How Much Does AI Chatbot Development Cost in 2026?

Most founders pose the incorrect query when asking how much an AI chatbot costs: Instead, they should ask, “What functions does my chatbot actually require, and is this the appropriate architecture to meet its requirements?”
Budget overruns often occur between these questions.
2026’s honest answer: A functional AI chatbot development cost anywhere between $5,000 and over $550k, depending on its intelligence requirements, integration points with systems, target user base size (ten or one million users), etc. The global chatbot market is projected to reach $11.8 billion in 2026, growing at a 19.6% CAGR through 2033. About 60% of B2B companies now use chatbot software — and 57% of those companies report significant ROI within the first year. The technology is no longer experimental. It’s a build-or-fall-behind decision.
This guide explores what that range entails, what drives costs at each tier, and where teams tend to overspend or underplan.

What Type of AI Chatbot Do You Actually Need?

There is no ideal answer when it comes to assessing the needed level of the chatbot, but as a rough guideline, the level of needed AI is balanced with the budget constraints. I have broken it down in a table for you below:

One clarification worth making: a generative AI chatbot is not automatically the right choice. For a business that handles five predictable support scenarios, a rule-based bot deployed in three weeks will outperform an LLM integration that took four months to tune. Match the architecture to the problem, not to the buzzword.

Read Also: AI Agents vs AI Chatbot 

How Much Does It Cost To Build an AI Chatbot?

The first step to understanding the budget for AI chatbots is establishing a classification for different types of chatbots. Budgeting for the chatbot also includes design functionality, level of complexity, connectivity, and embedded features.

Rule-Based Chatbot: $5,000–$15,000

Basic chatbots are the ones that cost the least because they are not especially complex. Basic chatbots are equipped with basic logic, where they are programmed to respond to questions, help navigate complex interfaces, and complete some actions like booking a support request.

AI-Powered Chatbot: $15,000–$100,000

Moderate chatbots are a bit more complex and also more expensive. They incorporate a bit more of the advanced machine learning and natural language processing technologies that help improve the understanding of user-directed queries. They also facilitate a more personalized user interaction and assist in handling complex user-related tasks.

Enterprise-Grade Custom Chatbot: $20,000–$500,000+

Custom chatbots in the price range listed above can offer deceptively enormous multi-use chat capabilities coupled with intricate multi-tool/complex multi-layered AI/bot builds and integrated multi-user complex security systems.

Costs of enterprise AI chatbots can be attributed to the fact that the systems being offered are fully customized, purpose-built, highly complex multi-use chat systems that can be designed for large-scale operational capabilities.

Key Factors That Drive Chatbot Development Cost

AI chatbots are unique in cost and can vary based on features selected, anticipated integrations, and desired level of intensity.

Being aware of key factors can help you set a more practical budget and reduce the risk of overages during development.

Chatbot Type and Complexity

Depending on the price range, basic chatbots can cost between $5,000 and $15,000, and AI chatbots can cost over $25,000 and even over $100,000.

For more advanced chatbots and/or more advanced features, costs will increase. Enterprise-level automated chatbots, paired with integrated analytics, will range from $100,000 to $500,000+. Chatbot complexity and cost are directly proportional.

Number of Integrations Required

The more complex a chatbot is, then an automated bot with CRM, automated payment systems, and complex API integrations, the more budget you’ll need, typically a range of $5,000-$20,000+.

There will be proportionately more costs across the design, testing, and maintenance activities of enterprise applications that have complex integrated systems.

Custom Training vs Pre-Built LLM APIs

If you opt to use pre-configured APIs, like OpenAI, then the budget will be approximately $10,000-$40,000.

Custom AI training, on the other hand, can more than double the cost, running about $50,000-$150,000+ for the data, training, and the infrastructure. This is also a big percentage of the cost of enterprise AI chatbots.

UI/UX Design Complexity

The cost of UI/UX design for simple chatbot interfaces ranges from $1,000 to $15,000. Advanced chatbot interfaces with custom UI/UX design and cross-platform design add $5,000 to $25,000 to the budget. If cross-platform design and custom UI/UX design are required, the cost of developing an AI chatbot with an advanced customer-facing app will increase.

Platform 

The cost of design directly correlates with the design of the chosen platform. Including a web-only chatbot design can cost $5,000 to $20,000. The cost of design further increases to $15,000 to $50,000 to support mobile app design (iOS/Android). 

