You’re looking to deploy a chatbot?
You’re in luck.
We’ve helped companies deploy over 750,000 AI agents (yes, for real).
So we’re pretty familiar with what makes for a successful chatbot implementation project.
Using a chatbot for business is a hot topic – and for good reason. Chatbots are the fastest-growing communication channel for brands.
They’re cost-efficient, allow companies to scale, and users are increasingly looking for digital messaging options.
But it’s hard. Companies make a lot of mistakes in chatbot deployment. In reality, this is a complex software project that your team needs to properly invest in.
Let me guide you through the steps that our Customer Success team uses for our enterprise chatbot clients.
1. Assess strategic alignment with the AI project
Your first step should always be mapping how a chatbot will align with your company’s existing strategy goals.
We see this mistake a lot: “We need a chatbot because we have AI on our roadmap.”
Wanting AI for the sake of it doesn’t set you up for success. It’s fine to start there – but figure out the point of it.
Luckily, as long as you have strategic goals, this isn’t hard to do.
Identify strategic goals
You can start by listing out your company’s strategic goals (if you don’t have an existing document).
Goals might look like:
- Increased efficiency and productivity
- Better customer experience
- Cost reduction
- Improved regulatory compliance
- Enhanced decision-making
Assessing AI contribution to strategic goals
Once the company’s goals are agreed upon by those who need to agree, you can conduct a short audit to figure out how your AI investment will tangibly impact those goals.
Our Customer Success team does this a lot. Like, all day, every day.
They’ve broken down this alignment audit into 6 questions for your team to align on.
1) Problem identification
Ask: What are the specific problems or opportunities that AI will address?
For example, we audited a Global 2000 tech company that was experiencing significant productivity dips year over year.
They identified five key areas that were sapping productivity: employee experience and engagement, internal tech support, global knowledge management, and customer onboarding.
Through correct problem identification, they were able to combat lessening productivity by automating information dissemination and service delivery across these processes.
2) Financial Impact
Ask: What are the financial implications of AI adoption? How will ROI be measured?
Measuring costs against savings and efficiency gains provides a clear financial impact analysis.
For example, we saw a logistics company that projected the ROI of implementing AI-driven route optimization to prepare for their initial pitch to management.
(They found that the initial investment was offset by a 20% reduction in fuel costs within the first year.)
3) Risk Management
Ask: What are the potential risks and how will they be mitigated?
Example? A healthcare provider identified data privacy as a major risk in their AI implementation plan. They developed robust encryption protocols and staff training programs to mitigate these risks, ensuring patient data remained secure.
4) Implementation Feasibility
Ask: What’s the timeline for AI deployment? What resources are required?
For example, one of our clients found that a phased implementation over 18 months, with iterative testing and adjustments, was essential for the success of their AI-driven customer service chatbot.
5) Cultural and Organizational Fit
Ask: How will AI affect organizational culture and employee roles?
Take an automotive company that implemented AI for predictive maintenance.
They conducted extensive training and workshops to ensure employees were comfortable with the new technology, leading to a smoother transition and higher employee engagement.
6) Technology and Data Readiness
Ask: Is the current technology infrastructure and data ready for AI implementation?
A telecommunications firm found that its existing data was siloed and inconsistent.
They undertook a comprehensive data cleaning and integration process before AI deployment, ensuring the AI models had access to reliable and comprehensive data sets.
2. Conduct an AI readiness assessment
Your company wants AI – but are you prepared for it?
An AI readiness assessment will help your team identify where you need to improve before investing in AI.
We see a ton of companies that start spending money before they’re really ready to accomplish anything.
So set yourself up for success with a formal assessment.
1) Strategy
Before embarking on your AI journey, it is crucial to have a clear and coherent strategy that aligns with your overall business goals.
This involves defining the specific problems you aim to solve with AI, identifying potential use cases, and understanding the expected impact on your business.
A well-defined strategy should outline the long-term vision for AI integration, including the roadmap for scaling AI initiatives across the organization (I can help you out with that below).
Ensure that there’s strong leadership commitment and a strategic alignment of AI projects with business objectives.
