AI in operations: What to expect in 2024
Contents
Introduction:
The state of play
It’s 2024 and Artificial Intelligence (AI) is continuing to shake up the operations landscape. According to The Hackett Group, large businesses could be set to save as much as 40% on costs in the next five years as a result of AI advancements. However, the AI revolution won’t come without challenges. Navigating the reality of AI’s impact on the labor force will be a bumpy ride, as the need for FTEs will reduce, and surviving employees will need to pivot to mission-critical, value-added work.
This AI in 2024 guide looks at the potential of AI in operations, presenting predictions and insights from leading AI minds for the year ahead. Discover the key AI concepts shaping the industry, from advanced machine learning algorithms driving task efficiency to AI-enhanced customer service bots that personalize client interactions.
We’ll also explore the regulatory landscape and the barriers facing AI adoption. Evolving legal and ethical frameworks pose significant stumbling blocks, requiring businesses to navigate choppy waters of complex compliance and organizational challenges.
Finally, the guide provides an actionable AI roadmap tailored for operations and service leaders. This plan outlines practical steps to integrate AI into your business, ensuring you stay ahead in a rapidly evolving digital landscape.
These insights will help you get a sense of what’s coming and arm you with the knowledge to forge ahead with AI in 2024 and beyond.
"In 2024, the longstanding paradigm of labor arbitrage in the Global System Integrators (GSIs) sector is poised for a radical shift."
2024 AI trends and predictions
We asked a selection of top industry minds to share their trends and predictions for AI in operations in 2024.
Virtual assistants will improve drastically
We’re going to see enterprise data being used by AI to provide real-time content to more advanced customer-facing natural language chatbots and internal knowledge bases and processes. Data stewardship and security will become increasingly important as Large Language Models increasingly take on more tasks. Customer interaction and servicing will shift towards far greater augmentation and automation.
AI will enhance digital transformation in GBS
AI will drive further digital transformation within GBS in the coming year. Organizations can harness its data analysis and task automation capabilities for better decision-making, customer experiences, and streamlined operations. AI tools will enhance workflows, freeing up employees for high-value tasks, boosting efficiency, and creating growth opportunities. I fully expect that AI will be a crucial tool for fostering lasting relationships with customers and employees, ensuring retention through personalized experiences.
It’s the end of labor arbitrage
In 2024, the longstanding paradigm of labor arbitrage in the Global System Integrators (GSIs) sector is poised for a radical shift. Historically, GSIs have thrived on labor cost differences, supported by cost-centric customer purchasing habits. However, this year we will witness two key trends: Firstly, BPO partners will aggressively deploy AI at scale, thereby drastically reducing workforce needs while enhancing service efficiency. Secondly, a decline in service renewals for those failing to adapt, as the focus shifts from labor to technology arbitrage. This shift could lead to the demise of major players who lag in effective AI and technology implementation, signaling an era where technology delivery becomes a critical survival factor.
Customer avatars will exist by 2034
In the next decade, AI-powered customer avatars will transform customer interactions. These avatars will be the ultimate brand ambassadors, with unique personalities, voices, and appearances customized to align with the brand's image. They will provide round-the-clock, highly personalized services based on customer data, speak multiple languages, analyze customer sentiments, and adapt in real-time to customer preferences and reactions. They'll anticipate customer needs, solve problems, and seamlessly transfer to human agents when required, all while maintaining data security and brand integrity. Companies that can successfully implement and manage AI-driven customer avatars will gain a significant competitive advantage in the GBS space over the next decade.
"95% of customer service leaders anticipate AI bots serving their customers within the next three years."techwireasia.com
BPOs will take an ‘automation-first’ approach
Generative AI, particularly in the form of virtual machine intelligence co-workers, is poised to revolutionize Business Process Outsourcing (BPO). Envisioned as a seamless integration into human workflows, these AI co-workers can interact with human counterparts as equals. This innovation heralds a pivotal moment for BPO, challenging the traditional reliance on low-cost human labor in favor of significant cost reductions. Unlike previous automation technologies, which offered marginal cost benefits over inexpensive human resources, generative AI promises a dramatic decrease in operational costs that businesses cannot overlook. BPO organizations will need to respond by upskilling their workers to provide higher-value contributions as part of a combined human plus machine workforce.