For web and mobile support, the cost of AI chatbot design further increases to support additional development, testing, and optimization when cross-platform support is required.

Compliance Requirements 

Compliance adds a high cost to chatbot development. The implementation of compliance frameworks, be it HIPAA, GDPR, SOC 2, etc., coupled with security concerns, can increase costs by $10,000–$40,000+. 

There is a revised focus on data security, including encryption, access controls, and auditing. For both the healthcare and enterprise sectors, compliance mandates are an absolute, not relative, consideration and are a large factor in price. 

Development Team Location

Price is a function of the location of the Development Team. For less developed developing countries like India, the costs are somewhat reasonable, and as is the level of quality of the end product, with a developer pricing range of $20 – $50.

In the U. S and about every other country in the developed world, the development teams are both more capable and more expensive, with a price range of $80 – $150 and exceptionally more expensive in Europe. This is a significant impact on the total cost of developing the chatbot.

Ongoing Maintenance and LLM API Costs

Development cost is the initial investment after the ongoing cost of maintenance, updates, monitoring, and API costs. On average, maintenance costs are about 25 to 30% of the development cost.

Should the LLM OpenAI be significantly used, the cost can be estimated to be between $1000 and $20,000. These costs are high.

AI Chatbot Development Cost Breakdown by Stage

The total cost to develop an AI chatbot isn’t all spent at once; it’s spread out over several phases of development. Each step is important for making a chatbot that works well and can grow.

This breakdown helps you better manage your budget and get a better idea of how much it will cost. 

Discovery and Planning

Discovery and Planning is the phase most teams underinvest in. A week of structured planning — mapping user flows, defining conversation states, and scoping integrations — prevents weeks of rework downstream. Skip it, and you’re trading a small upfront investment for a much larger mid-project course correction.

UI/UX Design

The design phase focuses on how people will interact with your chatbot. This includes chat flow, interface design, and user experience. 

A simple chatbot design is cheaper, whereas a complex UI with specific flows is more expensive. This stage typically consumes 10-15% of the budget, depending on complexity and platform (web or mobile).

Backend Development

Backend Development will consistently be the highest single cost. More integrations, more complexity, more time. There’s no way to compress this without reducing scope — which is often exactly the right call for an MVP.

LLM Integration and Fine-tuning

If your chatbot incorporates AI models such as GPT, this level entails connecting APIs, training models, and fine-tuned answers. 

Using pre-built APIs saves money, but custom model training costs more. This phase typically accounts for 15-25% of the entire expense, depending on the complexity of the AI.

Testing and QA

Testing guarantees that your chatbot runs smoothly and without mistakes. It includes performance testing, security checks, and bug fixes. 

This stage accounts for 10-15% of the cost. Proper testing helps to avoid difficulties after launch, reducing cost.

Deployment and DevOps

Once completed, the chatbot is deployed on servers or cloud platforms. This comprises configuration, monitoring tools, and performance optimization. 

This stage normally costs between 5 and 10% of the entire expenditure. Cloud infrastructure and scaling requirements may somewhat raise the cost.

Post-launch Maintenance

Post-Launch Maintenance is often missing entirely from initial estimates. Budget 10–20% of the original build cost annually for updates, model retuning, dependency management, and infrastructure monitoring.

Build vs Buy vs Use No-Code: Cost Comparison

A key decision when creating a chatbot is determining the way to build it. Starting from scratch, using no-code tools, and building on top of APIs are all options developers can choose.

Flexibility, cost, and value are affected differently based on the options chosen by the businesses. Budget, timeline, and business targets are all aspects affecting which option is ideal.

No-Code Chatbot Builders

No-code chatbot builders are very quick and affordable alternatives. You do not need to know how to code a chatbot. These platforms typically demand a monthly subscription, which makes them affordable to small firms. 

However, they offer limited customization and may not enable complex AI functions. If you are wondering how much a chatbot costs for basic use, this is the cheapest choice.

• Tidio: An easy chatbot builder for small businesses that includes live chat and automation tools.
• Intercom: A customer messaging platform that includes chatbots, support tools, and user engagement capabilities.
• Drift: A conversational marketing platform for lead generation and real-time chat automation. 

