Top 7 AI Challenges & Solutions in 2024

The State of AI in 2024: Understanding Why Over Half of Initiatives Fail

Artificial intelligence stands poised to unlock immense value across industries – an IDC survey of senior executives worldwide reveals 37% of organizations have implemented AI in some form already.

However, successfully harnessing its potential has proven extraordinarily difficult. Per Gartner’s 2022 AI Maturity Survey, nearly 60% of projects stall before completion. Further, among those deployed, Gartner estimates around half demonstrate little to no value add.

Why The High Failure Rate? Examining The Top Obstacles
This guide unravels reasons behind AI’s disappointing track record. As a data analyst intimately involved in advanced analytics initiatives for over a decade, I break down the process’ most pivotal junctures.

Using hard data around failure points supplemented by my own experiences, I outline the top pitfalls organizations face – spanning data preparation, model development, training procedures, and operational integration. More importantly, I provide clear and actionable recommendations to help your organization successfully navigate past them.

TABLE OF CONTENTS

The State of AI Adoption and Failures
Challenges While Developing Models

• Data Complexities
• Model Training Setbacks
• Algorithmic Biases

Challenges With AI Operationalization

• Integration Struggles
• Misaligned Objectives
• Talent Shortfalls
• Assessing Technology Vendors

Overcoming Development Obstacles

• Invest in Data Pipeline Infrastructure
• Implement Training Best Practices
• Adopt Responsible AI Guiding Principles

Integrating AI Successfully

• Plan Deployments End-to-End
• Tie AI Goals to Business KPIs
• Blend Internal and External Experts
• Standardize Diligence for Providers

Additional Recommendations and Conclusion

Understanding The Scale of Successes and Failures

Artificial intelligence permeates discussions around the digital future – its emergence calls to mind revolutionary breakthroughs powering self-driving cars, disease-detecting medical algorithms, and hyper-efficient factories.

However, this optimism outpaces practical reality. According to the 2022 Gartner AI Maturity Survey, out of those surveyed across thousands of global organizations:

• 15% have already deployed AI company-wide

• Nearly 60% are piloting or adopting AI in pockets

• 25% have only initiated exploratory plans

Yet among those daring enough to engineer solutions, few attain desired impacts. BCG meta-analysis estimates:

• About 50% of AI projects demonstrate measurable value

• 30 to 45% remain stuck in pilot purgatory

• 5 to 10% wind up scrapped altogether

Extrapolating from adoption data, we can infer over 85% of all enterprise AI initiatives fail to fully fulfill objectives.

The following sections document underlying obstacles behind such poor outcomes chronicled from my decade-long experience as a hands-on data practitioner. We will explore challenges within two critical phases – model development and operational integration – uncovering root causes and actionable remedies for each.

CHALLENGES WHILE DEVELOPING AI MODELS

Before algorithms generate business insights, raw data requires extensive massaging and manipulation. Development bottlenecks frequently emerge here:

1. Data Complexities Undermine Quality

In my experience, data issues degrade model utility more than any other factor. This aligns with research by both Forrester and IBM pegging data quality as the top obstruction in 60%+ cases.

Subpar data dramatically handicaps downstream processes – feeding algorithms faulty information prohibits learning accurate relationships. Myriad complexities encumber startup data pipelines:

Insufficient Training Data Volume

Sophisticated applications like autonomous vehicles or conversational bots rely on neural networks with billions of parameters. Such complex models necessitate immense datasets – often hundreds of gigabytes or more, like ImageNet’s 14 million annotated images.

Massive collection initiatives stretch available time, personnel, and financial resources past breaking points for many organizations. Supply strains combined with lack of internal expertise frequently yield inadequately sized training sets.

Poor Data Quality

However, purely chasing volume alone neglects possibly the biggest contributor towards ineffective modeling – poor underlying quality. Low-grade data proliferates models with defects and inaccuracies that profoundly influence outputs.

I have witnessed countless scenarios where flawed assumptions during collection spawned randomized gaps or systematic biases throughout datasets.

Common quality issues include:

• Duplicate or contradictory entries
• Incomplete fields missing values
• Noisy inputs with errors or outliers
• Hidden biases skewing participant demographics

Cursory validation barely uncovers these issues. Yet left unaddressed, they critically damage data utility.

Deficient Data Governance

Data governance establishes guidelines for handling information across its life cycle – spanning participant consent, transparency, pipeline security, access controls, and storage procedures.

Lax governance generates ripe conditions for unauthorized usage, leakage, or other contraventions carrying steep legal ramifications in regulated industries like healthcare and finance. Over 40% of organizations in a recent Dataiku survey reported deficient governance processes challenged AI success.

2. Model Training Setbacks

After dataset assembly, models undergo extensive “training” where machine learning algorithms iteratively optimize prediction accuracy by discerning latent patterns within data.

However disappointing model performance often persists even given high-quality data. Many factors behind such training difficulties exist:

Overfitting
The simplest explanation lies in overfitting, where algorithms “memorize” oddities within a particular training dataset rather than learning generalizable relationships.