AI stormers will leap ahead while strivers will lag behind
In 2024, those already experienced in AI deployment, labeled as 'Stormers,' will significantly expand their AI usage within the initial stages of service operations. These stages involve discerning customer needs and gathering relevant data. By employing AI at scale, Stormers will enhance their inbound communication and service delivery efficiency. Meanwhile, 'Strivers'—those new to AI—will begin their journey by implementing AI in decision-making processes, focusing on customer intent categorization and sentiment analysis. As ‘Strivers’ grapple with their inaugural AI projects, ‘Stormers’ will solidify their lead in service operations, showcasing a clear divide in AI maturity and application.
AI growth will be stunted by data problems
There is going to be a very real 'race to automate' among GBS firms. Along the way there will be frustrations as firms realise that an AI strategy requires a data strategy and they don't have one. And there will be tensions as clients realise that their IP in the form of their past work is being used to train these algorithms to be used on all of the firms' clients.
AI-powered processes will grow arms and legs
In 2023 we saw businesses begin to use elements of AI in the services they manage. For example, using email triage AI to sort thousands of emails, or extracting data from documents to lighten the manual load. 2024 is going to see AI application massively dialled up as new AI tools come to market with the capability to automate entire workflows through prompts and data. This surge in AI-driven automation will unlock huge opportunities for efficiency gains but also reshape workforce dynamics, as businesses realise they can automate many of the jobs that humans have traditionally done. This innovation sets the scene for a future where AI is not just a co-pilot, but a core component of the business operations model.
Risks and regulations
At the time of writing, lawmakers are lagging behind the speed of innovation in AI. It’s businesses and ultimately people who stand to bear the brunt of this failure to act, as deep fakes, consumer privacy and biased programming are just a few of the risks posed. Experts weigh in with their thoughts…
Legislation may put AI on ice. In this case, try open-source
As AI businesses, particularly service-oriented ones, often rely on major models like ChatGPT, Claude, and Llama, they're vulnerable to legislation targeting these 'big boys.' The risk is that your chosen AI vendor may be heavily regulated, limiting their data access for model training or restricting their operational fields, rendering them ineffective. To mitigate this, diversify your dependency away from a single model, as with any vendor. The extreme option is developing your own AI model with proprietary data, but this is often unfeasible. A more practical solution is adapting an open-source model, like those from Hugging Face, to meet your specific needs.
Fear is always the biggest barrier
Barriers might include technology, skills, security, privacy, and ethics, but the biggest barrier to technology advancements is always fear. People will be afraid that they will lose their jobs, and organizations will need to actively bring employees along on the journey with them, by having clear policies, being transparent about objectives, setting guardrails and ensuring training is fully inclusive.
Serious AI regulation will come into force
2024 is shaping up to be a game-changer for AI regulation. It looks like the EU AI Act might finally kick in, and it's a big deal. It's not just about ticking boxes; it's going to affect how we all work with AI, big time. Organizations are going to need to be properly on top of compliance as they deploy and roll out AI.
Also, prepare for industry-specific watchdogs to step up their game. Think financial services, pharma, and the like. They're scrambling to figure out how to handle AI without slowing down progress. Remember, not all regulators have their act together (flashback to the 2008 financial mess, anyone?), so if you're not hearing much from them on AI, it’s a red flag.
Bottom line: If your business is in the regulated zone, brace for impact. It’s all about staying ahead of the curve and not getting caught off guard.