API-Based Integration

This strategy helps to employ AI APIs to create smarter chatbots. You gain more versatility than no-code tools, as well as advanced capabilities such as natural conversations and automation. The pricing here includes both development fees and API usage charges. This is a popular choice among entrepreneurs since it strikes a balance between cost and performance.

• OpenAI: Offers advanced AI models such as GPT for natural language understanding and chatbot answers.
• Anthropic: an AI platform that focuses on safe and reliable conversational AI models like Claude.
• Gemini: Google’s AI model built for multimodal understanding and chatbot skills. 

Custom Built From Scratch

Custom chatbot development allows you complete control over the features, design, and integrations. This choice is the most expensive, but it provides high performance. It is perfect for companies that require advanced processes, security, and interaction with internal systems. This is where enterprise AI chatbot development costs come into play.

Which option is best for your US startup?

If you are a startup on a tight budget, start with no-code or API-based solutions. They allow you to launch faster and test your idea. As your business expands, you may upgrade to a custom chatbot for improved performance and scalability.

Your goals will determine which option is best. If you desire speed, choose no-code. If you need flexibility, use APIs. If you want complete control, choose bespoke. 

LLM API Costs: What You’ll Actually Pay in Production

Token usage is the cost that surprises teams most. Every input and output your chatbot processes is billed in tokens. At meaningful user volumes, this becomes a significant monthly line item.

Pricing current as of mid-2026. Verify current tiers directly with each provider before budgeting.

How to Estimate Your Monthly Token Bill

A practical calculation:

• 1,000 daily users × 10 messages per session × 500 tokens per message = 5 million tokens/day
• Monthly usage: ~150 million tokens
• At mid-tier GPT-4 pricing: $1,500–$4,500/month in API costs alone

Run this calculation early — before you commit to a provider or architecture. The numbers change significantly based on average session length, system prompt size, and output verbosity. Caching strategies and prompt compression can reduce token costs by 20–40% once you’re in production.

AI Chatbot Cost by Industry: US Startup Examples

Building an AI chatbot has different costs depending on the industry. The differences lie mainly in the different types of compliance, integration, and security standards.

Healthcare Chatbot Cost ($30,000–$200,000)

HIPAA compliance is non-negotiable and expensive. A healthcare chatbot requires encrypted data transit and storage, audit logging, BAA agreements with all infrastructure providers, and EHR/EMR integration that follows strict interoperability standards. Budget $20,000–$40,000 for compliance implementation alone, separate from the core build.

Fintech Chatbot Cost ($25,000–$150,000)

Financial chatbots handle data that regulators scrutinize closely. KYC, AML, and PCI-DSS requirements drive up both engineering cost and audit overhead. Advanced fintech chatbots with fraud detection and transaction automation sit at the higher end of this range.

The compliance standards and data security requirements are the main reasons costs are higher.

E-Commerce ($10,000–$100,000)

The most accessible tier for non-regulated industries. Basic product recommendation and order management bots can be built cost-effectively. AI-driven personalization engines with inventory integration and multi-channel deployment push the upper range.

SaaS Customer Support ($15,000–$120,000)

The value case here is clear: reduce tier-1 support load, automate onboarding, and decrease time-to-resolution. Costs scale with CRM complexity, knowledge base integration depth, and the sophistication of handoff logic to human agents.

HR and Internal Operations ($10,000–$80,000)

Internal chatbots face fewer compliance constraints but often require deep HRIS integration. They’re typically faster to build and maintain than customer-facing equivalents, making them a practical first deployment for organizations new to the space.

OpenAI GPT-4 API Pricing

OpenAI GPT-4 models are powerful but expensive. Pricing is around $10 – $60 per million tokens for input, output, or both.

Production apps will show monthly expenses from $500 to $5,000+, dependent on app usage. This expense calculates the cost of building an AI chatbot at scale.

Anthropic Claude API Pricing

Claude is known for having the ability to converse for longer periods of time, especially regarding safer output. Pricing is slightly better than GPT-4, at around $8 to $50 per million tokens. These expenses build up to $400 to $4,000/month, making it a cost-balanced option for medium-level usage.

Google Gemini API Pricing

Pricing becomes even more competitive for Gemini for companies that are used to utilizing Google Cloud. Pricing can be found from $5 to $40 per million tokens, making it quite affordably priced. Depending on utilization, monthly expenses can range from $300 to $3,000+.

