AI Software and di Trillion-Dollar Productivity Question

If AI sabi write software well well pass before, wetin business leaders suppose change today? My answer na: measure how e dey improve one complete business process, including di work wey dem need to check and maintain wetin e produce.

AI fit add $20 trillion to $30 trillion every year to world economy output? Elon Musk suggest dis rough estimate, plus di prediction say AI fit handle almost any digital task and reach Stockfish-level coding ability. Dis statements catch attention because dem dey show big change for di economics of knowledge work.

Dis na ambitious predictions, no be results wey don establish. Business leaders suppose separate di possibility of big productivity gains from di evidence say AI fit deliver every digital task reliably. Investment decisions need evidence from di work wey organisations dey actually do.

Wetin di Stockfish comparison dey tell us

Stockfish na open-source chess engine and one of di strongest for di world. Dis comparison dey show one real possibility: machines fit pass humans for demanding intellectual tasks. But dis analogy alone no fit establish when AI go pass every software engineer.

Chess get fixed rules, board wey you fit see, and clear goal. Enterprise software get incomplete requirements, policies wey dey change, legacy systems, and stakeholder priorities wey dey clash. One program fit pass tests while e dey solve di wrong business problem.

To generate code na part of engineering. To decide wetin to build, control access, check security, deploy safely, and support users still dey part of di outcome. Even AI wey sabi well well need process wey fit find errors and assign responsibility.

Understanding di economic claim

Make annual economic output level rise by 20 to 30 percent different from make e dey grow 20 to 30 percent every year. Claim of dis size need clear assumptions about adoption, reliability, investment, and di time wey e take to realize gains. To combine those percentages with $20 trillion to $30 trillion mean say na roughly $100 trillion starting economy.

For comparison, Goldman Sachs publish one scenario for 2023 where generative AI fit raise global GDP by about 7 percent, or nearly $7 trillion, and boost productivity growth by 1.5 percentage points over ten years. Dat na conditional forecast, no be measured result or ceiling on future gains. E no fit independently verify Musk bigger estimate.

For company level, one useful calculation dey simpler: net value = extra useful output and realized savings wey dem worth, minus AI costs, integration, review, rework, and losses from errors. Saved time only become financial value when di organisation fit use am productively.

Di advantages and di costs

Speed versus verification. AI fit shorten drafting, coding, and analysis. Leaders suppose measure di time wey dem need to check results alongside di time wey dem save to produce dem.

Lower production costs versus new operating costs. To create di first version fit become cheaper. Model use, integration, monitoring, and correction still dey chop money and staff time.

Wider access versus dependence. Smaller teams fit take on work wey pass dia own resources before. To depend on one single vendor fit make price, availability, and model changes hard to manage.

Bigger capability versus workforce disruption. Staff fit spend more time on customer needs and hard decisions. Companies also need to plan for changing roles, retraining, and di development of junior staff wey dia routine learning tasks fit become automated.

One practical enterprise example

Consider one hypothetical warehousing business wey dey build order-tracking service. AI fit draft one interface, propose database queries, and generate test cases. Di team still need to confirm which customers fit view each order, how dem dey handle inventory discrepancies, and whether di service dey work with existing systems.

Di meaningful comparison na di cost per accepted release and performance for use. Measure delivery time, defects, support requests, and customer outcomes. Faster first draft wey dey produce more production incidents fit reduce overall value.

Di same principle apply to customer service. To automate simple requests fit improve response times. Unclear cases need reliable path to person, with di relevant context preserved.

Wetin CEOs and CTOs suppose do

CEOs suppose choose one process with identifiable cost and clear customer outcome. Establish baseline, run limited pilot, and agree in advance which improvement go justify expansion. Include implementation costs and changes to staff responsibilities.

CTOs suppose evaluate proposed models on representative work from dia own systems. Check correctness, security, privacy, recovery from failure, and compatibility with existing services. Separate di permission to propose changes from di permission to execute dem.

For actions wey get significant effect, define approval owner and keep audit trail. Keep di ability to reverse changes, restrict access, and pause automation when performance spoil.

Review vendor dependence and train staff to question outputs. Expand deployment when measured benefit remain positive after review and support costs. Global economic forecasts suppose inform scenario planning, while local evidence suppose guide investment.

Di leadership decision

AI fit change di economics of digital work well well. Di scale and timing still dey uncertain. My advice: build di ability to evaluate and adopt am responsibly now, then increase investment as evidence dey strong.

Which process for your organisation you go test first, and wetin be di evidence wey go convince you say AI don create lasting business value?

Think mathematically. Engineer intelligently. Lead strategically. Build responsibly. Scale globally. Create real business value through AI.

#ArtificialIntelligence #AIStrategy #EnterpriseAI #SoftwareEngineering #Productivity #BusinessLeadership

Na Keshavan Thiagaraja first publish am for LinkedIn. Read di original for LinkedIn. [1]