Strategy gap analysis questions:
- Who owns the company’s AI strategy?
- Is the AI initiative aligned with strategic goals?
- Is there a roadmap in place for scaling AI across the organization?
2) Infrastructure
A robust and scalable infrastructure includes the technological foundation required for AI development, deployment, and ongoing maintenance.
The infrastructure should support the necessary tools and platforms for AI model development, testing, and deployment. Key components might include computing power, storage, and network capabilities.
Investing in the right infrastructure ensures that your organization can handle the computational demands of AI and scale operations as needed.
Infrastructure gap analysis questions:
- Does the organization have sufficient, dedicated GPU resources available?
- Are they available and integrated for processing AI workloads?
3) Data
Assessing your data readiness means investigating the availability, quality, and accessibility of data required for training and deploying the AI models.
This includes data management practices and data governance policies, not only for the initial deployment, but for the maintenance over time.
It also includes any Knowledge Bases that will be synced to your AI solution. The principle of ‘garbage in, garbage out’ can be prevented by high-quality data inputs to your AI agents.
Data gap analysis questions:
- Is there sufficient data to train and deploy AI agents?
- Is the data available and accessible?
- Are data management practices up- to-date?
- Are data governance policies up-to-date?
- Is there a plan to keep Knowledge Bases that will be used by AI agents up-to-date?
4) Governance
Effective governance is essential to manage the ethical, legal, and operational aspects of AI deployment. Robust governance helps mitigate risks, fosters trust in AI systems, and promotes sustainable AI adoption.
This step involves establishing policies and frameworks to ensure responsible AI use, data privacy, and compliance with relevant regulations.
Governance structures should include clear guidelines on data usage, model transparency, and accountability.
Additionally, your team should set up mechanisms for monitoring and evaluating AI performance to ensure it aligns with organizational goals and ethical standards.
Governance gap analysis questions:
- Who owns which aspects of the project?
- Are there policies and frameworks for AI use and data privacy?
- Is there a strong leadership commitment to driving AI initiatives forward?
5) Talent
Does your organization have the necessary skills and expertise to complete and maintain an AI initiative?
This may involve identifying skill gaps, as well as training or hiring if needed.
Otherwise, consider hiring a partner to build for you. I’ll talk a bit more about this option down below.
Talent gap analysis questions:
- What skills – in both development and business deployment – are needed for this AI initiative?
- Are those skills present in current employees? Can current employees be trained through external resources about AI development and deployment?
- If not, would hiring in-house or teaming up with a partner organization best fit the company’s vision and needs?
6) Culture
While an AI solution is often technology-focused, the human component is just as important.
Not all organizations or employees are open to AI adoption, which will harm the ROI of your solution.
Evaluate your organizational culture to ensure that there is a willingness to adopt and adapt to AI technologies. This includes assessing leadership support, employee openness to change, and alignment with AI-driven innovation.
Many employees or departments often feel threatened by AI. Given the cost of hiring and current labor shortages, organizations can easily make it clear that AI will be used to enhance output, rather than replace employees.
Culture gap analysis questions
- Is the organizational culture willing to lean into AI adoption?
- Are all key leadership roles willing to embrace AI adoption?
- If there is hesitation, why? Are these worries well-founded?
- How can the organization make AI a positive for their employees and properly convey this to them?
3. Build a chatbot team
Who’s gonna work on your chatbot project???
It might be obvious, but this is an ongoing issue for a ton of our clients.
The best method is to assign responsibility (as with any other project). And since a chatbot project is complex and long-standing, you’ll likely need to break it down into multiple roles.
If you’re building an AI agent for your small business with 1 employee – it’s chill, do everything you can.
If you have resources, here are some tips on breaking it down.
Key Roles
There are 3 key roles in a chatbot project: an Executive Stakeholder, a Project Manager, and a Developer.
Depending on the scope of your project, you might have 1 person pulling off all 3 roles (best of luck), or you might have a whole team of developers working on your solution.
The Executive Stakeholder sets the strategic foundation and ensures the project has the necessary support to succeed. They might secure funding, establish performance metrics, and champion organizational buy-in.
