Automating business processes through software is certainly not new – technologies like robotic process automation (RPA) have led the charge on that front over the past decade. But AI-based generative models like large language models (LLMs) are now set to radically expand the scope and transform the very paradigm of automation.
With capabilities like natural language generation, image creation, predictive analytics and hyper-personalization, generative AI can help automate many tasks that evaded automation previously. This is driving tremendous momentum with adoption expected to accelerate through 2023 across sectors.
The Generative AI Approach to Intelligent Automation
Traditional automation involved hard-coding software routines to mimic specialized human tasks. But this could only extend to repetitive, rules-based workflows. Generative AI however takes a more flexible approach – by examining vast datasets, it discerns complex patterns to make predictions, recommendations, judgments similar to humans.
So instead of needing rigid programming, generative models can receive natural language descriptions of tasks and context to automate accordingly. This ability to dynamically adapt makes generative AI uniquely suited for replicating subjective expertise and skills, unlike previous waves of automation.
According to research by McKinsey, technologies like machine learning, natural language processing and robotic process automation could automate 45% of activities people get paid to do across diverse industries. That automation potential rises to over 55% when factoring advances like generative AI.
Key Business Benefits Driving Adoption
The benefits of harnessing generative AI to transform enterprise automation are already clearly demonstrated in early use cases:
1. Boost Productivity & Efficiency
- Automating repetitive tasks allows staff to focus on value-added work. Studies indicate productivity gains between 20-50%.
- For transactional processes, time to complete can reduce by over 40% via automation.
2. Reduce Operations Cost
- 25-50% cost reduction observed from automating high-volume business processes.
- Lower human capital needs over time as roles evolve alongside AI. Cost per full-time-employees falls.
3. Drive Revenue Growth
- 45% faster lead conversion rates achieved via hyper-personalized sales & marketing automation.
- 60%+ increase in repeat purchases from individualized customer experiences.
4. Mitigate Risk
- 90% decline in process errors and failures enabling quality improvement.
- Oversight of all automation via audit trails and AI explainability techniques.
Adoption rates continue rising sharply – an IDC survey of IT leaders reveal almost 70% of organizations now have some workflows automated with AI. Let‘s examine the top use cases driving spend.
Top 5 Use Cases Where Enterprises Automate with Generative AI
Generative AI offers vast potential to enhance productivity across domains – from automating coder workflows to highly personalized medicine diagnostics one day.
But in the business context currently, 5 leading use cases make up over 75% of adoption:
1. Automate Document Processing
Converting unstructured data like scanned invoices or claims forms into structured, actionable data at scale has been a huge automation challenge historically.
But modern language models can now ingest documents and extract relevant information with 99% accuracy. This powers automated workflows like:
- Classify and route 10,000s of daily invoices to correct teams
- Review insurance claims and auto-process repeat submissions
- Extract insights from 1,000s of customer feedback forms
By 2025, 45% of data extraction tasks will be automated using generative AI per Gartner.
2. Enable Hyper-Personalized Marketing
Generative AI is enabling mass personalization of the entire marketing value-chain – personalized ads at population-scale are now viable both economically and operationally.
- Create Ad Content – AI copywriters generate 1,000s of tailored ads that resonate
- Optimize Campaigns – Continuously A/B test micro-segments on performance
- Recommend Relevant Products – Individualized suggestions that customers love
With individual-level personalization, conversion rates on automated campaigns can exceed 60%.
3. Automate Customer Service
Answering predictable customer queries like order status, refunds, etc. can be fully automated using chatbots. Natural language capabilities also allow bots to parse complex requests before auto-responding or escalating those to humans.
Use cases like virtual assistants and customer support chatbots will cumulatively save businesses over $11 billion annually by 2024 as per Juniper Research. Generative AI unlocks the more advanced implementations here.
4. Accelerate Content Production
Nearly 80% of online content could be machine-generated by 2030 per industry estimates. Even now, enterprises use generative AI to rapidly produce all types of digital content including:
- Tailored landing pages for campaigns, events or promotions
- Intelligent market research summaries analyzing all available data
- Auto-translated chatbot responses for global audiences
Output volume grows exponentially while freeing up internal content teams for more creative messaging.
5. Digitize Manual Documents
In insurance, financial services and healthcare, documenting domain knowledge into manuals, handbooks and forms underlies service delivery. But production bottlenecks plague the process.
Modern language models can ingest existing collateral to:
- Auto-produce updated policy documents, procedural guidelines
- Dynamically generate forms and questionnaires
- Synthesize insights from medical corpus into reference manual
This helps scale niche vertical expertise across the business through digitization. 30-50% of employee time spent producing such documentation can be saved.
Industry Adoption Trends and Forecasts
While still early days, functional domains leading automation adoption with generative AI include customer service, marketing, finance and HR.
High-Tech and BFSI sectors show the fastest uptake. In high tech, over 50% plan aggressive automation powered by AI like ML, NLP and predictive analytics per IDG. While in BFSI, spend on cognitive/AI software to enhance operations will grow at nearly 20% CAGR until 2025 according to Mordor Intelligence.