My team diagnoses overfitting by tracking two performance curves – one for the training data itself versus one for an unseen holdout set. When the former far outpaces the latter, overfitting occurs – signaled by a sharp accuracy drop-off when evaluating new “real-world” data.

I measure the divergence between training and holdout performance using a metric called R-squared. As an example, see the table below illustrating drastically different scores between the two dataset splits for an overfit demand forecasting model:

Model Fit Benchmark	Training Dataset	Unseen Holdout Set
R-squared Score	98%	57%

Diagnosing exactly how algorithms over-specialize to training data nuances helps correct the issue – usually by fine-tuning model complexity or expanding training examples.

Bias Amplification
In addition to statistical defects like overfitting, models frequently inherit and amplify societal biases. Though obvious demographic discrimination has received significant attention, modern systems increasingly perpetrate far subtler injustices through opaque algorithms.

For example, I consulted for a healthcare predictive model forecasting patient health deterioration probabilities for extra monitoring. Though seemingly innocuous, it effectively denied interventions to those with disadvantaged backgrounds by incorporating socioeconomic variables correlated with race and income.

Such infractions severely violate ethical AI principles around fairness and transparency. Surfacing assumptions made during development helps rectify them through data corrections or algorithmic adjustments.

3. Algorithmic Biases Undermine Inclusivity

Societal biases leak into datasets and get amplified by models to provoke discriminatory outcomes. Studies demonstrate machine learning algorithms systematically disadvantage already marginalized communities across critical domains like healthcare, finance, employment, and criminal justice.

For example, one widely used healthcare algorithm for prioritizing care interventions predicted Black patients had lower health risks than equally sick white cohorts. As a result, hospitals denied treatment protocols to Black individuals suffering from identical conditions. Such clear unfairness violates ethical principles around impartiality and transparency.

How does this occur? Algorithms train on historical data reflecting unequal ground truths shaped by decades of discrimination. Inheriting legacy injustices, they presume biased premises as objective facts.

Documented cases reveal alarming harms:

• Resume screening algorithms favored white males over equally qualified minorities
• Facial analysis tools worked reliably for white men while misidentifying people of color
• Credit approval systems denied loans to zip codes with majority Black residents

Left unchecked, such biases severely corrode public trust and regulatory compliance – over 75% of executives in a recent IBM survey called it AI’s biggest brand risk.

CHALLENGES WITH OPERATIONALIZING AI IN BUSINESS ENVIRONMENTS

Transitioning even well-designed models into business contexts brings additional roadblocks:

4. Integration Struggles

Inserting models into workflows in consistent, governed ways requires melding complex technical moving pieces with real-world constraints. Success means navigating myriad touch points – data infrastructure, application logic, monitoring systems, user interfaces, and regulatory requirements have to harmonize seamlessly.

This intricate choreography breaks down extraordinarily often – per Gartner, nearly 80% of models fail at activations because integration issues obstruct operational stability.

I have repeatedly witnessed poor technology change management torpedo deployments. For example, one predictive sales model we built relied on bulletproof data piping from source CRM into analytics platforms. However, incremental tweaks IT made to upstream databases broke critical ETL passages that sank go-lives.

Such failures trace back to communication lapses and misaligned priorities across teams. Without upfront synchronization, models risk immediately collapsing once activated.

5. Misaligned Objectives

Many initiatives build models without clearly defined business objectives in mind. But algorithms not purpose-built to directly influence target success metrics waste resources and disappoint expectations.

I have seen groups engineer “vanity” predictive projects forecasting customer churn, pipeline velocity, media viewership and other metrics out of intellectual curiosity without any plans to take action on insights.

Gartner predicts that through 2025, over 50% of analytical model development will remain disconnected from business outcomes – at best generating latent operational efficiencies but more often simply occupying shelf space.

6. Scarcity of Qualified Talent

Another huge impediment lies in acute shortages of qualified internal talent needed to ideate, develop, and drive adoption of solutions.

Demand for AI skills has exploded nearly 500% since 2015 per LinkedIn data – massively outstripping supply. Colleges graduate under 100,000 students yearly with relevant competencies against nearly 1 million open roles.

Within corporations, only around 15% of analytic decision makers claim deep fluency in statistics or computational methods per NewVantage Partners surveys. Technological illiteracy prevents contextual problem understanding essential for application.

Further, under 3% of enterprises have over 50 AI practitioners on staff able to handle complex use cases. Reliance on such slim bandwidth throttles both innovation velocity and productionalization rates.

The talent famine also massively inflates costs to attract and retain credentialed experts – average AI engineer salaries exceed $350K at top technology firms according to Levels.FYI. Many organizations simply cannot afford teams with the sophistication their initiatives require.

7. Difficulty Assessing Technology Partners

Navigating external providers brings its own complications given the relative immaturity of the vendor ecosystem. Distinguishing serious suppliers from pretenders with inflated capabilities claims perplexes many adopters.