The Project Manager drives the project forward day-to-day. They manage the project lifecycle, set timelines, identify risks, manage the scope, and coordinate cross-functional communications.
And the Developer, last but not least, is responsible for building the solution. They take care of everything technical: implementing the business logic, integrating with existing systems, and optimizing performance.
Even if you’re a 2-person team, clearly lay out which responsibilities will fall to whom.
And if your project is more complex, there are a few other roles to consider assigning.
Additional Roles
What about regulatory frameworks? What about serving your patients with a proper bedside manner? What about getting your users to actually use the bot?
Yeah, there’s a lot more to an AI deployment than you might think at first.
Again, this is more relevant the bigger your project is (or if you’re creating something serious like a finance chatbot or a healthcare bot).
They can be taken on by a single individual, assigned to one of the Key Roles, or taken on by multiple individuals.
- Quality Assurance: Provide organizational experience to ensure chatbot is up to industry standards
- Conversation Designer: Craft clear, engaging dialogue
- Data Analyst: Translate the requirements and results of the chatbot into ROI measurement
- Cybersecurity Specialist: Ensure proper data protection practices
- Compliance Officer: Adhere to applicable laws and regulations
- Marketing Specialist: Communicate presence and purpose of chatbot with users
- Website and System Administrators: Maintain servers and containers
4. Pick a chatbot solution
You might already have your tech solution picked out.
But if your team is still in an exploratory phase, there are 3 types of chatbot tools for you to consider.
The scope and ability of your AI project will vary vastly depending on which of the three you choose.
DIY or open source
A DIY option will involve researching, designing, prototyping, building, testing, configuring, deploying, hosting, maintaining, supporting and evolving a solution.
This can be done from scratch, but most developers will use a variety of open-source materials to build an agent from the ground up.
This option offers maximum control and customization, allowing for tailored solutions that precisely fit the business's specific needs.
However, this approach demands substantial development resources, technical expertise, and maintenance efforts.
Extensible platform
Platforms sit at the intersection between a closed solution and a DIY solution.
These chatbot platforms typically offer CSM guidance and expertise, hosting, infosec, development support, and pre-built integrations to streamline the design and configuration of solutions.
Extensible platforms provide a middle ground with router-like functionality, highly configurable layers, and integration capabilities. They facilitate quicker deployment and flexibility, though they still require some technical skill for configuration and customization.
These platforms can offer a balance between customization and ease of use. They can be extended more seamlessly across departments or business processes than the other options.
Closed proprietary solution
Many closed solutions are vertical-specific (i.e. a customer service chatbot company, or a social media chatbot platform), or offer a cut-and-paste solution (i.e. generic chatbot).
Provided they meet key requirements, seamlessly connect to existing systems, and the vendor roadmap aligns with the organization's ambitions, these can be extremely cost-effective to deploy and maintain.
However, while closed proprietary solutions are faster to implement, they come with the tradeoff of limited extensibility, limited use cases, potential vendor lock-in, less flexibility to adapt to unique business requirements, and a limited ability to integrate with other systems.
5. Pick chatbot partners (optional)
Not every company is set up to build chatbots in-house. Maybe you’re a team of 5 with no bandwidth, or maybe you want a complex AI agent that spans beyond your team’s ability.
Whatever the reason, there are a few benefits of using an external partner:
- You don’t need to buy the software yourself
- Timelines are accelerated
- They already have experience and expertise
- They can be cost efficient if you don’t have the in-house expertise
We have a pretty robust roster of AI partners and freelancers – but whatever solution you use, ensure that you find a partner organization that’s well-versed in that specific solution (and ideally, in your specific use case or industry).
The Key to Strong Partnerships
It’s strong SLAs. That’s it.
SLAs (Service Level Agreements) should define clear deliverables, including milestones, timelines, and success metrics.
You should also specify requirements for uptime, response times, and issue resolution.
And lastly, you should have an exit strategy. How will knowledge transfer, intellectual property, and system access be managed after the partnership ends? Who will be responsible for maintenance? All of this should be signed on in advance.
