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The Emergence and Evolution of Artificial Intelligence Services

Artificial intelligence (AI) has rapidly evolved from an aspirational concept to an integral part of business, driving transformation across industries. As AI platforms and capabilities advance, a thriving services market has emerged to help companies strategically leverage AI to unleash innovation. Just as business processes have been outsourced to services partners historically, many companies are now partnering with specialists to accelerate their AI adoption journey.

The AI development services comprise a range of offerings: from cloud services delivering AI components like natural language processing on-demand to boutique machine learning consultancies guiding custom AI solution development. As interest in AI has grown exponentially, integrators, digital agencies and major IT consultancies have built new capabilities to define scope, build, deploy and manage AI initiatives for clients.

Evolution of Artificial Intelligence Services

Core AI Services Offerings: An Overview

The landscape of AI services spans varying depth and complexity. Some common offerings include:

AI Strategy Consulting – Experts assess use cases, data/infrastructure and objectives to develop an AI roadmap aligned to business goals. Includes opportunity identification, solution scoping, program governance and change management guidance.

Machine Learning Development – Custom ML model development activities, including data evaluation, feature engineering, model selection/tuning and performance measurement to achieve key metrics.

MLOps Engineering – To operationalize models in sustainable, scalable production workflows by building model management, monitoring, retraining and integration capabilities on cloud or on-prem infrastructure.

Data Engineering – Since quality training data is vital for ML success, services to build reliable data pipelines, enhance data quality and structure unstructured data through techniques like EL/NLP.

Augmented Analytics – Combining AI/ML with business intelligence, data prep and visualization capabilities to surface insights faster by automating manual analysis.

Conversational AI – Building chatbots, voice assistants and other interfaces powered by NLP that allow human-like interactions to boost customer experience and agent productivity.

Computer Vision Services – Solutions applying CV techniques like image recognition, video analysis and anomaly detection for use cases ranging from manufacturing QA to security systems.

Benefits of Leveraging AI Development Services Partners

Many factors are driving increased demand for external AI services. Adopting AI often requires new specialized skillsets while digital talent supply is still limited. Internal data/analytics teams also have full roadmaps which can delay AI progress. Services firms offer leading expertise and flexibility to accelerate AI adoption with less disruption or diversion of internal resources.

Additional reasons organizations are leveraging AI partners include:

Accelerated Innovation – Quickly prove value by getting the fastest ROI on AI use cases with the highest impact by tapping external expertise. Over 30-50% faster than attempting purely in-house.

De-Risked Initatives – External specialists apply proven AI/ML frameworks to ensure model accuracy, reduce technical debt and set up governance to instill trust/transparency.

Future-Proofed AI Program– Through well-designed ML Ops, model management, continuous training/monitoring and infrastructure support services to ensure sustainability of solutions.

Optimized Cloud Utilization– Partners optimize usage of cloud services like EC2, Rekognition, Translate, Comprehend etc. to reduce costs. They also determine which applications warrant custom cloud-native development vs relying purely on cloud vendor AI offerings.

The Evolution of AI Services and What’s Next

While still early days, AI services have already progressed across several phases:

Phase 1 – Point AI Consulting – Initial focus was on strategy projects identifying and prioritizing AI use cases through workshops and opportunity assessments.

Phase 2 – Custom AI Build – As cloud ML democratized capability building, focus shifted to specialized development of custom ML solutions tailored to clients’ data and objectives.

Phase 3 – MLOps Services – Need emerged to make AI solutions sustainable in production via MLOps services for integration, monitoring and governance.

Phase 4 – Scaling AI Adoption – Present stage has seen maturity of end-to-end services to industrialize AI adoption across the enterprise through interconnected strategy, data, ML engineering and MLOps programs.

Emerging Phase 5 – Multi-Horizon AI Innovation – Next evolution centers on engaging partners continuously to work on concurrent batches of AI innovations on different time horizons aligned to business objectives.

As AI becomes a core pillar of competitive advantage across functions, services partners act as ongoing capability amplifiers through embedded, multifaceted programs spanning ad hoc innovations, long horizon moonshots and everything in between. The future role of services shifts from project-based consulting to serving as end-to-end AI transformation catalysts.

Evaluating the Right AI Services Partner

Not all service providers are equal when it comes to driving impact through AI. As with any strategic capability, organizations should carefully assess partners across several dimensions:

1.    Specialized AI Expertise – Look for specialized skills in ML and AI techniques vs general software engineering prowess. Experts focused exclusively on AI will likely produce higher quality solutions. Big consulting firms may sell AI services but leverage general IT resources lacking advanced AI skills to deliver projects.

2.    Technical Foundations – Seek partners grounded in software engineering excellence with the infrastructure and automation skills needed to properly productionize solutions with MLOps. Analytics experience is also vital to ensure model business alignment.

3.    Industry Understanding – Domain expertise in your specific industry goes a long way, as does leveraging industry-specific data in training models. This produces solutions tailored to your environment vs generic capabilities.

4.    Methodical Approaches – Ask partners to walk through their frameworks for solution development, deployment and ongoing operations. Assess how robust, repeatable and scalable the methods are.

5.    Cloud Platform Experience – Given most solutions utilize cloud infrastructure, evaluate expertise across platforms like AWS, GCP and Azure to determine best fit for your technology environment.

While assessing partners look beyond tactical project delivery and position services as multiplying forces to realize AI’s full, ongoing disruptive potential.

The era of leveraging external specialists to unlock value through AI adoption has clearly arrived. With the right partner, AI initiatives can avoid the pitfalls of past technology wave hype cycles. Services partners help companies build AI competency while continuously delivering business impact today. They also provide invaluable perspective on the art of the possible tomorrow – enabling organizations to go both broader and deeper with AI.

After working as digital marketing consultant for 4 years Deepak decided to leave and start his own Business. To know more about Deepak, find him on Facebook, LinkedIn now.


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