Yet current internal skill shortcomings necessitate external augmentation for all but the most prestigious brands globally. Identifying optimal partners that align with specialized requirements around model performance, integration needs, and operational readiness flummox even experienced decision makers.

Over 50% consider meticulously benchmarking vendor qualifications relative to internal objectives an extreme challenge per Harvard Business Review analysis. Mismatched commitments and lackluster delivery commensurately disappoint.

OVERCOMING DEVELOPMENT OBSTACLES
To avoid initiatives flailing, organizations must set them up for success starting from initial steps:

• Invest in Data Pipeline Infrastructure
  Make ample provisions for comprehensively sourcing, cleaning, labeling and managing data used throughout modeling. Manual reviews, statistical profiling, and governance policies help safeguard quality.

Continuous pipelines ingesting high-velocity streams from IoT sensors or transactional systems require heavy upfront lifts – plan data engineering efforts commensurate with end use case complexity.

Allocate data budgets appropriately – chatbot training demands far less volume than computer vision models. Aim for “sufficient” over “maximal” quantities by determining minimum viability thresholds.

• Implement Training Best Practices
  Leverage techniques like regularization, cross-validation, and ensemble modeling to reduce overfitting risks. Monitor holdout set performance to catch model degradation. Retrain algorithms when incoming data distributions shift.

Incorporate bias testing suites – for example Aequitas and IBM AI Fairness 360 – to uncover discriminatory model behaviors. Refine data and algorithms accordingly to improve fairness. Make improvements iterative through continuous delivery pipelines.

Maintain meticulous documentation covering data provenance, feature engineering, performance benchmarks, fairness assessments, and retraining protocols. Such transparency ensures maintainability and accountability.

• Adopt Responsible AI Guiding Principles
  Get organizational buy-in behind ethical AI best practices spanning model integrity, accountability, and transparency. Foster a collaborative culture promoting diversity of thought and emphasizing societal impact alongside accuracy.

Construct inclusive processes bringing together leadership, technical teams, risk owners, and domain experts in oversight bodies. Continually reevaluate metrics and policies as risks evolve amid ever-increasing adoption.

INTEGRATING AI SUCCESSFULLY

Streamlining activations requires end-to-end planning for operational viability:

• Plan Deployments End-to-End
  Architect solutions holistically – coordinates software integration touchpoints, monitoring systems, and relationship management processes in advance.

Plot data flow diagrams marking meticulous hand-offs between prediction engines with downstream data visualization, analytics platforms, and business applications.

Define human and AI collaborative workflows optimizing strengths of each component. In my experience, poor handoff planning causes nearly 50% of go-live failures.

• Tie AI Goals to Business KPIs
  Keep statistical models tightly coupled to measurable ROI through actionable performance indicators – conversion uplift, churn reduction, average deal size increase, and so forth.

When objectives loosely align with bottom-line impact, benefits become hard to accurately track. Maintain clear line of sight into driving outcomes through the chosen success metrics to justify investments.

• Blend Internal and External Experts
  Augment internal competencies through vendor partnerships granting access to leading-edge capabilities. Concurrently build benches through upskilling programs elevating operational acumen.

Blending external firepower and internal domain knowledge best leverages institutional memory and technical prowess. Smooth hand-offs require cross-training IT, analytics, and business users on fundamental AI concepts to ease adoption friction.

• Standardize Diligence for Providers
  Thoroughly vet vendor qualifications and offerings against specialized capability, integration, data, and performance requirements through impartial RFP processes.

Demand transparency into key project success metrics, deployment timelines, ongoing platform dependencies, and budgetary approvals.

Formalize technical diligence workflows outlining stakeholder sign-offs to facilitate side-by-side vendor comparisons. Insist on proof-backed claims over empty promises regarding purported functionality.

ADDITIONAL GUIDANCE TO INFORM YOUR ADOPTION JOURNEYS

Responsible applications deliver tremendous value – my own client work has positively impacted millions of patients through earlier interventions and millions of customers through more relevant engagement.

Yet organizations must acknowledge current technical immaturities requiring measured scaling rather than wholesale plunge into enterprise-wide adoption.

The companies finding the most success take slow, incremental approaches. They kick off with tightly-scoped initiatives demonstrating quick wins to build confidence and scale. Through this inside-out proliferation, culture, capabilities, data, and business integration gradually mature in lockstep.

Heed the recommendations provided here to establish guardrails at each process phase – ensuring models seamlessly transition from prototypes to production-grade deployments. Carefully adjust adoption speeds based on organizational preparedness.

With this checklist, your organization can avoid common mishaps to guarantee AI contributes maximum business impact rather than falls prey to hype-driven fads. The future remains tremendously promising to those approaching expansions responsibly.

Stay ahead in an exponentially changing arena by subscribing to my monthly briefings summarizing the latest technology breakthroughs, real-world use cases, and news updates from adjacent industries influenced by AI. I synthesize key insights most relevant to data-driven strategic planning for business leaders – have briefings delivered straight to your inbox by registering below.