Open Source Alternatives (Llama, Mistral) — Self-Hosted Cost

With self-hosted models, there are no token payments to be made, but you do have to pay for the upkeep of the infrastructure. Cloud server upkeep can cost $500 to $3,000+ a month, and while there are no API costs, funds will be used to cover the setup and upkeep of the AI chatbot systems.

How to Estimate Your Monthly API Bill

To estimate your cost, consider:

• Number of users per day
• Average messages per user
• Tokens used per message

Example:

• 1,000 users/day × 10 messages × 500 tokens = 5M tokens/day
• Monthly usage = ~150M tokens

Depending on the provider, this can cost anywhere between $500 to $5,000+ per month.

AI Chatbot Development Team Structure and Rates

Building a chatbot requires a team of specialists. Each role adds to the overall AI chatbot development cost, and rates vary based on location, like India vs the US.

Project Manager

The project manager plans and executes the chatbot project from start to finish, ensuring all teams stay aligned and communicate areas of blame as necessary. The project manager’s rate also impacts the overall cost of creating the chatbot, as long and complex projects will likely require more of their time.

AI/ML Engineer

This is one of the most significant roles. AI/ML engineers are responsible for developing the models, and the cost for developing an AI chatbot increases significantly for complex or enterprise solutions.

Backend Developer

Backend developers take care of server-side logic and code the APIs and integrations. More integrations will require more development and will therefore increase the overall cost of developing the AI chatbot.

Frontend/UI Developer

They are responsible for designing the chatbot user experience, and while a better UX/UI will ultimately result in a more usable chatbot, it will increase development time and cost.

QA Engineer

This role ensures the chatbot is functioning as designed. Testing appropriately runs help avoid post-launch issues and build cost savings.

DevOps Engineer

The roles and responsibilities of DevOps engineers are the scalability of the system and the system deployment. There is little flexibility in the system, which increases the overall cost for maintaining infrastructure.

Prompt Engineer

This is the most critical role for chatbots, as prompt engineers improve how chatbots provide responses. Having this role will significantly help, but not ultimately dictate, how well and how fast the chatbot works.

Hidden Costs US Startups Always Forget

AI chatbot development costs often only include direct development costs. Creation, though, only accounts for part of the budget. There are several direct and hidden costs that need to be considered, and if the budget ignores these costs, the project will not be successful.

LLM API Usage at Scale

Some budgets account for API costs and expect growth pricing to be manageable due to limited API calls and user session growth. It happens until the chatbot gets a user.

The budget could shift from a few hundred dollars to $2,000–$10,000+. This is the highly variable cost of chatbot development due to its heavy user growth, which is expected.

Vector Database and Embedding Storage

If your AI bot has a memory and is able to perform document searches, then you will require a vector database. This leads to more hidden costs of chatbot development.

Services such as Pinecone cost to both store and to search the stored data. Costs can span between $100 and $1,000+ monthly.

Security Audits and Compliance

First, encryptions and audits are required in certain industries, such as fintech and healthcare. If you fall into these industries, you will need to comply with regulations such as HIPAA or GDPR.

This will add an additional $5,000 to $30,000 to your total budget, but you will also need to continue to spend on audits post-development.

Retraining and Fine-Tuning Costs

AI models require constant improvement even after deployment. For proper personalization or enhancement, retraining and fine-tuning are necessary. To incorporate necessary user data, new tools or infrastructure may be needed.

The cost for this may be between $5,000 to $50,000 or more, depending on the specifics.

Human Oversight and RLHF

To improve the performance, a human’s checking of the chatbot response is frequently needed. Teams monitor the conversations and see how to make necessary changes. 

Reinforcement Learning with Human Feedback (RLHF) leads to increased recurring costs, priced between $1,000 to $5,000 or more, based on how much it is in use.

Cloud Infrastructure Scaling

Your cloud infrastructure must match the growth of your chatbot’s capabilities. Hosting, servers, and cloud services (AWS, Azure, and GCP) will cost between $500 and $5,000 or more per month. This will be more expensive as your app receives more traffic and real-time data.

How Long Does It Take to Build an AI Chatbot?

Building an AI chatbot involves time and effort focused on a chatbot’s complexity, features, and integrations. Whereas a simple chatbot may require less time, an advanced chatbot may require more time in the three core areas.

