ideas, with the AI further assisting by instantly modelling potential outcomes, budget implications, or resource needs for the chosen concepts. The AI acts as an tireless idea generator and rapid scenario tester, while humans provide strategic direction and nuanced judgment.
Augmented Scientific Research and Discovery: Scientists could task AI with continuously scanning and synthesising vast amounts of research papers, experimental data, and genomic sequences to identify novel patterns or potential hypotheses. The AI wouldn’t just present raw data but would offer preliminary interpretations or highlight areas warranting human investigation. Human researchers would then design experiments to test these AI-generated leads, working with the AI to analyse the results and iterate.
Co-Piloted Complex Decision Making: Business leaders could use AI “chief of staff” agents. Before a major strategic decision, the AI could independently gather all relevant internal data, analyse market conditions, model various scenarios, and present a succinct brief with several well-reasoned options, including potential risks and benefits. The human leader then engages in a dialogue with the AI, asking clarifying questions and exploring nuances before making the final, human-led decision.
Human-Supervised Autonomous Operations: In manufacturing or logistics, AI systems could autonomously manage entire production lines or warehouse operations for extended periods. Human supervisors would transition from direct control to overseeing the AI’s performance, managing exceptions, and focusing on strategic improvements to the automated system, intervening only when the AI flags a novel problem or requires a decision outside its established parameters.
