Business
3 Major Trends That Will Impact the Events Industry in 2020

As is the way with many industries, the events industry is rapidly advancing and evolving to meet the growing demands of consumers. In an industry that is already so dynamic, 2020 is predicted to bring an abundance of exciting developments to the world of events.
Event management teams and planners can prepare and adapt to what’s going to shake up the event industry by staying ahead of the trends. By keeping an eye on what’s predicted to come, you can be sure to plan and manage some unforgettable events this year.
- Attendee Personalisation
Advancements in technology mean that consumers are continuing to expect more personalisation in their interactions during events. Personalisation is now going beyond digital marketing and seeping into event marketing and the nature of functions. While personalisation is not a new term, we are entering a new phase in which planners finding ways to respond to the needs of attendees.
Consumers are expecting more interactive experiences with each event. The key to delivering this experience is enhanced individual personalisation. Traditional event design is structured around the satisfaction of attendees. However, today, event organisers need to deliver on a customised experience by considering the person, professionally and personally, and understanding their preferences and personal value structure.
Tailoring for personal relevance and experience is now a crucial component of event design. In 2020, event managers who refuse to use collected data to deliver tailored and relevant experiences will fail to satisfy the demands of event attendees. According to Sydney event management company Polite, the biggest trend in 2020 will be towards delivering a personalised experience for all event attendees.
- Sustainability
Sustainability is not a new trend; however, it looks like it’s only going to be further stressed this year. Rather than making sustainability an after-thought, event planners are going to need to weave eco-friendly behaviours into the design, planning, and execution of events.
Eco-friendly choices are already being offered in the world of events. Event organisers are making behavioural changes and adapting in what has traditionally been a relatively wasteful industry. Consumers are becoming more aware of sustainable practices and are demanding more from their events to implement such changes. As such, events need to be planned in a manner that will have a minimal environmental impact.
So, how do you make an event sustainable? There are plenty of opportunities to make impactful changes. From switching to digital advertisements and ticketing systems to replacing plastic cutlery and bottles with biodegradable alternatives, using seasonal and local produce for catering, and providing vegan and vegetarian menu options. Events typically are a huge source for landfill and waste, so planners need to consider ways to minimise footprint with each event.
- Artificial Intelligence
Human-centred technology is quickly becoming an important asset to the planning and delivery of a successful event. In today’s exciting phase of digital innovation, technology is advancing to become more human-centric than ever. Tech is being designed with the ability to reach a deep understanding of people, the items they use, where they go, their activities, and the nature of their relationships. Technology has become second nature for so many of us that it is becoming difficult to imagine what life would be like without it. Every resource or tool is smart, optimised, and automated for efficiency.
So, what does this have to do with events in 2020? Consumers are still looking for that human touchpoint with the added abilities of advanced technology. Artificial Intelligence (AI) is a solution that provides a connection between attendees and planners, while strategically relying on technology for event planning efficiency. Here are some ways that events can utilise AI:
- Chatbots
- Collecting Data on Attendees
- Translation
- Process Automation
Business
MetaWorx: Building Full-Stack AI Teams, Not Just Automation

Automation still dominates most headlines, yet the returns often fail to meet expectations. A sprawling chatbot rollout might shave a few support tickets, but it rarely shifts the profit-and-loss statement in a lasting way.
McKinsey’s 2025 workplace survey pegs AI’s long-term productivity upside at $4.4 trillion, but only one percent of enterprises say they’ve reached true “AI maturity.” MetaWorx, a Dallas, Texas-based AI employee agency founded by Rachel Kite, argues that the shortfall has nothing to do with models and everything to do with people.
“Treat AI like a point solution and you’ll get point-solution results,” shares Kite. “You need a roster that can carry the ball from raw data to governance, or the whole thing stalls at the proof-of-concept phase.”
The pod blueprint
When a plug-and-play automation script collapsed under real-world data drift, costing Kite a lucrative contract, she sketched the six-person “pod” that now anchors every MetaWorx engagement:
- An infrastructure architect to tame compute costs.
- A data engineer to secure and shape pipelines.
- An applied scientist to prototype models against live feedback loops.
- An MLOps engineer to automate rollback and retraining.
- A domain product lead translates forecasts into features users actually notice.
- Ethics and compliance analysts to stress test outputs for bias and keep the audit.
The team’s first sprint still delivers a quick-win bot — “small enough to calm the CFO,” jokes Kite — but the roadmap quickly pivots to reliability, explainability, and eventually optimization. By tying every algorithmic decision to a quantifiable business metric, the pods turn AI from a science project into a growth lever.
Recruiting for curiosity, not credentials
With Bain & Company predicting a global AI-skills crunch through 2027, MetaWorx has stopped chasing unicorn résumés. Instead, it hires “adjacent athletes”: a computer-vision PhD who hops from medical imaging to warehouse surveillance, or a former journalist who recasts her nose for story into prompt-engineering finesse.
“Domain expertise expires fast,” Kite says. “What doesn’t expire is the instinct to ask better questions.” The result is a lattice of overlapping skills that stays flexible when models wander into the long tail of edge-case data.
A culture of rapid experiments
Inside MetaWorx, every idea faces the same litmus test: ship something — anything — into a user’s hands within 21 days. The “three-week rule” forces prototypes into the wild early, where failure is cheap and feedback is swift. Post-mortems, including cost overruns, are circulated company-wide, erasing any stigma associated with missteps.
That laboratory mindset powers velocity. “Our first model is almost always wrong,” Kite admits, “but version 1.0 is the tuition we pay for version 2.0.” The philosophy echoes her TEDx talk on resilience: progress is iterative, not heroic.
How leaders can steal the playbook
Executives itching to replicate MetaWorx’s results don’t need a blank check. Kite offers a five-step sequence:
- Inventory pain points, not tools: Walk the P&L line by line and tag the friction you can measure.
- Map the stack to the problem: A recommendation engine, for instance, requires behavior data, retraining triggers, and feedback capture — automation alone won’t suffice.
- Stand up a pod: Reassign existing talent into a cross-functional tiger team before hiring externally; the chemistry test is free.
- Measure the story, not just the statistic: Pair model accuracy with human-scale metrics like ticket backlog or employee churn.
- Budget for the boring: Reserve at least 30 percent of spend for MLOps and governance; Stanford’s HAI review links most AI failures to neglected upkeep.
Taken together, those steps shift AI from a pilot novelty to an operational habit that compounds value rather than topping out after an initial PR splash.
Character still scales faster than code
MetaWorx plans to double its headcount this year, yet Kite insists the secret isn’t a proprietary framework or a monster war chest. It’s credibility. Clients see a founder who has wrestled with the same outages and surprise bills they face. That authenticity converts skeptics faster than any algorithmic novelty.
“Tools level out,” Kite says. “Culture compounds.”
The insight lands in a marketplace still dazzled by generative fireworks. Yes, MetaWorx ships models and dashboards, but its true product is a mindset: resilience over rigidity, questions over credentials, experiments over edicts. In Kite’s world, automation is merely the appetizer. The main course is a full-stack team that knows why the model matters to the business and who owns its success after launch day.
And that, Kite argues, is how AI finally graduates from cost-cutter to growth engine, one curious pod at a time.
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