The Smart Choice for Conversational AI: Build vs Buy

Conversational artificial intelligence (AI) has emerged as a transformative technology for enterprises across industries. As leaders aim to keep pace with rising customer expectations and ever-increasing volumes of consumer interactions, conversational AI offers a compelling path forward.

However, a key question arises - should businesses build a custom conversational solution in-house or buy an off-the-shelf platform from vendors? This article examines the critical considerations that should guide organizations in making smart choices based on their specific requirements and goals.

The Vital Role of Conversational AI

Conversational AI leverages natural language processing (NLP), machine learning, and dialog management to enable intuitive voice- and text-based interactions between humans and automated systems. According to Grand View Research, the global conversational AI market size is projected to reach $41.39 billion by 2030, expanding at an explosive 24.6% CAGR. What’s driving this remarkable level of investment and adoption?

Business Impact and its Key Stats:

1. Enhanced Customer Experience - 80% of customers prefer chatbots for quick query resolution, with wait times reduced by 30% via automation.

2. Higher Engagement - Chatbots can achieve up to 85% CSAT scores through personalized and instant responses.

3. Increased Productivity - Chatbots resolve up to 80% of routine customer queries, freeing human agents for complex issues.

4. Cost Savings - An average saving of $0.70 per interaction is achieved using AI assistants.

Conversational AI empowers enterprises to boost customer experience, employee productivity, cost efficiency, and revenue growth targets. As leaders recognize these manifold benefits, deploying smart virtual agents becomes a strategic priority rather than an optional efficiency play.

However, while the rationale for adopting conversational AI is compelling, should you build or buy the enabling technology? We’ll analyze the key factors to consider.

‍

Building Conversational AI In-House Offers Control and Customization

For enterprises with extensive in-house technical talent, building a proprietary conversational AI platform allows maximum control and customization aligned to unique business needs.

Key Advantages of Building Conversational AI

Building Conversational AI can be a game-changer for businesses in different areas to overcome existing limitations. Here is a list of several areas and how building Conversational AI can help there:

Areas & Benefit:

1. Product Fit - Optimally customized to your industry, users, products, processes and data formats.

2. Security - Retention of proprietary data and models within your firewalls enables tighter access controls.

3. Competitive Edge - Specialized proprietary IP can’t be easily replicated by rivals.

4. Control - Complete authority over technology choices, evolution path and monetization.

With cloud infrastructure and open-source frameworks lowering barriers, developing conversational AI is no longer an option exclusively reserved for tech giants. The open-source Rasa framework, for instance, allows fairly rapid prototyping of custom NLP models using Python.

However, a majority of effort goes into continuous training of models using representative dialogues and integration with surrounding enterprise systems. For fluid end-user experiences, conversational flows must interlink CRM, marketing automation, payments, fulfillment and support suites into a cohesive fabric.

Building also necessitates hiring specialized machine learning expertise for development and maintenance. Ultimately, while control and quality benefit from internal builds, the appetite for investment and execution must equal the ambition.

‍

Buying Conversational AI Solutions Saves Time While Lowering Risk

If in-house technical skills or executive buy-in fall short, partnering with commercial conversational AI platforms may be prudent. IDC predicts the AI software market to hit $632 billion worldwide by 2028, underscoring tremendous innovation by vendors targeting every niche.

Key Advantages of Buying Conversational AI

Buying Conversational AI can have an upper hand in some other areas and aspects of the business. Here is a list of unique advantages of buying Conversational AI:

Dimension & Benefit:

1. Speed - Pre-built solutions for common use cases allow faster time-to-market. This also allows first mover advantage to companies.

2. Ease - Low code configuration and templatized reports simplify rollout. Here companies do not need to scale up or build the low-level engineering capabilities.

3. Cost - In addition to emanating R&D costs, subscriptions are cheaper than retaining specialized in-house developers. Overall cost in the long-term is reduced.

4. Flexibility - Usage and resources can scale up or down to match evolving needs, offering flexibility.

Mature SaaS products like Ada, LivePerson, and Bold360 enable rapid deployment of chatbots and voice assistants using intuitive interfaces. They combine robust natural language models with built-in integration to complementary martech and collaboration tools.

However, relying on external applications limits control over custom intents and slot training. Core IP still rests with vendors who host user interaction data to continuously enhance their offerings. Data residency and opaque upgrade processes may also give pause to heavily regulated entities like financial institutions.

Therefore, assessing all dimensions of the build vs buy decision is vital before committing fully to either path.

‍

Comparing the Costs: Upfront Build vs Ongoing Subscriptions

Beyond qualitative technology considerations, cost is often the final arbiter in software adoption decisions. Conversational AI equally necessitates prudent ROI analysis, whether opting for internal development or third-party licensing.

Cost Metrics - Build vs Buy

Here is a breakdown of costs associated with building vs buying Conversational AI solutions along with key relevant metrics.

1. Metric - Upfront Spendings

   Build - $$$$

    Buy - $ to $$

2. Metric - Timeline to Launch

    Build - Within Months

     Buy - Within Weeks
‍

3. Metric - Talent Needs

    Build - Data Scientists, NLP Engineers

     Buy - Citizen Developers
‍

4. Metric - Ongoing Costs

    Build - Maintenance, Upgrades

     Buy - Monthly/Annual Contracts

Itemized Cost Examples

Here are some of the items that are involved with buying and building a Conversational AI solution, to compare apple to apple.