Current vs Projected Adoption by Company Size
Analyzing adoption trends by company size reveals that currently mid-sized companies dominate automation usage driven by generative AI:
| Company Size | % Adopting Now | % Projected in 2 Years |
|---|---|---|
| Under 100 staff | 23% | 48% |
| 100 – 1000 staff | 31% | 63% |
| 1000 – 5000 staff | 37% | 69% |
| Above 5000 staff | 29% | 58% |
Table: % of companies per size class adopting automation with generative AI currently and forecasted (AIMultiple Research)
Mid-market is bullish because automation provides big productivity upside. Larger corporations plan investments but move cautiously. Overall, adoption span across company sizes will converge around 60% industry-wide within 2 years as solutions mature further.
Cost Savings Achievable Through AI-Driven Automation
Determining ROI is key driver for funding automation projects. Exact savings achievable depend significantly on which processes get automated. But data aggregated across use cases reveal:
- 20-30% reduction in operations costs by automating customer-facing processes like sales & support
- 8-12X ROI over 5 years from marketing automation through generative AI
- 40-60% lower administrative overheads via automating back-office document workflows
In dollar terms, this could translate roughly into:
| Process Automated | Annual Savings Range | Assumptions |
|---|---|---|
| Customer Service | $620,000 – $850,000 | 1000 support tickets/day @ $8/ticket |
| Marketing Campaigns | $950,000+ | 5000 leads generated monthly via automation |
| Claims Processing | $240,000 – $360,000 | 300 claims daily @ $5/claim admin cost |
Table: Modelled cost savings ranges achievable via automation per key business process (Sample company data)
Exact returns depend on current operational maturity, where automation is applied and execution strategy. But well-targeted automation using AI promises material cost takeout.
How to Successfully Automate with Generative AI
While promising conceptually, effective adoption requires concerted efforts spanning technology, people and process:
1. Complete Pilots Showcasing Impact
The most compelling driver for securing investments in automation is demonstrating value through quick but telling pilot projects. Some best practices include:
- Target high-impact processes primed for automation
- Keep scope narrow, metrics unambiguous
- Plan for test period covering seasonal variability
- Arm frontline teams to lead the pilot themselves
Success here unlocks sponsorship for larger production rollouts.
2. Continuously Retrain Models
Unlike traditional code, machine learning models need periodic retraining to ingest new data and prevent inaccurate drift over time. Key tactics include:
- Schedule monthly retraining runs with updated real-world data
- Automate feedback loops to dynamically improve models
- Refresh models after changes in related systems or processes
- Ensure ready access to large, high-quality datasets
Ongoing model maintenance is integral for sustained performance.
3. Coach Teams on Working with AI
The greater automation promise lies in AI augmenting staff rather than replacing them. But this requires teams skilled in leveraging AI tools seamlessly:
- Encourage reverse mentoring where junior analytic talent coach seniors
- Incentivize sharing AI knowledge across business units
- Train end-users hands-on in interpreting model outputs
- Foster a collaborative culture around human-AI partnerships
Upskilled workforces will be the ultimate competitive edge here.
Getting the operating model right is key – Generative AI solutions should seamlessly integrate across platforms like data warehouses, CRMs and cloud infrastructure. Core business logic stays configurable for custom needs. And implementation rollouts should span across regions using agile delivery models to accommodate variances.
Addressing the Ethical Imperatives
Automation enabled by AI does raise reasonable concerns over its risks beyond just business impacts. As generative models permeate across functions, we must address issues like:
Transparency & Explainability
In regulated sectors especially, having transparency and audit trails around automation decisions is mandatory. Tactics like keeping humans in the loop and requiring AI explainability help instil trust.
Responsible Data Usage
Generative models are notoriously data hungry – the credibility of their outputs depends directly on consumption of high-quality, representative data during training. Misuse of personal or sensitive data is unacceptable.
Job Displacement‘s Impact on Social Equity
Automating a wider range of jobs does risk disadvantaging segments of society lacking the skills to transition to new roles. Extensive re-skilling programs are vital to minim minimise inequity.
Inclusive governance of AI and earnest redressal of its ethical pitfalls ultimately needs to run parallel to its rapid tech advancements for responsible innovation.
The Outlook for Intelligent Automation
Generative AI proposes to fundamentally expand business automation possibilities in the next decade. As models mature, anything requiring human-level pattern recognition, reasoning or creativity has the potential for augmentation if not outright automation.
While still early stages, significant year-on-year gains in model accuracy across benchmarks promise plenty headroom for innovation. And modularity allowing plug-and-play integration with enterprise IT landscapes will accelerate adoption.
Between elevated ROIs and multiplying use cases, IDC forecasts worldwide spend on AI automation to deliver 3X ROI overall and exceed $190 billion annually by 2025.
Automation powered by generative intelligence looks positioned to drive the next productivity leap across sectors. But its responsible adoption will shape whether the ensuing transformation unlocks more harm than good. Successful integration needs to simultaneouly prioritize people and technology issues to realise full upside.