6. Map out implementation plan
When we’re deploying AI chatbots, we’re big fans of the Crawl-Walk-Run method.
We use it with our clients, we use it internally – it’s our North Star for implementation strategy.
Let’s break down each step.
Phase 1: Crawl
Objective: Establish a project foundation and address immediate business needs.
Begin with straightforward AI solutions to tackle basic, high-impact tasks. For instance, a chatbot can be deployed to handle frequently asked questions (FAQs) and provide basic customer support.
The point of this stage is to collect data. What are users asking? What actions would be helpful for it to accomplish?
The name of the game here is quick wins. Show value.
(And be sure to pilot your solution to a portion of users and collect data before rolling it out to all users.)
Phase 2: Walk
Objective: Incrementally enhance AI capabilities based on collected data.
Now use your data from Phase 1. Refine and expand your chatbot’s capabilities.
Build more sophisticated workflows, trim any conversational fat that’s making your users drop off. Keep iterating to improve accuracy and performance.
Phase 3: Run
Objective: Fully integrate AI into your company’s operations – and scale.
You’ll know when you’re in the last mile when AI has been deeply embedded into your company’s operation fabric.
Of course, a chatbot project is never done. Like any software, it’s a long-term investment that gets better and better the more you iterate.
When you’ve expanded to most of the place you see the opportunity for gains, ensure the team has a feedback loop for continuous learning. You’ll need to update and retrain your models as you get new data and business needs evolve.
7. Measure success
Measuring success is far too often overlooked – it’s the most important part. Investments need returns.
We’re going to talk about KPIs (setting up a chatbot for success) and ROI (measuring that success).
Key Performance Indicators
Your KPIs should be devised at the very beginning of your AI agent project. Each should be able to be mapped to a strategic goal that your AI agent is built to solve.
Your AI agent KPIs should :
- Be straightforward
- Include both short- and long-term results
- Use quantifiable outcomes, like exact percentages
- Include baseline comparisons to clearly show the ‘before’ and ‘after’ measurements
Each KPI should be tied to a specific monetary value. It’s not enough to say a project will ‘save 10 hours per week’. Calculate how much money your AI investment will save every month or year, taking into account how much you pay employees for those 10 hours.
Start low, then increase
While it’s tempting to maximize your bot’s impact from the get-go, our Customer Success team recommends aiming for a minimal value ROI at first.
Focus on incremental gains. Once the bot has proven effective with this initial load, gradually increase its scope – so you can minimize risk and maximize long-term success.
Example KPIs
What do chatbot KPIs look like?
If you’re aiming to measure adoption and engagement, the KPIs might include # of queries handled, feedback quality, or session lengths.
If you want to measure revenue and sales, your KPIs might be conversion rates, average upsell or cross-sell results, or lead qualification rates.
I won’t dive into detail here – just enough to give a sense of what your KPIs should look like. But hopefully, your team knows what a KPI is already.
Return on Investment
If you've never done it before, measuring chatbot ROI can be full of hidden rewards and costs.
We have a cmoplete list of what should be considering when trying to get an accurate ROI read on your AI project.
Measuring Investment
Properly measuring your AI investment enables companies to capture a complete view of its impact.
This means accounting for more than just the initial costs, such as ongoing maintenance, staff training, and the resources needed for successful integration.
I won’t dive into the full list here, but I wrote up a full PDF briefing on implementing chatbot strategy – you can see the full list of what to consider when measuring investment.
Measuring Return
Measuring business success with AI agents starts by aligning returns with their specific use case. The impact of an AI agent designed for lead generation will differ significantly from one built for internal HR processes.
To maximize value, guide your team in systematically evaluating every area the AI agent could influence and prioritize the ones with the greatest potential for measurable outcomes.
Again, I dive into this more in the guide linked above, but I’ll spare you here for the sake of word count.
Deploy the Right Chatbot for Your Company
We’ve deployed hundreds of thousands of chatbots – and we have the most flexible AI agent platform on the market.
Botpress offers a suite of pre-built integrations, a host of educational resources, and a partnership network of expert builders.
Start building today. It’s free.
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