Simple Rule-Based Bot: 2 to 4 Weeks

Since rule-based bots depend on predestined logic, building a simple bot is the most straightforward. Simple bots may answer the most basic questions and simple FAQs, requiring less coding and time. Since these bots require no AI, the overall cost to build a rule-based chatbot may be the least as well.

NLP Chatbot: 1 to 3 Months

NLP chatbots take the longest to build due to the necessary training to understand more complex communications and user intents. From this aspect, the development time and overall cost to build an AI chatbot increase. For organizations that want to improve user experience and, similarly, communications, this is a recommended option.

LLM-Powered Custom Bot: 2 to 5 Months

LLM-powered chatbots take the longest and are the most complex as they employ more advanced artificial intelligence (AI) models. Compared to the previous options, development in this case includes more complex tasks and takes up more time due to advanced customization. The cost to build AI chatbots in this case is the highest as well.

Enterprise Multi-Agent System: 4 to 9 Months

Developing an enterprise chatbot is an intricate endeavor because it combines multiple agents, integrations, and workflows. Therefore, it involves elaborate design, development, and testing. The development time and costs are significant, along with the costs for enterprise AI chatbot development, but the system is highly scalable and performs as efficiently as possible.

These timelines assume a clear scope, available integration documentation, and a team that’s done this before. Scope creep — adding features mid-build — is the single most common reason projects run 40–60% over timeline.

How to Reduce Costs Without Cutting Quality

Chatbot development doesn’t have to drain your budget. There are methods you can take to lower your AI chatbot development cost without compromising your performance and user experience. Here are a few ways you can achieve this.

Start With an MVP Chatbot

You can begin your chatbot project without developing the entire product. Start from the building blocks, and develop only the basic features such as the rudimentary basic answers and a limited number of use cases. This approach can minimize the initial development cost of an AI chatbot.

Once the chatbot has users due to its basic utility, you can build and enhance the other areas and features of the product from their feedback. This approach helps avoid wasting money on features that have no utility.

Use Pre-Built LLM APIs Instead of Training From Scratch

Training your own autonomous AI model is an expensive and lengthy process. The use of pre-built LLM APIs, such as OpenAI and similar AI platforms, can provide an instantly ready-to-use product.

This can reduce the overall development of your chatbot. While you will incur a cost depending on the number of calls your chatbot gets in a given amount of time, the cost is significantly lower than the in-house chatbot development and model maintenance.

Offshore Development for Engineering Work

Outsourcing services to India or Eastern Europe may provide savings without a loss in service quality. These developers charge less than U.S. developers.

As a result, overall costs may decrease by 30%-60%. This strategy is how most companies balance costs with quality.

Open Source Frameworks (Rasa, LangChain, Botpress)

Using an open source framework avoids having to pay licensing fees. Rasa, LangChain, and Botpress are examples of powerful and inexpensive frameworks in bot development.

The cost for developing an AI chatbot with these frameworks is minimized, which is beneficial for startups. While you will need to pay for development, the frameworks will cost less than development on the other platforms.

Phased Feature Rollout

You can have a feature breakdown and give it to your team in phases.

Start with the basic functions and then introduce the advanced ones. This method is especially useful for startups with small budgets; it helps distribute the costs over a period of time.

This method is good for building the cost optimization of your enterprise AI chatbots while gradually enhancing your product.

What to Look for When Hiring an AI Chatbot Development Company?

When choosing an AI chatbot development company, the quality, cost, and success of your project all come from the ability to choose the correct partner for your AI planning challenges.

Experience With LLM Integration

Ascertain the degree to which the company has engaged with AI models like GPT, Claude, etc. This also gives you the capacity to develop scalable and sophisticated chatbots. Compared to seasoned professionals, risks stemming from a lack of experience increase the development costs.

Industry-Specific Compliance Knowledge

In high-stakes situations, a lack of diligence on your development firm’s part to learn and implement all of the necessary controls and regulations (e.g., HIPAA, GDPR, and KYC) is a sure way to cause an increase in development costs down the road from all of the necessary security and compliance updates.

Post-Launch Support Model

A chatbot is never a one-and-done tech product. The company you choose should have a lot of flexibility to cover different options and aspects in its client support. You should be cognizant of what ongoing support covers, as it will impact your future budgets & expenditures.