Sunk cost fallacy will hold shared services back
In internal shared services the transition to AI may be slower due to the sunk cost fallacy. Organizations have invested heavily in creating captive service units, and it will take time to divest from these established systems. Additionally, the transformation within internal functions will be gradual, as companies will need to adapt their working patterns over time. GBS leaders need to start thinking bigger and redesigning these functions for the combined human plus machine workforce. This shift represents not just a technological advancement, but a fundamental change in how businesses approach outsourcing, internal processes, and the very nature of work. Generative AI is not just an upgrade; it's a game changer, redefining the economics and strategies of business operations.
Responsible AI business practices are key to prevent trust issues
The primary risk is eroding trust in AI, which will prevent adoption of AI systems. Whilst regulations are under development, businesses must adopt responsible AI practices and, critically, engage their workforce to establish AI principles for design, implementation, communications and robust governance. Collaborating with trusted AI advisors is recommended to take proactive measures.
AI data bias and regulation will pose significant challenges
The trouble with AI models is they've got bias. For example, if an LLM is made by an American team - it's going to lean towards an American viewpoint, which might not resonate with people elsewhere. And then there's the whole privacy can of worms. Businesses that use AI chatbots without solid data rules are opening themselves up to worrying data leaks. Leaning too hard on outside tech for crucial stuff in your business is generally asking for trouble.
GenAI tools rely on using public datasets, which is like a magnet for regulators. So, if regulations shift suddenly, boom – your GenAI-powered tools could be yanked off the market just like that, highlighting the need for caution.
It's essential for these companies to adhere to traditional best practices in data management as a foundation. This approach can provide a more stable and universally accepted baseline, ensuring they remain agile and compliant in the face of diverse and evolving international regulations. This strategy not only mitigates risks but also aligns with global standards of data ethics and privacy.
AI Action Plan
So, how can you actually start implementing AI into the operations you manage in 2024? Leo Ryan presents his advice.
1. Find the pot of repeatable gold : Evaluate all operations in your business, identifying tasks by their skill level, frequency, and cost. Focus especially on high-repetition, high-skill tasks that require significant investment.
2. Integrate by use case: Use a quadrant system to categorize operations based on effort, repetition, and cost. Prioritize AI implementation in areas with the highest cost and repetition, and the lowest skill requirements.
3. Let the data guide you: Leverage data from timesheets or other records to understand where your team's efforts are most utilized. This data will be invaluable in prioritizing AI integration and evaluating its impact.
4. Choose your AI fighter: Recognize that major software vendors are integrating AI into their products (e.g., Microsoft's Co-pilot, Adobe's Firefly, Autodesk's Generative Design). Identify which of these tools are already in use and assess their potential for further AI implementation.
5. Augment, don’t replace: Address the behavioral changes required for AI adoption. Ensure that the integration of AI is positioned as a move to enhance workforce output and conditions, rather than a means to replace human labor. Understand that successful AI integration involves significant effort in changing attitudes and work practices.
6. Develop a long-term AI strategy across every department: Implement a coherent AI strategy across the business to avoid unintended consequences of isolated AI applications. This includes mapping out every step of your business operations, from attracting customers to delivering final products or services.
7. Keep tinkering until it works: Continuously evaluate the impact of AI on your operations, especially in terms of cost reduction and efficiency improvements. Be prepared to adjust your strategy based on these findings.
Closing remarks
Don’t ignore AI!
The chances are, your employees are already using AI, and your competitors are developing it. Your organization needs to embrace AI and take proactive action. Decide on an AI policy, carry out risk assessments and understand how AI can be best and most safely be used in your organization. Build a platform [Some organizations may suffice with just providing access to a public LLM, some may use an enterprise version with built in security, others may want to build a ring fenced solution which only uses their data. It’s horses for courses.] that is appropriate for your needs. Review the quality of your data. Involve everyone, leave no one behind.
Test the waters in the verticals you operate in
Finance and accounting: Not only is there an opportunity to automate tasks such as invoice processing, expense management, but AI can be used to improve financial reporting both in real-time and predictive models, analyse financial data to identify anomalies and fraud patterns and ultimately enhance risk management and compliance.