1. Cost Component - Conversational Platform

     Build - $200K+

     Buy - $25K to $100K per year

2. Cost Component - Team (5 FTEs)

     Build - $500K+

     Buy - $0

3. Cost Component - Compute Resources

     Build - $100K+

     Buy - Included
‍

4. Cost Component - Professional Services

     Build - $150K

     Buy - $25K+

5. Cost Component - Total (Year 1)

     Build - $950K+

     Buy - $75K to $150K

As the tables illustrate, buying commercially available conversational AI software translates to 10x lower first-year costs compared to building capabilities in-house. But custom-built solutions enjoy fixed pricing after initial rollout versus ongoing annual licensing expenses for purchased solutions.

Both options end up costing comparable sums over a 5-year horizon. However, buyers avoid expensive technical debt and extended payback periods. For most use cases, buying commercial software with advanced enterprise features allows faster value realization at lower risk.
‍

Timelines: Balancing Speed vs Perfection

Beyond monetary costs, the time required to deploy conversational interfaces also differs notably between build and buy approaches. Development lifecycles hinge on the scope of customization and integration complexity involved.

For niche industry applications requiring tight data security or unique dialog flows, custom builds could stretch to 9+ months. However, for common consumer scenarios like order status checks and account updates, pre-built market solutions take as little as 6-8 weeks to activate.

In fact, leading platforms like Intercom and Drift offer free trials to set up introductory chatbots without any coding. These justify ROI before larger production investments and can be extended incrementally to handle more complex tasks later.

The choice ultimately balances trade-offs between going live faster with good enough capabilities now versus waiting longer for customized excellency. As the saying goes: Perfection is the enemy of progress.

Prioritizing speed-to-market and iterations is mostly prudent for conversational AI projects with direct customer-facing impact. For instance, answering pre-sales queries 24/7 can directly drive more conversions versus back-office HR chatbots without immediate revenue upside.

However, use case complexity should temper urgency. Financial advice scenarios demand greater precision than food delivery promotions. Rather than arbitrary deadlines, focus on shipping working software that solves high-value problems from day one.

Scalability and Upgrades: Key Pillars for Sustained Success

The mark of well-architected software is its ability to sustainably scale impact over time through continuous improvements. Both build and buy approaches require investments here across three fronts:

Scaling User Traffic

Whether developed in-house or externally hosted, the underlying cloud infrastructure must auto-scale to maintain uptime and speed as usage expands from hundreds to potentially millions of conversations.

Supporting Advanced Use Cases

The platform should offer APIs and tools to quickly build smarter skills like data analytics, payments, and document processing over vanilla inquiries alone.

Adding Natural Language Capabilities

Foundational NLP must be augmented using contextual data and dialogue patterns to understand more complex utterances and sentiments.

• For builds, technical teams enhance proprietary models using internal datasets.
• Buyers rely on vendor roadmaps but can request customized extensions.

Ideally, the foundational architecture should abstract complexity into easy interfaces for citizen development without dependency on data scientists for every new feature.

Testing reliability and performance early in QA environments is critical before unveiling bots to consumers. Patience and budget reserves are equally essential for ongoing innovation post-launch.

Making the Smartest Choice: Key Factors to Consider

Determining whether building or buying conversational AI is the best path forward depends on a combination of enterprise-specific elements and use case nuances.

Key Factors Favoring Build

• Niche industry or heavily regulated data compliance needs
• Core to competitive advantage or IP protection necessities
• Large in-house ML teams with proprietary data access
• Budgets support high initial and ongoing development costs

Key Factors Favoring Buy

• Need for speedy marketing or sales pipeline impact
• Limited developer talent or executive sponsorship
• Standard consumer-facing application without special data needs
• Prefer predictable operating costs versus large upfront capital

Of course, the decision need not always be binary. Hybrid models are possible where businesses buy solutions for foundational capabilities like chat and voice interfaces. However, custom-build specialized predictive models utilizing internal data.

FedEx similarly invested in conversational AI specialist Sensei while retaining key delivery and logistics IP like route optimization and warehouse automation modules.

Evaluating the right juncture to leverage external innovation while still retaining differentiation is an exercise in strategic product management for enterprise leaders.

Conclusion: Focus on Delivering Differentiated Value

The promise for conversational AI to transform customer and employee experiences is clearly compelling, but so is the nuance involved in actualizing it. While buying solutions help launch apps faster, the need for customization around unique data and domain constraints necessitates a deeper evaluation of build vs buy trade-offs across multiple dimensions from feature needs to cost.

Rather than prematurely rushing to either option, consultative deliberation to align on specific functionality goals and constraints is the smartest first step. Leadership must encapsulate the quadruple aims of utility, viability, feasibility, and differentiability into their roadmap.

Well-packaged products can today deliver significant baseline utility around conversational apps for standard use cases. However, sustainable value creation and competitive advantage for enterprises lie in customizing experiences with proprietary data and specialized integrations.

Therefore, technology decisions must follow from crystal clarity on which capabilities truly move the needle given an organization’s unique customers, constraints, and strategic context. Only when resisting the temptation for short-term savings or precipitous action does one uncover the prudence required for long-term gains.

Embrace the full potential of conversational AI with Alltius. Our custom solutions provide proprietary data and unique features for easy implementation, enabling you to offer distinct customer and employee experiences. Contact us today to make smart and wise decisions that will result in profitability and benefits to the Enterprise in the future.

Get in touch with Alltius today!