Pricing Transparency

Choose without exception a reputable company that is only partially explicit with client pricing. AI chatbot development costs can unexpectedly spike with the addition of fees that were described in detail in your contract. Pricing from a firm helps you to be financially prudent.

What to Look for When Hiring an AI Chatbot Development Partner

Demonstrated LLM integration experience. Ask for specific examples — not a portfolio page, but a conversation about what models they’ve used, what prompt engineering strategies worked, and what didn’t. Teams without real LLM experience will cost you more in rework than their lower rate saves you.

Industry-specific compliance knowledge. In healthcare or fintech, a team that learns HIPAA or KYC requirements on your project is a liability. Look for teams that can reference prior compliant deployments.

A clear post-launch support model. Chatbots aren’t a launch-and-leave product. Your partner should offer a defined SLA for bug resolution, model updates, and infrastructure monitoring — and that support structure should be priced transparently in the contract.

Pricing that includes the full scope. The most common disappointment in chatbot projects: a fixed-fee quote that doesn’t include integration work, compliance requirements, or post-launch maintenance. Before signing, confirm that the quote covers everything that will actually be billed.

Questions to Ask Before Signing

Before you make a decision, ask essential questions like:

• What kinds of AI models and technologies will you use?
• What does the overall cost include?
• How do you deal with scaling and upkeep?
• What is the expected time frame?

You can be sure you are making the right choice by asking yourself these questions to learn the true AI chatbot development cost. 

Conclusion

Building an AI chatbot in 2026 is not a single-cost decision — it’s an investment with a build cost, an operating cost, and an optimization cost that evolve. The range of $5,000 to $500,000+ isn’t vague; it’s honest. Where you land in that range depends almost entirely on what you’re building, who for, and how much intelligence the problem actually requires.

The key will be to determine the best option based on your company’s preferences, not the lowest. For best budget management, consider starting with an MVP, utilizing APIs, and being prepared for additional costs.

At GMTA Software, we help businesses move from requirement to roadmap without wasting the first half of their budget on scope they don’t need. If you’re planning a chatbot build in 2026 — whether it’s a lean NLP integration or a full enterprise deployment — we’ll give you an honest estimate based on what your use case actually requires.

Frequently Asked Questions

How much does it cost to build an AI chatbot?

Between $5,000 and $500,000+, depending on complexity. A rule-based bot costs $5,000–$15,000. An LLM-powered chatbot with custom integrations typically runs $50,000–$200,000. Enterprise multi-agent systems with deep workflow integration start at $150,000 and scale from there.

How long does it take to develop an AI chatbot?

A rule-based chatbot can be production-ready in 2–4 weeks. An NLP chatbot takes 1–3 months. An LLM-powered custom build typically takes 2–5 months. Enterprise systems with multiple integrations run 4–9 months from kickoff to launch.

What factors affect development cost the most?

Ans. Key factors include chatbot type, integrations, AI model usage, UI/UX design, and compliance requirements. 

Is it cheaper to use APIs instead of building from scratch?

Significantly, yes. A pre-built API integration runs $10,000–$40,000 in development costs. Training a custom model from scratch starts at $50,000–$150,000 before any interface or integration work. For most businesses, APIs are the right starting point — custom training is an optimization for later.

What are the ongoing monthly costs of running an AI chatbot?

Expect $500–$10,000+/month in total operating costs, depending on traffic and architecture. That includes LLM API usage ($300–$5,000+), cloud hosting ($500–$5,000), vector database storage if applicable ($100–$1,000), and maintenance support.

What factors drive chatbot development costs the most?

The five biggest drivers are:

• Chatbot type and intelligence level
• Number and complexity of integrations
• Compliance requirements
• Whether you use pre-built APIs or custom model training
• Development team location.

What’s the most underestimated cost in chatbot projects?

LLM API usage at production scale. A chatbot that costs $200/month in API fees during beta can cost $5,000–$10,000+/month once it’s serving real user traffic. Model this before you finalize your architecture.

Uday Shekhawat

Uday Singh Shekhawat is a skilled Content Writer and Technology Researcher with 9+ years of experience creating in-depth, SEO-driven content for the technology and software development space. At GMTA Software, he focuses on translating complex technical concepts into clear, informative, and actionable content for founders, CTOs, and business leaders.