Procurement: AI can be leveraged to streamline procurement processes and reduce costs by automating supplier sourcing, purchase order processing, and contract management, as well as analysing supplier performance and market trends to identify potential risks and opportunities.
Human resources: AI will reduce administrative burdens and improve HR efficiency by automating tasks such as CV screening, candidate assessment, and employee onboarding, but where it adds exponential value is in analysing HR data to identify areas for improvement in employee engagement, retention, and diversity.
Safeguard your operations
OpenAI's recent GPTs release, offering custom bots trained on internal documentation, addresses the custom vs. generic AI dilemma. To safeguard operations, consider these measures:
- Decouple the AI model from your IT system for easy replacement; ensuring data integrity by clarifying data provenance, ownership, and ensuring data portability from vendor models.
- Implement decision audits. This is vital due to the ambiguity around responsibility for AI actions—whether it lies with the creator or owner.
- Regular evaluation of the AI's decision-making processes is essential to maintain oversight and ensure alignment with client interests.
These strategies provide a framework for responsibly integrating these advanced AI tools into your business infrastructure.
It’s time to share the AI wealth
In 2024, AI has got to start playing nice in terms of equity. We all know we need a productivity boost in the economy, and it’s true that AI's the golden ticket for that. But here's the kicker – it's not about AI taking over jobs; it's about AI giving jobs a supercharge.
AI is essentially a huge natural resource which every internet user has contributed to over many years. The benefits should be distributed equally, not just hoarded by a few Silicon Valley tech wizards who got savvy with soaking up internet smarts to build AI gadgets. We're talking fair game here, making sure AI's benefits are available to the whole crowd, not just a group of smart geeks.
Acknowledgements
Katie Swannell-Gibbs, UK CEO and global head of consulting at Cognition, is a trailblazer in user-centric AI consulting, with a proven track record of delivering profound transformation for clients. Her commitment to challenging conventional consultancy models, championing employee adoption of AI, and advocating for women in tech sets her apart as a driving force for a more inclusive and forward-thinking future with AI.
Kit Cox is Enate’s Founder and CTO. He began coding at the age of 10 and is a lifelong technology enthusiast. Kit’s expertise in engineering and passion for integrating a manufacturing mindset into service solutions have been pivotal in his role at Enate. Under Kit’s leadership, Enate has developed a cutting-edge SaaS platform that transforms business operations. This innovative platform provides complete visibility into processes, helping companies to streamline workflows and ensure efficient delivery. By incorporating the latest in automation and AI technologies, Kit's vision has steered Enate towards helping businesses operate more effectively and adaptively, thus promoting a smarter and agile work environment.
Leo Ryan has over 2 decades of experience working with brands like Unilever, Land Rover, Burberry and GSK to unlock the power of tech innovation. For the past 6 years he has been focussed on helping brands develop practical AI and automation strategies for their marketing teams.
Tim Olsen is currently working as VP of WonderBotz UK. Tim is a pragmatic automation expert with experience of scaling the UK's largest RPA CoE, and specialises in turning around failing automation programmes. He is a passionate futurist and speaks about the Future of Work and AI.
Ted Shelton is an expert Partner at Bain and Company, specializing in automation, artificial intelligence, and software development. His expertise shines in guiding companies across healthcare, manufacturing, and technology sectors towards transformative success.
Sam is currently working as Enate’s Head of AI Research & Development following almost two decades of engineering experience with a focus on innovation and research, specifically in AI and machine learning. He has a passion for solving complex technical problems and delivering solutions that are heavily augmented with AI.
About Enate
Enate is a no code SaaS solution that combines workflow orchestration and AI technologies to help you run operations better.
Those running a large managed service can use Enate's solution to organize, manage and automate tasks, see what's really going on in their operation, assign the right tasks to the right resource and complete work on time.